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The Learning Game
Vikaas S. Sohal
A Proverb
Fool me once, shame on you, fool me twice, shame on me. The old adage
points to what may be a human being’s most important asset: the ability
to learn. We as humans learn every day of our lives, and yet, when we try
to explain how we learn, we run into trouble. Pick up a baseball and a
bat, and practice your swing for long enough and one would hope that you
would eventually learn how to hit the ball. After watching a professor
do an example problem on the chalkboard (and after reading more examples
in the textbook and perhaps asking your TF to do yet more examples for
you), you ought to be able to reproduce the professor’s methods to solve
similar problems. We humans (and most of the animal kingdom) learn like
it was going out of style even though we have know idea how we do it.
Machines Do It Too
Machines are a little bit like human beings, in that they can sometimes perform some of the same functions as human beings. A human being who has completed a couple of years of elementary school can usually add and subtract. So can tiny microchips. The difference is that until the latter half of this century, human beings, unlike machines, could learn. Machines had to be built for a specific purpose, or occasionally, they could “learn” to perform new tasks, but only if a human programmer explicitly specified every step of those tasks.
But for the past thirty or so years, researchers have begun to find
ways for machines emulating the structure of the human brain to “learn.”
Although no machines are likely to begin enrolling alongside college students
anytime soon, they are beginning to provide models of human learning.
Neural Networks
While many researchers have been diligently working to discover the molecular, cellular and anatomical features of the nervous system, others have begun studying how these features contribute to abilities such as learning, by using neural networks. The nervous system is made up of cells called neurons, and every neuron can be characterized by a level of activity, measuring the rate at which it undergoes electrochemical fluctuations known as action potentials. The activity in certain neurons, or the pattern of activity among groups of neurons, is thought to underlie the functions of the nervous system ranging from knee-jerk reflexes to cognition.
How do these patterns of activity come about? Each neuron sends connections to many neurons and receives connections from many other neurons. As a result, each neuron's activity is determined by the activity of other neurons, and each neuron's activity affects many other neurons’ activity levels. In short, the neurons in our brain really are connected up into a kind of network.
Neural networks are scientists’ attempts to capture this structure. A computer can store the values of the activity levels for many neurons and can modify these activities to simulate the effects of the connections between neurons. A neural network can also simulate the effects of changes in the external environment (such as the presentation or removal of a particular stimulus) by changing the activities of those neurons which depend on the external environment. A neural network is nothing more than a computer simulation of how the activities of a group of neurons change during time as they communicate and influence each other and as the environment changes. It is no different from a computer game of Risk which simulates the changes in groups of armies as they do battle, receive reinforcements, etc.
After setting up armies in their initial position, a Risk game evolves such that the particular configuration of armies on the Risk board determines the situation (victory for one player, conflict between two colliding armies, a retreat, etc.). Similarly, after giving the neurons in a neural network an initial set of activities to represent the “input” provided by the environment, the neurons influence each other, so that the pattern of activities in the network changes. The resulting patterns of activities might be said to represent the “output” the network has reached from the inputs. In terms of human beings, your math teacher might write a math problem on the board, eliciting some “input” activity in your brain. Presumably, some computations occur as the neurons in your brain communicate, and the new set of activities gives you an “output” — the correct answer or at least a good guess.
Two Classes of Learning Rules
Clearly, the output state reached by the neural network for a given input depends on the kind of communication which takes place between the neurons. So the only way the network can “learn” to return a different output state after starting with a given input state is by changing the way in which its neurons communicate — by making a neurons’ activity influence some neurons less strongly, and others more strongly, for example. The method by which a neural network determines how to change the influences of particular neurons on other neurons is called the learning rule in neural network jargon, and broadly speaking, most learning rules fall into two categories: supervised learning and unsupervised learning.
One way to think about the difference between supervised and unsupervised learning is to think about road trips. Really. Suppose you have two groups of friends, both of whom want to go on road trips. The problem is that know no one knows exactly how to get to a fun place. This problem is not entirely unlike that faced by a neural network. A neural network needs to “reach” an appropriate “output” starting from an “input” but it does not know how its neurons should influence each other to make this happen. After all, many different outputs would be possible depending on exactly how the neurons influence each other. Just as your friends need to find a way to get to a fun place, the neural network needs to find the right set of interactions between neurons.
Looking For Fun in All the Wrong Places (And One Right Place)
The first group proposes the following plan for finding a fun destination. From the town in which they start they will ask the locals in what direction to drive to get closer to Disneyland. Then they will drive in that direction until they reach the next town. At that town they will once again ask the locals in what direction to drive to get closer to Disneyland. Then they will drive in that direction until reaching another town and repeat the process again and again until they reach either Disneyland (and presumably have fun) or end up somewhere from which they can no longer get any closer to Disneyland (e.g., prison).
This method of finding the most fun destination is a very crude approximation to the types of learning rules which fall into the class of supervised learning. A neural network begins with a particular set of weights — weight simply refers to the amount of influence one neuron’s activity has on the activity of another neuron, so the set of weights describes the way in which all the neurons’ activities influence each other. There is a known set of input activities, and furthermore, there is a desired set of output activities which is known. The desired output corresponds to Disneyland in the analogy. Supervised learning methods compute which change in the set of weights (the ways in which neurons’ activities influence each other) decreases the difference between the output produced by the current set of weights and the desired output. Then, they make this small change to the weights. Based on the updated weights, the next appropriate change to bring the output closer to the desired output is computed and the process is repeated until the output is sufficiently close to the desired output, the same way that our hypothetical friend drove from town to town until finding a sufficiently fun town.
The key aspect of the analogy is that during each step of each process,
the system (our friend or the neural network) made a change which the system
knew in advance would bring it a little bit closer to the desired goal:
the network computes the change which would bring it closer to the desired
output and the friend asked locals about what direction to take next.
But these sorts of methods are not the only ones available to students
eager for a fun road trip or neural networks seeking to generate an appropriate
output for some given input. Imagine a second group of friends, who like
the first, drives from town to town. These friends do not know what their
final destination will be. And after reaching each town, this group does
not ask the locals where to go next. Instead they fan out in every conceivable
direction. Some directions lead to dead ends. Some lead back to where they
started. Some lead to places which are less fun. But a particular direction
turns out to be the most fun, and eventually, after exhausting all other
possible options, the group travels in this direction, reaches the next
town, and repeats the process.
This group of friends operates in a similar manner to neural networks using unsupervised learning rules. Simply put, no one tells an unsupervised network where to go next. At each step, there are many possible changes in the set of weights which the neural network could make, and each represents a step towards a different set of output activities. The network does not literally try every possible change before selecting one, but the change is chosen without the knowledge of what direction leads towards some “known, desired goal.” The exact criteria by which the appropriate change is chosen is difficult to explain non-mathematically, but those changes which lead to “dead ends,” i.e. which are not self-consistent or not reinforced, are not made whereas those changes which do meet those criteria are made. If each change in weight was like a flavor of ice cream, supervised learning might be like asking the person behind the counter what flavor to have, while unsupervised learning would be more like trying one taste of each flavor and then making a selection. The key aspect of unsupervised learning which differentiates it from supervised learning is that the changes in the weights which are ultimately made are borne out by experience and not based on advance knowledge.
Thus, although the two classes of learning rules (or two methods of road-tripping) may seem the same superficially, they are really quite different. First, supervised learning clearly would be expected to be (and usually is) much more efficient than supervised learning. After all, how many people do you know who road trip by driving town to town and fanning out in all directions before settling on the next leg of their journey? For this reason, supervised learning usually provides the learning rules of choice for engineers and many computer scientists.
Back to Humans
But there is an issue of human learning which hasn’t really been considered
since the opening paragraphs of this article. Since both supervised and
unsupervised learning take place in neural networks meant to model a real
population of neurons, it seems natural to ask whether either supervised
or unsupervised learning provides a model for human learning.
Turning for a moment to the physiology of learning, most talk these
days are about the phenomena of long term potentiation (LTP) and long term
depression (LTD). LTP and LTD are mechanisms by which the influence the
activity of one neuron has on the activity of another neuron can change.
Recall that these very changes were at the heart of learning in neural networks. So examining how these changes occur among real neurons might shed some light on the relationship of learning rules for neural networks to actual learning.
What is most amazing about LTP and LTD is that the amount by which the changes the influence one neuron has on another seems to depend primarily on only two quantities: the activity levels of the two neurons involved. Recall that in supervised learning, the amounts of change made in the network’s weights (the weights are just numerical representations of the influence between neurons’ activities) depended on knowledge of the desired pattern of neurons’ activities and on a calculation of what changes in the weights would bring the neurons’ activities closer to the desired patterns. Clearly, this is much more knowledge than actual neurons seem to use when they change the amount of influence different neurons have on each other. Herein lies the main objection to supervised learning: that it is not biologically realistic.
This is not to say that supervised learning has no place in modeling the way human learning occurs. Although the methods supervised learning and biological systems use to change the ways in which neurons interact may be different, both supervised learning and real systems may still arrive at the same final interactions among neurons after learning to solve the same problem. And many researchers have suggested ways in which the information necessary for groups of actual neurons to change their interactions in a way consistent with supervised learning might occur. LTP and LTD need not be the only possible mechanisms for learning; it is just that other mechanisms have yet to be precisely described.
Nevertheless, in contrast to supervised learning, unsupervised learning seems to be possible using only the kinds of mechanisms for changing the interactions among neurons suggested by LTP and LTD. Unsupervised learning does not require excessive amounts of knowledge — actual experience takes the place of this extra knowledge. In the ice cream cone example, tasting every flavor before making your selection takes longer, but doesn’t require obtaining knowledge by asking someone else.
Lessons for People
So to sum up, on the one side there is supervised learning and efficiency, while on the other side there is unsupervised learning and (possibly) biological realism. In the future, new physiological mechanisms for learning and more possible learning rules for neural networks may be discovered, but for now, most researchers choose to pursue one or the other class of learning rules. Of course, none of this is likely to help any of you learn the material in any of your classes, but it might provide useful (if time consuming) ideas for what to do next spring break.
REFERENCES
Hasselmo M.E. and Stern C.E. (in press). Linking LTP to network function:
A simulation of episodic memory in the hippocampal formation. In Long-Term
Potentiation, Vol. 3 . M. Baudry and J. Davis, eds. (Cambridge, MA: MIT
Press).
Rumelhart D.E. and Zipser D. (1986). Feature discovery by competitive
learning. In Paralell Distributed Processing: Explorations in the microstructure
of cognition, Vol. 1: Foundations. (Cambridge: MIT Press).
Vikaas S. Sohal '97 (vsohal@fas.harvard.edu) is an Applied Math concentrator
in Leverett House.
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The Development of the Neuromuscular
Junction
Anna Greka
The neuromuscular junction (NMJ) is a particularly interesting subject of study due to the wealth of information known about its structure and function. The NMJ is the only example of a synapse between a nerve and a muscle and plays an important role in successful motor control.
In developing mammalian muscle, multiple motor neurons innervate the same NMJ on each muscle fiber. During early postnatal life, most of these connections are retracted until each muscle fiber is innervated by one motor neuron. This process is called synapse elimination. In terms of biochemical processes, it is widely known that the main neurotransmitter released in the NMJ is acetylcholine (ACh), which is picked up at the post-synaptic membrane by acetylcholine receptors (AChR). The localization of AChRs has been achieved by staining with alpha-bungarotoxin, which binds to the AChRs and blocks them (Zaimis, 1976; Brumback and Gerst, 1984; Dowling, 1996).
The fine structure of the NMJ may be studied extensively using electron microscopy, which has revealed some of its finest details. Scientists have used this technique to study the successive steps of nerve-muscle differentiation to form the NMJ during development. The development of the NMJ begins in utero with an axon and its Schwann cell lying close to embryonic cells of muscle mass, which are characterized by a large nucleus, sparse cytoplasm, granular endoplasmic reticulum, many mitochondria, and polyribosomes (Fig.1, A). Gradually, clusters of axons approach the developing myotubes, which are characterized by glycogen granules, lipid droplets, and peripherally located myofibrils (Fig.1, B). Clusters of axon terminals eventually sit on the surface of the myotube, covered and separated from one another by a Schwann cell. The postsynaptic sarcoplasm is devoid of myofibrils, but has many mitochondria. Both the Schwann cell and the myotube have a fuzzy coat of basal lamina (Fig.1, C). Gradually, axon terminals on the surface of the myofiber become separated by processes of the Schwann cell. The post-synaptic membrane begins to form the characteristic folds, and the basal lamina now runs throughout the synaptic cleft (Fig.1, D). Finally, the mature NMJ forms when the post-synaptic membrane forms the primary synaptic gutter that engulfs the axon terminal. The prominent basal lamina extends throughout the cleft, including the depths of the secondary post-junctional folds (Fig. 1, E. taken from Brumback and Gerst, 1984).
Having described the morphogenesis of the NMJ, it is also important to consider some of the most recent research findings. In addressing the question of how pre- and post-synaptic differentiation are achieved, scientists have suggested that specific signals during development allow for the appropriate differentiation of the axon terminal and the post-synaptic muscular membrane.
Little is known about pre-synaptic differentiation, but the recent finding that omega-conotoxin-binding Ca++ channels are concentrated at the active zones of the axon terminal provides a molecular marker for the spatial organization of the nerve terminal (Jennings and Burden, 1993).
In terms of the post-synaptic differentiation, that is the differentiation of the muscular surface, there seems to be more information available. Studies of AChRs have shown that their clustering at the post-synaptic region may arise in two ways: a) pre-existing AChRs are redistributed to the synaptic sites, or b) new AChRs are synthesized locally from mRNAs located in the synaptic region. This process is called synapse specific transcription (Jennings and Burden, 1993).
Preliminary findings seem to indicate that the synthesis of new AChRs is linked to a process of local transcription that takes place in the synaptic nuclei, within the post-synaptic region. This synapse specific transcription seems to be initiated by signals received from the basal lamina. The basal lamina (Fig.1) is a formation that appears during the development of the NMJ, and it is thought to release bioactive molecules, such as the recently isolated agrin, when it goes through a process of conformational change. Agrin has been found to induce AChR clusters on cultured muscular tissue. Whether it is responsible for the synthesis or the redistribution of AChRs, as well as which other compounds are responsible for these processes, remains to be determined by further research.
REFERENCES
Carry, M.R. and Morita, M. (1984). Structure and Morphogenesis of the
Neuromuscular Junction. In The Neuromuscular Junction. R.A. Brunback and
J. Gerst, eds. (New York: Futura Publishing Company, Inc.), pp. 25-64.
Drachman, DB. (1994). Myasthenia Gravis. The New England Journal of
Medicine 330:1797-1807.
Jennings, C.G.B. and Burden, S.J. (1993). Development of the neuromuscular
synapse. Current Opinion in Neurobiology 3: 75-81.
Munsat, T.L. (1993). Neuromuscular disease. Current Opinion in Neurology
6:679-680.
Sanders, D.B. (1984). Acquired Myasthenia Gravis. In The Neuromuscular
Junction. R.A. Brunback and J. Gerst, eds. (New York: Futura Publishing
Company, Inc.), pp. 25-64.
Anna Greka '98 (agreka@fas.harvard.edu) is a Neurobiology concentrator
living in Lowell House. She has spent the last two years working on RNA
structures in an X-ray crystallography laboratory. This summer she will
be working on the molecular cloning of GABAC receptors in the mouse retina.
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The
Integration of Literature and Neuroscience...And Its Inhibitors
Dan Engber
Ode to the Diencephalon
(after A. T. W. Simeons)
How can you be quite so uncouth? After sharing
the same skull for all these millennia, surely
you should have discovered the cortical I is
a compulsive liar.
He has never learned you, it seems, about
fig-leaves
or fire or ploughshares or vines or policemen,
that bolting or cringing can seldom earth a
citizen’s problems.
We are dared every day by guilty phobias,
nightmares of missing the bus or being laughed at,
but goose-flesh, the palpitations, the squitters
won’t flabbergast them.
When you could actually help us, you don’t. If only,
whenever the trumpet cries men to battle,
you would flash to their muscles the urgent order
ACUTE LUMBAGO!
June 1972
Referring to W. H. Auden’s “Ode to the Diencephalon” provides me with a cheap defense of my plan for a combined thesis in literature and neuroscience. Casual doubters are usually satisfied with a few lines quoted from the poem: “How can you be quite so uncouth? After sharing the same skull for all these millennia, surely you should have discovered the cortical I is a compulsive liar.” As if the mere mention of neurological jargon in the work of a famous poet justifies the merger of two vastly disparate fields; as if a lyrical quip could merit the emergence of an ambitious new discipline. On the contrary, Auden is addressing neuroscience only to exploit it as metaphor. A few stanzas later, he complains that “When you could really help us, you don’t. If only, whenever the trumpet cries men to battle, you would flash to their muscles the urgent order ACUTE LUMBAGO!” Auden cares little for IA afferents or alpha motor neurons—his issue is purely abstract. Should not our instincts, he asks rhetorically, prevent us from killing each other? The philosophical question employs the neurological term absolutely—it is a one-way incorporation of neuroscience into literature without any degree of reciprocal concern. I mention the poem to defend my plan of study even while recognizing that Auden’s work is non-integrative. He uses neuroscience only as a parasite.
It is far more difficult, I have found, to find an occasion when neuroscience parasitically enters the field of literature. This is, in part, due to the utterly co-optive position to which literature relegates itself, especially in relation to the sciences. The self-description supplied in the Fields of Concentration deliberately places the Literature department at the wobbly crux of interdisciplinary study: “The concentration is designed to provide students with the opportunity to ask a number of fundamental questions. What is literature? What forms does it take in different social and historical contexts? What functions does it serve? What are its relations to other arts and disciplines?” The immediate and striking self-doubt of the first question elasticizes the entire field, while the last question explicitly potentiates an integration with all other studies. The Literature department defines itself by its willingness to co-opt or be co-opted. It suggests itself vaguely, as something defined only by the various perspectives with which it is studied—a protean pursuit whose form manifests specifically in relation to its context. The description of the Biology department dramatically echoes this sentiment but with a bizarrely constrictive postscript: “Some of the most important advances in modern biology have been made by those whose training and creativity have enabled them to synthesize disparate fields toward a new understanding of life processes. Biology is also an interdisciplinary subject. Elements of mathematics, chemistry, and physics are essential for an understanding of life processes and as investigative tools. Also, biology is an important component of other disciplines, ranging from medicine, psychology, and anthropology to geology, oceanography, and applied engineering.” Here we see a remarkable affirmation of the malleability of the field and of the merger of “disparate fields,” and then two explanatory sentences which restrict those fields in both the co-optive and the co-opted sense. In terms of the former, the description cites only the core program’s hard “A”-sciences as potentially incorporated into Biology, and in terms of the latter, only the softer narrative “B”-sciences can potentially subsume Biology. In neither case is the possibility of combination with a truly disparate field (like Literature or Philosophy) even considered. This undercutting restriction constitutes the integrative bias by the neurosciences against the humanities.
The integrative bias manifests in any attempted combination of the two disciplines. Such a combination usually involves the objectification of one discipline by another, where the objectified field is incorporated into the subjectified field; the co-optive field objectifies the co-opted. We shall consider both sides of the bias: the relatively easy incorporation of neuroscience into literature, and its more difficult inverse. Looking at the former case, where neuroscience is co-opted by literature, we see two potential methods of fusion: one can either analyze a text which adopts neuroscience as a sort of trope, like Auden, or one can attempt to examine neurological research from the perspective of a literary movement; the integration can therefore be either thematic or theoretical. We have briefly seen in Auden an example of the former method; a critical look at the famous neurological case study of Patient HM exemplifies the latter.
In 1953, an epileptic patient (“HM”) received an experimental operation in which his hippocampus and amygdala were removed from his brain bilaterally. The brain damage did relieve him of the worst seizures, but it also created a host of other problems, including a total anterograde amnesia. Left without the ability to remember anything that happened to him from that day forth, HM immediately became the subject of endless psycho-neurological studies. Jenni Ogden has written a compelling account of her work with HM in which she describes his bizarre condition. “Hours, days, weeks, and years of watching the same stimulus on a computer monitor and pressing buttons in response may not be tedious for him because he has no conscious awareness of ever having done the experiment (or any other experiment) before.” For HM, each experience in his life after the day of his operation is entirely new—he never notices the continual research projects which have comprised the remainder of his life. His special condition was of such scientific significance that an entire literature of HM-studies was created; “he has had more words written about him than any other case in neurological or psychological history.”
For these same reasons, the story of HM can be researched from a literary perspective. In particular, he provides an interesting model for the consideration of nineteenth century Aestheticism, a movement somewhat obsessed with models. For the Aesthetes, originality, depth, and novelty were the ultimate virtues; routine and monotony the principal foes. Nietzsche likens habit to suffocation in his Aestheticist treatise, The Gay Science: “Enduring habits I hate. I feel as if a tyrant had come near me and as if the air I breathe had thickened when events take such a turn that it appears that they will inevitably give rise to enduring habits.” And habits are broken by forgetting—Kierkegaard declares that “a person’s resiliency can actually be measured by his power to forget. He who cannot forget will never amount to much.” From this perspective, HM is an Aestheticist hero. He forgets everything and has no enduring habits. Furthermore, his position as a scientific construction reinforces his interest to the movement. The Aesthetes were also fascinated by notions of construction and experimentation—Oscar Wilde remarks in his most famous Aestheticist work that “it was clear to him that the experimental method was the only method by which one could arrive at any scientific analysis of the passions, and certainly Dorian Gray was a subject made to his hand.” The case study of HM, with his curious amnesia and scientifically constructed persona, could therefore provide the basis for the study of an entire literary movement.
In both formulations of literature incorporating neuroscience, we see that the relationship is indeed parasitic—the implications of the work remain exclusively within the co-opting field. Even working against the grain of the integrative bias—incorporating literature into neuroscience—yields the same result: the significance of the effort is primarily in literature, the objectified field. We see this peculiar, almost paradoxical relationship in the field of neurological linguistics, the primary site for this upstream fusion. More specifically, the work of Roman Jakobson demonstrates the unusual effect. In a 1956 article entitled “Two Aspects of Language and Two types of Aphasic Disturbances,” the Russian psychologist and linguist convincingly categorizes aphasias as affecting one of two axes of language, the metaphoric and the metonymic. The study showed the localization of these processes in terms of brain function, as well as their fundamental importance in a new theory of language. He goes on to reapply the idea of metaphoric and metonymic axes to their original sources in the study of literature, associating metaphor generally with poetry and specifically with Romanticism and Symbolism, and metonymy generally with prose and specifically with Realism. Jakobson incorporated literature into what was basically a neuro-linguistic study; from it he created a new perspective on literature and formed the basis for the Russian Formalist and Modern Structuralist schools of literary criticism. Jakobson’s was a classical study of aphasia; like Broca and Wernicke, he created a theory of language by evaluating various impairments. But by bringing a consideration of literature to his work, he was able to bring original insights to both fields.
Still, the impact of Jakobson’s study was much more intensely
felt within literature than within neuroscience. Despite his work to integrate
literature into neuroscience, and not vice-versa, the major significance
of his effort conforms to the integrative bias and adheres to the literary.
Even as the objectified discipline within an interdisciplinary study, the
bias towards literature remains. A dissonance of meaning comes out of these
one-sided efforts. The resulting clang and clamor makes one wonder how
a harmonious integration is possible with these fields. Only in simultaneity
could such a congenial combination occur. That is to say, only in a situation
in which both fields co-opt each other at the same time, objectify each
other, and incorporate each other simultaneously, can produce the dulcet,
mutualistic fusion of literature and neuroscience.
Fourteen months before he published “Ode to the Diencephalon,” Auden
wrote and dedicated another poem to his old friend Oliver Sacks. In this
poem, in the work of Sacks, and in the work of Sacks’ model A. R. Luria,
we do see an example of this last form of integration, this harmony through
synchrony. The poem, entitled “Talking to Myself,” and Luria’s novel The
Mind of the Mnemonist reverberate the themesof consciousness and subjectivity
from a truly interdisciplinary position. They do so through the specific
examination of the surface of that interdiscipline — they till the fertile
plane of simultaneous literature and neuroscience, and they create from
it. This task requires a double-vision lacking in the one-sided efforts
described above.
Luria achieves the double-vision through what he terms in his autobiography the “Romantic Science.” Here he establishes simultaneity: “Romantics in science want neither to split living reality into its elementary components nor to represent the wealth of life’s concrete events in abstract models that lose the properties of the phenomena themselves.” The romantic scientist explores the interior of this dichotomy, avoiding the dogmatism of isolationist neuroscience and expansionist literature. The interior marks the pure interdiscipline in which, as a colleague of his wrote, “Luria the ‘romantic’ narrativist was not only on good terms with Luria the ‘classic’ scientist, but the two of them were working hand in glove...”
In The Mind of a Mnemonist, these two Luria’s work together to provide the narrative case-study of S., a man who cannot forget. Perhaps more interesting than his limitless memory was his rare “synesthetic” sensitivity to the world which mixed sight, smell, taste, and sound into single impressions in his mind. “‘Lines,’ ‘blurs,’ and ‘splashes’ would emerge not only when he heard tones, noises, or voices. Every speech sound immediately summoned up for S. a striking visual image, for it had its own distinct form, color, and taste.” Luria makes it clear that understanding S.’s synesthesia was scientifically important—“from an objective standpoint,” he clarifies—but he also very deliberately connects synesthesia to poetry: “Poets, as we know, are extremely sensitive to the expressive qualities of sounds, and ... this ability was developed to such a degree that [S.] never failed to detect the expressive qualities of sounds.” Luria poetically describes the fantastic images that S. imagined when he heard even dreary daily conversation, and labels him “a dreamer whose fantasies were embodied in images that were all too vivid.” In order to fully understand S. as a valuable case-study in hypertrophied memory, Luria romanticizes him and evaluates him from a literary perspective. We see this further as Luria dwells on the synesthetic experience of literature, “dealing with S.’s responses to imagery, narrative prose, and poetry ... What effect did his figurative, synesthetic thinking have on his grasp of this type of material?” Luria asks. To create a rich portrait of an individual within his Romantic Science, Luria investigates the strange ways in which literature affected S., he examines synesthesia as a potentially poetic mode of experience, and he concludes his study with the judgment that S. lived in “the world of imagination,” but not that of a “creative imagination.” In this fully integrative and simultaneous text, Luria provides a more profound case-study than would be possible through monotonic scientific discourse, and illuminates literary themes with the unusual light of psychology. The graceful accord between the two fields in his work overcomes integrative bias and yields a new and valuable fusion.
Auden is able to achieve the same effect through more abstract means. “Talking to Myself” does not merely play with neuroscientific jargon, as in “Ode to the Diencephalon.” Instead, the relationship between poetry and neuroscience is integral to the work, dramatized in the poem as the bifurcation of the author into “I” and “You.” This split also analogizes mind and brain as well as conscious and unconscious:
For dreams I, quite irrationally, reproach You.
All I know is that I don’t choose them: if I could,
they would conform to some prosodic discipline,
mean just what they say. Whatever point nocturnal
manias make, as a poet I disapprove.
In this stanza, Auden investigates all three relationships
simultaneously. First we see the problematization of poetry with regard
to neural functioning, phrased by a poetic voice which earlier admits the
indispensability of the “You,” “but for whose neural instructions I could
never acknowledge what is or imagine what is not.” In this sense, the poetic
“I” engages its own limitation and explores it in the context of a poem.
Secondly, the conscious mind identifies dreams as originating in the unconscious
workings of the brain while maintaining the desire for interpretation.
The mind is posited without abandoning a sense of the biochemical processes
which are somehow a part of it. Auden uses poetry to examine fundamental
neurological issues like the mind-body problem; he uses metaphor and polysemy
to provide insight unseen in raw technical discourse.
Auden continues the process by referring to poetry and neuroscience
as a couple whose “marriage is a drama, but no stage-play where what is
not spoken is not thought: in our theatre all that I cannot syllable You
will pronounce.” This last phrase beautifully accounts for the symbiosis
of the fields; they are complementary parts of total expression—when one
field falls silent, the other can finish the sentence. The effect of this
completion is an illuminating unity, a simultaneous integration which allows
the dialectics of poetry and neuroscience, mind and brain, conscious and
unconscious to be examined with more depth than could possibly be attained
with a fragmented perspective.
“Seldom have you been a brother,” Auden writes. The sentiment is accurate. While literature has been quick to adopt neuroscience as, perhaps, a servile child, a notion of true brotherhood has been rare. By allowing the fields to adopt each other, Auden and Luria create this brotherhood of disciplines. Luria sees the integration as a new form of psychology, one “that is capable of dealing with the really vital aspects of human personality.” He looks to the future and predicts that his Romantic Science will play a role. For Auden the stakes are even higher: “I’m scared of our divorce: I’ve seen some horrid ones.”
REFERENCES
Auden, W. H. (1976). Collected Poems. Edward
Mendelson, ed. (New York: Random House).
Jakobson, Roman and Morris, Halle. (1971). Fundamentals of Language.
(2nd rev. edn.) (The Hague: Mouton).
Kierkegaard, Søren. (1987). Either/Or, Part I. Howard V. Hong and Edna
H. Hong, eds. (Princeton: Princeton University Press).
Luria, A. R. (1987). The Mind of a Mnemonist. Lynn Solotaroff, tr.
Foreword by Jerome Bruner. (Cambridge: Harvard University Press).
Nietzsche, Friedrich. (1984). The Gay Science. Walter Kaufmann. tr.
(New York: Vintage Books).
Ogden, Jenni A. and Suzanne Corkin. “Memories of H.M.” Memory Mechanisms:
A Tribute to G. V. Goddard. Eds. Wickliffe C. Abraham, Michael C. Corballis,
and K. Geoffrey White. Hillsdale: Lawrence Erlbaum Associates, 1991.
Wilde, Oscar. (1985). The Picture of Dorian Gray. (New York: Penguin
Classics)
Dan Engber '98 (engber@fas.harvard.edu) is a Literature concentrator
in Winthrop House. Hailing from New York, Dan has suffered many illnesses
over time, but he has never once been a victim of ACUTE LUMBAGO.
Prosopagnosia:
A look at the laterality and specificity issues using evidence from neuropsychology
and neurophysiology
Mike Takamura
Introduction
“Prosopagnosia,” which is derived from the Greek words for “face” (prosopon) and “not knowing” (agnosia), is a condition that occurs as a result of an impairment in the medial occipitotemporal cortex of the human brain. Simply put, it results in the loss of ability to recognize and identify familiar faces. Damasio (1982) defines it succinctly in the following manner:
Patients with prosopagnosia only know that a face is a face and name it as such. Also, they can name parts of the face and point to them. Yet they are unable to recognize a given familiar face; i.e. they do not know to whom it belongs and consequently are unable to name it.
The earliest cases of prosopagnosia date from the late nineteenth century, as its symptoms were described by Wilbrand in a paper first published in Deutsche Z Nervenheilkd in 1892. However, the term “prosopagnosia” was not coined until 1947, when another German psychologist, Joachim Bodamer, described an injury to a 24-year old man who suffered a bullet wound to the head. The man mysteriously lost his ability to recognize his friends and family, and even his own face in the mirror (Szpir 1992). He was, however, able to recognize and identify them through other sensory modalities such as auditory, tactile, and even other visual stimuli patterns (such as gait and physical mannerisms). What he had lost, and quite, specifically, was his ability to recognize a face.
Since then, over 140 cases of prosopagnosia have been reported, as of 1992. Researchers have distinguished prosopagnosia from facial agnosia, since the former applies to familiar faces whereas the latter applies to any face, familiar or not. Interestingly enough, the familiar face does not have to be that of a human; Assal (1984) and Bruyer (1983) both mention cases where recognition of animals’ faces are impaired. Current opinion, however, regards these two impairments as actually part of the same overall problem (Bruce and Young, 1986).
The field, however, still remains riddled in other controversies
that are deep-rooted and have extensive histories; the issues are not likely
to be decided anytime soon, but recent research has at least progressed
towards a resolution. The main questions to be resolved are the following:
1) Is prosopagnosia caused by unilateral or bilateral damage to the
cerebral hemispheres?
2) Is prosopagnosia characterized by a failure in episodic memory components
or perceptual mechanisms?
3) Is a specific region of the brain allocated to the so-called “face
cells?”
This paper will present a number of answers to these questions, and analyze why the controversies still remain heated.
The Studies from Neuropsychology
In 1962, Hecaen and Angerlergues published a paper which claimed that patients diagnosed with prosopagnosia frequently have impairments in the left visual field, which correlates with damage in the right hemisphere. Twenty-two patients were studied, and sixteen were found to have damage in the right hemisphere, four with bilateral damage, and only two with left hemisphere damage (Benton, 1980). Their findings were replicated in a study by De Renzi et. al. (1968), who administered face recognition tasks to 114 subjects with unilateral brain damage. De Renzi’s study had initially set out to question two issues: 1) whether face fragments could be presented alone and still be identified correctly when asked to match with the whole picture, and 2) whether impairment of facial recognition was primarily a memory or perceptual deficit, as tested in a delayed memory task.
In the face fragment tasks, three experiments were conducted. First, patients were asked to match different parts of faces with the corresponding whole face image. Second, patients were asked to match the profile with the front view. Finally, an immediate memory test was conducted, where the patient was shown a photograph which was promptly removed, and then asked to identify the face he just saw among twelve different faces on display. De Renzi’s experiments confirmed Hecaen’s findings, showing that out of the 114 unilaterally damaged patients, the group that performed the poorest on the face fragment tasks were the nineteen patients with right brain damage and deficits in the corresponding left visual field. Furthermore, the fact that those patients with no damage in the left visual field (yet with damage to the right hemisphere) did better than those with a left visual field deficit led De Renzi and colleagues to the conclusion that recognition impairment of unfamiliar faces was most likely a perceptual deficit. They reasoned that facial recognition impairment in right brain damaged patients was due to the failure to process small subtle differences and to integrate visual cues.
In De Renzi’s second experiment, subjects performed a delayed memory task. The purpose of this experiment was to determine whether impairment of facial recognition was primarily a memory or perceptual deficit. A subject was shown a photograph of a face for a period of time; the picture was then removed from the subject’s view, and then the subject was asked to identify the face among a group, either immediately or with a minute delay. The reasoning behind the experiment was that if a memory component were crucial for face recognition, then a delayed memory task should give the greatest amount of impairment. The results of the test, however, showed no significant differences in correct responses between the tests. Thus, the claim that right temporal lobe damage and the facial recognition impairment associated with it was due primarily to memory was discredited; the experiment provided further support for De Renzi’s claim that the impairment in prosopagnosia was a perceptual one.
The notions of right hemispheric dominance in facial processing, however, were challenged in a paper by Damasio, Damasio, and Van Hoesen in 1982. In the post-mortem analysis of the patients cited in the original studies by Hecaen and Angerlergues, Damasio and colleagues discovered that all of the subjects had bilateral lesions. They claimed that those were “silent” lesions in the medial occipitotemporal regions which did not appear as overt visual defects because they were not detected in the initial clinical neurological tests. As Martha Farah (1990) notes, silent lesions present in the homologous region of the left hemisphere are usually smaller than their right hemisphere counterparts, thus making detection even more difficult. These silent lesions could also produce deficits in recognition and identification (Damasio 1985). Thus, according to Damasio, the tests developed from the early days in neuropsychology were not reliable enough to determine the extent of laterality in prosopagnosia, since posterior cerebral lesions fail to produce visual field defects. In Damasio’s opinion, silent lesions are the most likely cause of prosopagnosia when it appears that patients may have only unilateral damage. Therefore, it is likely that the damage is bilateral.
Based on his evidence that prosopagnosia requires bilateral lesions, Damasio argues that the deficit in facial recognition is one in which the memory processing is paramount (Damasio, 1985). Memory is not specially allocated into the separate hemispheres, whereas perception is. Benton (1990) agrees with this proposition, and offers two hypotheses as to why prosopagnosia is more than just a perceptual deficit. First is the notion that prosopagnosics suffer from recognizing individuals within a single class while noting no manifest deficit in recognizing the class itself. Second, Benton postulates that perhaps prosopagnosia is a “material-specific defect in memory” (Damasio 1982), for the problem lies in connecting the present facial stimuli with past representations and experiences. Damasio et al (1982) describe recognition as the “combined evocation of pertinent multimodal memories that permit the experience of familiarity with a given stimulus.” When we cannot recognize an individual, it is the inability to evoke pertinent nonverbal and verbal memories, the inability to give an appropriate response to a stimulus, that fails us.
What could be the possible reasons as to why the right hemisphere may be more efficient at face processing than the left? There is some physiological evidence that there are more neurons responsive to only face stimuli in the left1 hemisphere of the rhesus macaques (Perrett, Mistlin, and Chitty, 1987). Results from visuospatial tasks administered to monkeys confirm this anatomic left hemisphere bias (Jason, Cowen, and Weiskrantz, 1984). With such findings in monkeys, it is plausible to find anatomical correlates in humans as well.
Furthermore, Tovee and Tovee (1993) offer a simpler and yet ingenious reason as to why a right hemisphere dominance for face processing exists. Instead of asking the question of why the right hemisphere has special face processing abilities, they approach the problem from the viewpoint of why the left hemisphere somehow could not have the same abilities. Considering the fact that it has long been established that the left hemisphere subserves language, it seems very logical that the development of these areas in the left have restricted the availability of more cerebral space for other specializations. Therefore, language areas in the left temporal cortex are approximately mirrored in the right by the face processing units. This theory is particularly interesting because it places language processing and facial recognition on approximately equal levels of cognitive importance, giving new ideas as to how these faculties first developed.
The current most popular theory of face processing is that proposed by Bruce and Young (1986). They propose three stages to facial processing: the structural encoding stage, the physiognomic invariant stage, and the familiarity decision stage. In the first stage, the raw visual data enters the striate cortex and gets processed as a face. Basic features of the image, such as orientation, color, and position, are processed to give the image of a face, i.e. “what am I looking at?” In the second stage, the question of whether the face has a physiognomic invariant is asked. This means that we ask whether or not the face is one that is familiar. What makes the face unique to the individual is perceived at this stage, e.g. generic class versus individual. In the third stage, the familiar face is processed with all the episodic, semantic, and emotional memories, i.e. biographical data necessary to identify the person. Prosopagnosia can arise as a result of the damage at any one of these stages. Some people may not be able to recognize faces as faces, while others cannot recognize familiar faces (the most traditional notion of prosopagnosia), while still others can admit that the person seems familiar, yet strangely enough, they do not know anything about them, since the “road” accessing that particular memory in the visual association pathway has been destroyed.
The Studies from Neurophysiology
Nowadays, we have a good idea of how the visual system works, due largely to the work of Hubel and Wiesel from the 1950’s. In 1959, Hubel and Wiesel reported in the Journal of Physiology their data from single cell recordings in the cat striate cortex, which revealed the presence of neurons that had specific response properties to visual stimuli with bars or edges (Perrett and Rolls, 1983). Their model for visual processing is one in which there was a hierarchy of cells that were sensitive to more and more complex stimuli the further one went along the striate cortex pathway.
Early on in the visual pathway, simple cells in the occipital cortex resolve an image based on simple stimuli, such as bars or slits of light, whereas further along in the pathway, more complex cells respond to more sophisticated stimulus patterns, such as bars of particular length or orientation (Perrett and Rolls 1983). At the level of the visual association cortices, then, the image gets resolved into finer and finer components that have a more precise and accurate representation of the visual stimulus.
Once visual information reaches the primary visual cortex, it is processed further along two pathways (Bloom, 1988). One pathway deals with determining “where” something is in the visual space, and it travels along the superior visual association cortices, i.e. the occipitoparietal region (Damasio, 1985). The other pathway deals with “what” something is, and it is involved with the recognition and identification of visual information. This second pathway is the one primarily involved with prosopagnosia, and it is located along the occipitotemporal region.
In terms of Bruce and Young’s visual processing model, the superior temporal sulcus located in the occipitotemporal lobe represents the final physiological correlate of Stage 2, where physiognomic invariants are assessed and the determination (i.e. is this a familiar face?) of a face made. Not only are Stage 2 correlates of Bruce and Young’s model seen at the neuronal level, but they are also seen at the level of brain areas. Justine Sergent (1992) conducted tests of facial recognition while using positron emission tomography (PET) and magnetic resonance imaging (MRI) to show evidence of specific brain areas that performed facial recognition processing. When Sergent compared the PET scans of prosopagnosics with normal patients, she was able to locate three prominent regions in the occipitotemporal lobe that “lit” up when processing a familiar face, that did not light up in the PET scans of prosopagnosics. She concluded that the three regions could represent anatomical correlates to Bruce and Young’s model. One region in the posterior occipital region was thought to be involved with the perceptual operations of extracting unique facial features. Slightly anterior to the first region is another region which is involved in making connections between the face and biographical information. Slightly anterior to the second region, located in the temporal lobe, is the third region, which acts like a file cabinet that contains all the biographical data (Szpir, 1992). These three regions correlated with the perceptual operations stage of visual grouping (Perrett et al., 1987), the structural encoding stage, and the biographical memory stage (Bruce and Young, 1986).
The fact that there are clusters of “face” cells in various brain components along the occipitotemporal pathway indicates that instead of adopting the previously popular idea of a specific “face” region in the brain, it would be more appropriate to assign a specific face “pathway” which is a distinct part of the overall visual processing pathway. But as Perrett sternly warns (1988), we do not in any sense have proof for a brain area exclusive for facial processing, only that there exists a facial processing subsystem within certain brain areas.
Conclusion and Remarks
Converging evidence suggests that there may be a face “pathway” in the occipitotemporal region. Controversy remains, however, as to the nature of the impairment in prosapagnosic patients and the issue of laterality.
Prosopagnosia: A Deficit in Perception or Memory?
Damasio cites evidence that patients with prosopagnosia have deficits for other complex visual stimuli. For example, some patients are unable to recognize familiar animals or buildings. However, there is contrary evidence to this notion. Perrett et al. (1988) cite evidence provided by Assal (1983) and Bruyer (1984), whose patients were able to recognize familiar complex stimuli, further supporting the notion that prosopagnosia is not solely associated with damage to the third stage of Bruce’s model, i.e. the biographical memory retrieval components. Prosopagnosia, according to Damasio (1985), is the impairment of the recognition process at the stage where specific identification of a member within a group is required. As the following evidence shows, in some cases, Damasio’s contention does not hold.
Assal’s patient was a farmer who could individually discriminate his own cows, but was not capable of distinguishing his own friends. And Bruyer’s patient, also a farmer, had the exact reverse of this condition, where he was able to differentiate among and correctly identify his friends but not do this his own cows. Does this mean that we can infer a certain “cow” pathway? The answer is both yes and no. To the farmer, the face of the cow has a specific biographical representation in the association cortices. Thus the cow’s face was also part of the general “face pathway” because of the associations built with it. For a person who does not associate with cows everyday, there would be no significant representation stored in the association cortices that would elicit a response upon the sight of a cow.
There is record of another patient, C.K., as described by Gurd and Marshall (1992), who can recognize famous faces and match unfamiliar faces, but cannot distinguish between a feather duster and a dart. These are cases where prosopagnosia occurs with visual object agnosia. Therefore, these findings seem contrary to Damasio’s claim that prosopagnosia is not a perceptual problem but a problem with integrating visual information with contextual (episodic) memory. Rather, it seems to be a deficit where both perception and memory are compromised in different proportions, depending on the specific case involved.
One may argue that prosopagnosia is primarily an impairment in the biographical memory stage, but Damasio’s argument uses the claim that the perceptual mechanismsin prosopagnosics appear normal to make its case stronger (Damasio, 1985). As he showed, two of his patients with prosopagnosia were able to discriminate complex visual stimuli such as an owl or an elephant, whereas mistaken identities were made between rather simple visual stimuli such as an exclamation point or a dollar sign. The fact that the complexity of the stimuli had little to do with the identification errors supports his view that the deficit is one where a generic class versus individuals cannot be distinguished. But relevant studies done by Perrett et al. show that cells in the superior temporal sulcus are definitely sensitive to changes in orientation and profiles of faces, whereas emotion is not a critical variable. Thus we can infer that these cells are most likely involved in visual analysis and not other higher processing schemes. Why is this finding so important to the perceptual argument? First of all, it would be meaningful to mention the specificity of these cells were they located in the striate cortex, for there the complex visual cells are expected to and should respond to color, orientation, and position. Thus these cells in the striate cortex do not pose an argument for Damasio’s claim at all.
According to the model of a face pathway, prosopagnosia, depending on the nature of the damage, can involve facial recognition impairments in general, and not just the inability to recognize familiar faces. The bilateralist’s view of prosopagnosia as mainly a biographical information retrieval and integration problem seems too confined because it only describes the last stage of facial processing.
Prosopagnosia and Laterality
The laterality issue still seems to have conflicting arguments, namely along the lines of whether or not bilateral lesions are required for complete prosopagnosia as opposed to unilateral lesions for certain aspects of prosopagnosia. De Renzi (1994) made claims that in a PET study of three patients, all with right hemisphere unilateral damage, prosopagnosia was manifest. Perrett’s finding of the asymmetrical distribution of face cells, with the left hemisphere processing more such units, sheds light on the fact that facial processing definitely relies more on the left hemisphere (for macaques, with the implication that it is the right hemisphere for humans) as opposed to equally on both hemispheres.
So after all these arguments made by different researchers, we ask ourselves again why we even care about the laterality issue in prosopagnosia. The reason is that because the brain is asymmetric, it is possible that its hemispheres work via different mechanisms, even though the end result may be the same. So, in our case, a possible resolution to the laterality controversy is to state that both hemispheres play a role in facial processing, but the mechanisms they utilize allow for a more efficient processing in one hemisphere over the other. Perrett’s finding of an asymmetric distribution of face cells may explain why Damasio found larger left hemisphere lesions, and yet the right hemisphere plays the key role in face recognition, since it is the right hemisphere that has a greater proportion of specific face cells. Perrett et al. (1988) propose the finding that monkeys may either process faces by piecemeal fashion, dissecting individual features of the face and encoding that information to arrive at an identity. For humans, this would correspond to the approach taken by the left hemisphere. However, as studies (Perrett and Rolls,1983; Perrett et al., 1988; Tovee and Tovee, 1993) show, the right hemisphere in humans is specialized for structural configurational processing and thus process complex visual stimuli much more efficiently than would by the piecemeal method. This statement for right hemisphere feature configuration versus left hemisphere piecemeal manner processing has been proposed foremost by cognitive neuroscientists as well (e.g. Carey and Diamond, 1977; Yin, 1969; Hillger and Koenig, 1991). Damasio (1982) states that template formation of a visual image and template matching may be more global in the right hemisphere, and that these differences in storage and retrieval between the left and right hemispheres may be biologically advantageous. The rare instances where individuals with unilateral damage have complete prosopagnosia may be due to individual differences in the relative strength of the hemispheres. Sergent states the best conclusion on laterality and facial processing. Sergent (1994) states that facial processing is “a bilateral, yet asymmetric, involvement of the cerebral hemispheres.”
So what we have gained to date are the following:
1) Prosopagnosia can and should be seen as both a perceptual and memory
deficit, depending on which stage visual processing the lesion occurs.
2) The right hemisphere is the more efficient at face processing, in
order for a complete loss of facial processing, a bilateral lesion must
occur.
3) The notion of face specific cells may be substantiated, but prosopagnosia
is not limited to just human faces; rather, it is the individual associations
that trigger the appropriate responses.
4) The notion of a face pathway more accurately describes the broader
framework of prosopagnosia and visual associative agnosia, for it is not
the specific regions within certain pockets of brain areas that are selective
for faces, but a clear and visible path (as seen in latency time studies)
along the entire occipitotemporal region.
REFERENCES
Assal, G., Faure, C., and Andreas, J.P. (1984). Revue
Neurology (Paris), 140: 580-584.
Benton, A. (1980). The neuropsychology of facial recognition. American
Psychologist 35(2): 176-186.
Bloom, F.E., and Lazerson, A. (1988). Brain, mind, and behavior. (New
York: W.H. Freeman & Company).
Bruce, V., and Young, A. (1986). Understanding facial recognition.
British Journal of Psychology 77: 305-327.
Damasio, A.R. (1985). Prosopagnosia. Trends in Neuroscience 8: 132-135.
Damasio, A.R., Damasio, H., and Van Hoesen, G.W. (1982). Prosopagnosia:
anatomical basis and neurobehavioral mechanism. Neurology 32: 331-341.
De Renzi, E., Faglioni, P., and Spinnler, H. (1968). The performance
of patients with unilateral brain damage on facial recognition tasks. Cortex
26: 18-33.
De Renzi, E., Perani, D., Carlesimo, A., Silveri, M.C., and Fazio F.
(1994). Prosopagnosia can be associated with damage confined to the right
hemisphere - an MRI and PET study and a review of the literature. Neuropsychologia
3( 8): 893-902.
Gurd, J.M., and Marshall, J.C. (1992). Drawing upon the mind’s eye.
Nature 359: 590-591.
Hecaen, H. and Angerlergues, R. (1962). Agnosia for faces . Soc. for
Info. Disp. Arch. Neurol. 7: 92-100.
Hasselmo, M.E., Rolls, E.T., and Baylis, G.C. (1989). The role of expression
and identity in the face-selective responses of neurons in the temporal
visual cortex of the monkey. Behavioural Brain Research, 32: 203-218.
Jason, G.W., Cowey, A., and Weiskrantz, L. (1984). Hemispheric asymmetry
for a visuosspatial task in monkeys. Neuropsychologia 22: 777-784.
Leonard, C.M., Rolls, E.T., Wilson, F.A.W., and Baylis, G.C. (1985).
Neurons in the amygdala of the monkey with responses selective for faces.
Behavioural Brain Research 15: 159-176.
Meadows, J.C. (1974). The anatomical basis of prosopagnosia. J. Neurol,
Neurosurg. Psychiat. 37: 489-501.
Perrett, D.I., Mistlin, A.J., and Chitty, A.J. (1987). Visual neurones
responsive to faces. TIN 10( 9): 358-364.
Sergent, J. (1995). Hemispheric contribution to face processing: patterns
of convergence and divergence. In Brain Asymmetry. R.J. Davidson and J.
Hugdahl, eds. (Cambridge: MIT Press), pp.157-182.
Szpir, M. (1992). Accustomed to your face. American Scientist 80: 537-539.
Tovee, M.J. and Cohen-Tovee, E.M. (1993). The neural substrates of
face processing models: a review. Cognitive Neuropsychology 10(6): 505-528.
Whiteley, A.M. and Warrington, E.K. (1977). Prosopagnosia: a clinical,
psychological, and anatomical study of three patients. J. Neurol. Neurosurg.
Psychiat. 40: 394-430.
1 In rhesus macaques, the hemispheric specializations appear to be
reversed than it is for humans. Thus in humans, face processing seems to
involve a strong right hemisphere component, as to general spatial and
configurational judgments in general (Perrett et.al. 1988). However, this
finding should not be surprising, for hemispheric asymmetries evolved for
the sole purpose of specialization of different and specific types of information
processing. There is no dogmatic reason why one hemisphere should be favored
over the other. For a more detailed account on the evolutionary basis for
cerebral lateralization, see Hiscock and Kinsbourne’s article in Brain
Asymmetry (1994).
Mike Takamura '97 (takamura@fas.harvard.edu) is a Winthrop House resident concentrating in Cognitive Neuroscience.
The
Psychologizing of Religion in William James' Varieties of Religious Experience
Tracey Cho
Introduction
In the Varieties of Religious Experience, American psychologist and philosopher William James (1842-1910) embarked on a uniquely empirical treatment of religious phenomena. Presented as the Gifford Lectures of 1901-1902 at Edinburgh, Varieties addressed religion’s usefulness in human experience. Amidst debates on the validity of religion in a world of physiology and philosophy, James attempted to prove the validity of religious experience for the individual and to create a new “Science of Religion” to compile and interpret those experiences empirically. Shunning a priori arguments for the Absolute and yet remaining open to the reality of supernatural external forces, his method lay in compiling religious experiences as “facts” and then unifying them in psychological theory. Contemporary theories of the subconscious informed James’ psychological approach. While he drew upon Janet and Binet’s work on the subconscious for scientific grounding, the “Subliminal Self” bythe British theorist Frederic Myers provided James with a ladder with which to climb from abnormal experience to religious insight. Reflecting on his scientific analysis of religious experience, James concluded his lectures with a look at the broader personal and social implications of taking an empirical approach to a topic which had previously confounded science.
Scientific Validity
James lived in an era permeated by science. As the designated authority on what to believe, science wielded a tremendous amount of power and influence over popular opinion. Rather than trusting their instincts or emotions, people were encouraged to base their beliefs on scientific fact established by thorough experiment. According to John Herman Randall, a contemporary of James,
... we are living in a scientific age, which seeks to base all its conclusions not on conjecture or faith, but on facts, just so far as these can be discovered. And the demand of such an age, in religion as well as everywhere else, is for certainty, not for mere pious conjecture or an only imaginary hope. If we are to believe today, the demand is for clear evidence that our beliefs are founded on facts, not on delusions. (Randall, 2)
For James and other contemporary intellectuals, science was the only instrument through which their beliefs, as well as their theories, could be validated. Thus, James approached Varieties as a scientific treatise addressing traditionally unscientific phenomena.
Unfortunately for James, empirical science was not willing to address such realms of phenomena. In his essay“The Hidden Self,” James asserted that the goal of science was a “closed and completed system of truth” (Allen, 90). In other words, any phenomenon which did not fit within the realm of “clean” or reductionist science was not to be admitted as evidence. In his tribute to Frederic Myers, James again described the problem, this time distinguishing between the “classic-academic” and the “romantic” investigator. He attributed the former tendency to all psychology up to the time of Myers, which had a “fondness for clean pure lines and noble simplicity in its constructions.” According to James, those “clean” conceptions of nature “missed the native quality of existence” (Perry, 156). The romantic, such as Myers, however, searched beyond the pretty picture to the “fantastic, ignoble, hardly human, or frankly non-human” psychological phenomena (Memories , 148-149). Although ignored and even despised by science, these sketchy phenomena of the “Unclassified Residuum” gave much hope to James (Allen, 90).
James had always entertained a sympathy for the speculative or despised realms of the “Unclassified Residuum.” He actually enjoyed playing big brother to neglected or detested realms of knowledge, and he often served as the equalizer in conflicts where science was seen as the aggressor. Referring to adherents of science narrowly defined, James lamented, “With these persons it is forever Science against Philosophy, Science against Metaphysics, Science against Religion, Science against Poetry, Science against Sentiment, Science against all that makes life worth living” (Perry, 30-31; 155-156).
Having amassed a store of descriptive phenomena as prescribed by science, James then turned to the assertion of their authenticity. Even if all these religious phenomena occurred, how could science be sure that these were not fraudulent imitations ingeniously created by overzealous mystics? Admittedly, religious accounts were not always reliable. The religious witness tended to shape the experience around the ideal which he or she considered the most significant. Still, James could speak confidently about these ideal accounts because experiences all pointed to the ideal (Perry, 338). Furthermore, James argued, what right had science to question literal truth, when so many scientists themselves compromised authenticity for the “larger truth?” For example, at public lectures a scientist would contrive an experiment which would otherwise fail in order to illustrate a truth about nature; yet, the scientist’s honesty was not questioned for such an imitation of nature (Memories 181). James thus attempted to establish the factual truth of religious experiences. This constituted the “existential judgment” which James asserted an investigator must make, that is, questions of historical fact concerning the nature, origin, and history of the phenomena. “Spiritual judgment,” which involved a determination of value and questions of meaning and significance, was yet to be made (Varieties 13).
James attempted to defend mystical and psychic experiences from attacks against their unromantic and ignoble nature. According to James, most people naturally associated exalted origins with divine importance, which made spiritual value dependent upon high causes. By asserting that a hallucination was the effect of the divine on the brain, supernatural importance was attached to the hallucination itself. For James, however, this contingent relationship was not necessary. In fact, a group of skeptics under the head of medical materialists employed an analogous argument to deny the validity of religious experiences. Rather than invoking the divine meaning from the origins of religion, the medical materialists dismissed spiritual encounters by associating them with malfunctions of the physical body. What the religious subject felt as a divine encounter was considered no more than a pathological manifestation. Thus, St. Paul’s revelation on the road to Damascus was invalidated as an epileptic fit. James inverted this argument back upon the medical materialists and asserted that scientific theories were just as much a manifestation of organic tendencies as religious fervor. Hence, unless a value were given to the physiology itself, the value of the experience could not be determined by causation (Varieties 19-24). James safely assumed that no scientist would assert that a certain physiological function had value — that thin blood was a sign of religious truth or that a stomach ache represented bad science.
James established this basis of judgment from the outset of Varieties in order to open the minds of his audience to the lessons to be learned from the morbid and pathological states which he was about to present. The displeasing nature of mystical and religious experiences should not condemn their study to morbid speculation: “To reject [such research] for its unromantic character is like rejecting bacteriology because penicillium glaucum grows on horse-dung and bacterium termo lives in putrefaction” (Memories, 186). He attributed extreme religious experiences to psychological pathology, but he did not want to disprove the validity or diminish the value of them for his audience merely because of their origin. James chose to study extreme cases of religious feelings and mystical encounters precisely because of their similarity to pathological experiences. In the tradition of empirical medicine, he planned to use abnormalities to help explain the meaningfulness of “normal” mental life. In fact, he argued that all of us exhibit some neurotic tendencies and that “a life healthy on the whole must have some morbid elements” (Taylor, 15).
To further relax his audience’s anxiety at such morbid phenomena, James attributed the quality of genius, in both religious and secular realms, to neurosis. Such abnormal tendencies were necessary for a person to act on his religious or otherwise creative visions. While a normal person asked, “What shall I think of it?”, the neurotic asked, “What must I do about it?” (Varieties, 29). It was this active response to the religious experience which made the pathological cases so instructive. By arguing thus, James hoped to convince his audience that they should not judge religious phenomena by their origin or by their morbid nature. If they could not judge experiences by their origin, then how could they judge them? James’ general pragmatic answer to this was, “By their fruits ye shall know them, not by their roots” (Varieties, 26).
Psychology of the Subliminal
As James wrote in his article “The Hidden Self,” the French psychologists studying the subconscious provided much of the content of his psychology. French psychologists such as Pierre Janet and Alfred Binet were leading the way in studying “abnormal personal peculiarities” such as hysteria, automatism, and other psychic phenomena. He described the theory of Janet as presented in De l’Automatisme Psychologique (Binet had independently arrived at a similar theory). According to Janet, hysteria was caused by a contraction of the field of consciousness. Unlike the person with normal consciousness, the hysteric could absorb only so much into his or her consciousness. Because of this monoideism or smaller field of consciousness, the hysteric was forced to block out parts of normal consciousness. This explained the symptoms such as hysteric blindness or anesthesia in certain parts of the body. Janet compared the state of hysteria to that of a normal person during hypnosis, in which the consciousness was allowed to focus on only a narrowed part of the usual field ( Allen, 93-94).
An important discovery for James was the multiple personalities of some of Janet’s patients. James described Janet’s discovery of three personalities in one subject named Lucie. Through hypnotic techniques, Janet was able to induce a trance in which a different personality or consciousness arose. The new personality was called Lucie 2 (by Janet), and although she was fully aware of Lucie 1, she denied that they were the same person. Upon further hypnosis, Janet discovered Lucie 3, a third personality who again was aware of both previous personalities. While each successive personality was aware of the other ones, the earlier ones had no knowledge of the existence of the later personalities. Lucie thus displayed three distinct personalities or consciousnesses (Allen, 94-96).
For James and the question of religion, the most significant aspect of Janet’s studies was not that patients exhibited successive personalities, but that they exhibited them at the same time. Such coexisting but independent personalities were demonstrated by such techniques as hypnotic suggestion and automatic writing. For instance, Janet would place a pencil in the subject’s hand and then distract him or her by some activity such as conversation. When a voice whispered a question in the subject’s ear, the subject’s other consciousness would spontaneously guide the hand to write the answer, while the primary consciousness remained preoccupied with the conversation. Janet claimed that in some people, consciousness was split into two or more entities, each of which ignored and complemented the sensibilities of the other (Allen, 99-100).
Janet attributed the split consciousness to mental or moral weakness. Possibly a hereditary trait, this weakness became apparent only when a traumatic event or events affected the brain. When the brain’s capacity to maintain focus was undermined, waking consciousness was split, allowing subconscious elements to displace normally waking ones. This opening of the subconscious accounted for the greater influence of suggestion and hypnosis in these persons (Taylor, 22). For Janet, the different consciousnesses added up to no more than one normal consciousness. James, however, took the theory a step further. He referred to cases in which the secondary consciousness transcended the possible normal consciousness (Allen, 108). James turned to the Subliminal Self of Frederic Myers to account for this supernormal consciousness.
Frederic Myers, a Victorian psychologist, classicist historian, and psychical researcher, had developed the concept of the Subliminal Self in his quest to give a scientific basis for immortality. Like Wordsworth, he believed that nature revealed a window to the spiritual world (Turner, 118). While Wordsworth expressed his spiritual attitude toward nature in his poetry, Myers turned to psychical research to collect the facts of nature concerning the spiritual. He was a founder of the Society for Psychical Research (SPR) in England. In an address to the SPR in 1901, James paid tribute to the studies of Myers. James noted how Myers had collected facts through the SPR, looked at all of them arranged in series, and made hypothetical connections where it was necessary (showing again James’ preoccupation with collection of facts as a scientific procedure).
The theory which Myers postulated to connect the facts was centered around the Subliminal Self. He identified the supraliminal or empirical consciousness as a part of consciousness evolved to deal with the natural environment. It was, however, not the only part. Other regions of consciousness allowed humans to be active in other realms of being; these other regions constituted the Subliminal Self. Myers explained hallucinations and active impulses as sensory and motor automatisms. Automatisms, to him, were merely symbolic messages from the subliminal to the supraliminal consciousness (Allen, 157-160). The Subliminal Self designated all the aspects above and below the supraliminal consciousness, including “the disintegrative streams of consciousness that were manifested in hysteria, the personality absorbing cosmic metetherial energy in sleep, and the personality rising to new spiritual awareness in ecstasy and sleep” (Turner, 124). This was the key step in James’ climb to the supernatural, for the subliminal consciousness accounted not only for those regions below, but also those above the supraliminal. Myers pointedly disavowed the view that “any perturbation of the ordinary personality is necessarily in itself an evil” (Myers, 6). In the phrase “above the supraliminal,” Myers included the passion of the artist and the inspiration of the scientist, attributing creative and artistic imagination to a “subliminal inrush of creative energy” (Turner, 130). More importantly to James, the subliminal consciousness also included the mystical realms of supernormal knowledge.
This positive aspect of the Subliminal Self was the distinguishing trait of Myers’ psychology, for others had already conceived of a subconscious realm whence mental disease originated. Myers asserted that “hidden in the deep of our being is a rubbish-heap as well as a treasure-house; — degenerations and insanities as well as beginnings of higher development” (Turner, 122-130). This was the bridge which James needed to cross from the ostensibly morbid nature of religious experience to glorified spiritualism. Myers’ theory parried the attacks of the medical materialists on the origin of the phenomena in question: “... In so far as they have to use the same organism, with its preformed avenues of expression — what may be very different strata of the Subliminal are condemned in advance to manifest themselves in similar ways” (Allen, 160). Thus, supernormal knowledge, mania, drivel, and deception all came to the surface through the same channels. This was the reason why mystical experiences were viewed as pathological. Such revelations, however, were not to be discounted merely because they were forced to emanate through the same channels as mental disease.
Religious Experiences as Subliminal Emanations
James indicated a division in the self as the underlying animus for the religious experience. The self was divided in a conflict between its higher and lower wishes. During the struggle for unification of these impulses, the person experienced the most intense grief and anguish. Not merely a battle between physical and spiritual impulses, the struggle also consisted of conflicts between multiple drives (Varieties, 156-158). The varying processes of unification brought characteristic sorts of relief. Although this resolution was a general psychological process, James focused on the religious form, which he termed conversion (Varieties, 165-176).
James introduced the concept of a “centre of personal energy” to help explain the process of conversion. According to this idea, every person, depending on the time and situation, focused his or her energy on a specific portion of consciousness. Whenever this centre of personal energy changed, the person underwent a transformation. This transformation was not necessarily religious in nature, but when it was, it constituted a conversion:
An athlete ... sometimes awakens suddenly to an understanding of the fine points of the game and to a real enjoyment of it, just as the convert awakens to an appreciation of religion. If he keeps on engaging in the sport, there may come a day when all at once the game begins to play itself through him — when he loses himself in some great contest. In the same way, a musician may suddenly reach a point at which pleasure in the technique of the art entirely falls away, and in some moment of inspiration he becomes the instrument through which music flows ... so it is with the religious experience of these persons we are studying. (Varieties, 192)
At this point, James expounded his psychological explanation. In such cases of transformation, the door to the subconscious had finally been opened. The tension and angst had reached the maximum capacity of the mind. Thus, the brain was no longer able to hold the supraliminal together to the exclusion of the subliminal; the breaks in the “accidental fences” had been found: “When the centre of personal energy has been subconsciously incubated so long as to be just ready to open into flower, ‘hands off’ is the only word for us, it must burst forth unaided!” (Varieties, 195). While Christianity asserted this self-surrender to the descent of an external supernatural force, psychology asserted it to subconscious impulses from within reaching normal consciousness.
According to his psychological explanations, those who experienced religious conversion were those who were more prone to opening the door to the subconscious. He even cited a study by Professor George A. Coe which showed that converts were prone to automatism and passivity while non-converts were more spontaneous and self-suggestive, implying that the non-converts were denied transformation by their spontaneous assertion of the impossibility of conversion (Varieties, 221-222). Despite this scientific explanation of religious conversion, James made a point not to exclude the possibility of divine influence: “It is conceivable that if there be higher spiritual agencies that can directly touch us, the psychological condition of their doing so might be our possession of a subconscious region which alone should yield access to them” (Varieties, 223). In this admission, James hinted at his sympathetic view toward the epistemological validity of religious experiences and the existence of a supernatural force.
James summarized religious experience by describing its two universal aspects. First, something was innately wrong with the subject. Second, only connecting with a higher power resolved this wrongness. James then hypothesized that the “more,” the higher power with which the religious subject connected beyond normal consciousness, was the “subconscious continuation of our conscious life.” Thus, there really was a higher power — it just happened to be part of ourselves. The “higher” control was exerted by the “higher faculties of our own mind;” thus, the sense of union with a higher power was literally true. From this truth individuals diverged into what James termed personal over-beliefs, the specific forms in which the truth became manifest, depending on their religious needs. These various over-beliefs, though they might negate each other, were indispensable for the individual (Varieties, 458-461).
In the final pages of Varieties, James finally revealed his own over-beliefs concerning religion. Given the truth of the union with a higher power, he designated that higher power supernatural. According to James there was a distinct supernatural connection to the subliminal consciousness. While our personalities mingled through our supraliminal consciousnesses in the physical world, we were also joined in the underlying “mother-sea” of subliminal consciousness:
Out of my experience, such as it is (and it is limited enough) one fixed conclusion dogmatically emerges, and that is this, that we with our lives are like islands in the sea, or like trees in the forest. The maple and the pine may whisper to each other with their leaves, and Conanicut and Newport hear each other’s foghorns. But the trees also commingle their roots in the darkness underground, and the islands also hang together through the ocean’s bottom. Just so there is a continuum of cosmic consciousness, against which our individuality builds but accidental fences, and into which our several minds plunge as into a mother-sea or reservoir. (Memories, 204)
It was the weaknesses in the fences which allowed the subliminal to manifest itself in the supraliminal world, “leaking in” on the natural world. Because the ideal (the religious sentiments from the subliminal realm) were given to influencing the real (the resulting conduct of the individual), the ideal was a definite part of reality (Varieties, 462-469). By exerting a force in the actual world, the supernatural became a reality itself. Thus, James’ psychological perspective of religious experience allowed him to develop a “piece-meal” supernaturalism, in which the ideal and the real were intertwined.
Conclusion
When James wrote Varieties at the turn of the century, science had recently gained dominance in popular as well as intellectual culture. For many, the cold, heartless assumptions of science shook the spiritual foundations of their lives. Was life reducible to physico-chemical mechanisms? Were humans mere automatons of nature? By collecting data on religious experiences and explaining them through psychological theory, James brought religion within the realm of science. Rather than ignoring the phenomena as “unclean,” James depended on science to give validity to the religious experiences. Through this empirical approach, James sought to give religious experiences power as guides for life — guides validated, not through faith, but through science.
The unique scientific approach of William James to religion provides an instructive comparison for the modern neuroscientist. Given that the latest research continues to reduce the functions of the brain to neurobiological mechanisms, one may easily assume the position that the wonders of the mind can likewise be reduced to mere mechanisms. Obviously neurobiological and psychological research are a crucial part of modern medicine and science, but, as responsible scientists, researchers must keep abreast of the larger implications of their work. They must maintain an open mind and continually challenge their assumptions. Especially as modern brain scientists continue to elucidate the higher functions of the brain, they should, like James, maintain an awareness of the larger implications of their work in a culture which places so much trust in science and so much hope in faith.
REFERENCES
Allen, Gay Wilson, ed. (1971). A William James Reader. (Boston: Houghton
Mifflin).
James, William. (1911). Memories and Studies. (New York: Longmans,
Green, and Co.).
James, William. (1987) Varieties of Religious Experience. William James:
Writings 1902-1910. (New York: Library of America).
Myers, Frederic W. H. (1976). The Subliminal Consciousness [1893-1894].
In Proceedings of the Society for Psychical Research. (New York: Arno Press),
pp. 2-25.
Perry, Ralph Barton. (1935). The Thought and Character of William James.
Vol. 2 of 2. (Boston: Little, Brown, and Co.).
Randall, John Herman. (1921) William James: The Philosopher. The New
Light on Immortality. (New York: Macmillan), pp. 33-50.
Taylor, Eugene. (1982) William James on Exceptional Mental States:
The 1896 Lowell Lectures. (New York: Charles Scribner’s Sons).
Turner, Frank Miller. (1974) Frederic W. H. Myers: The Quest for the
Immortal Part. Between Science and Religion. (New Haven, CT: Yale University
Press), pp. 104-133.
Tracey A. Cho '97 (tacho@fas.harvard.edu), a resident of Cabot House,
is concentrating in History and Science, with a focus on neuroscience and
modern European history. He is currently doing research on medulloblastoma
child brain tumors under Scott Pomeroy at Boston Children’s Hospital.
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Neurobiological
Insights into Infantile Autism
Amy Herman
Infantile autism is a developmental disorder characterized by deficient communication, socialization, and imagination. Kanner first described the disorder in his 1943 paper entitled “Autistic disturbances of affective contact.” Kanner placed emphasis on the autistic child’s inability to relate to other people:
I was struck by the uniqueness of the peculiarities which Donald exhibited. He could, since the age of two and a half years, tell the names of presidents and vice presidents, recite the letters of the alphabet forwards and backwards, and flawlessly, with good enunciation, rattle off Twenty-Third Psalm. Yet he was unable to carry on an ordinary conversation. He was out of contact with people, while he could handle objects skillfully. His memory was phenomenal. The few times when he addressed someone- largely to satisfy his wants- he referred to himself as “You” and to the person as “I”. He did not respond to any intelligence tests but manipulated intricate form boards adroitly. (Kanner, 1943)
Kanner identified the following characteristic features of these children: extreme autistic aloneness, anxiously obsessive desire for the preservation of sameness, excellent rote memory, delayed echolalia, oversensitivity to stimuli, limitation in the variety of spontaneous activity, good cognitive potentialities, and highly intelligent families. He considered these findings “a unique syndrome not heretofore reported.” Almost simultaneously, in 1944, Asperger published a dissertation about the autistic psychopathy in children treated by him in Vienna. Asperger described a high-functioning form of autism, where mental retardation was not prominent. Ironically, Kanner and Asperger independently chose the term “autistic” as a label for their patients. They borrowed the term autism from Bleuler, who used it to describe schizophrenic patients’ withdrawal from social interaction. (In Greek, “autos” means “self.”) This choice reflects their independent observation that the impaired child’s social problems were the most striking feature of the disorder. It has been suggested that Kanner’s syndrome may represent either “classical” autism, or the prime mode of presentation of a number of related disorders. Kanner’s syndrome and Asperger’s syndrome may possibly occupy positions on a continuum of cognitive deficits characterized by impairment in reciprocal social interaction (Ciaranello and Ciaranello, 1995).
There is still great interest in understanding the neurobiology of infantile autism. The purpose of this paper is to review work done in the study of autism from the biologic viewpoint.
The Clinical Picture of Autism
Autism occurs in about 5 per 10,000 live births (Folstein and Piven,
1991). It varies in its clinical presentation, depending on both the severity
and the extent of the underlying brain dysfunction. Severely autistic children
may be retarded, mute, profoundly withdrawn, and preoccupied with repetitive
activities. They often exhibit motor stereotypies such as hand-flapping,
rocking, or head-banging. More mildly affected children may show deficits
in social relatedness, yet they may have normal or even superior intelligence,
with well-developed language skills. These children may be given the diagnosis
of Asperger’s syndrome (Ciaranello and Ciaranello, 1995).
Autistic children display a wide range of intellectual abilities, from
profound mental deficiency to superior intelligence. It is generally thought
that less than 30 percent of autistic persons have intelligence in the
normal range (Dunn, 1994). Some autistic persons show exceptional talents
despite functional disability in general. Ten percent of autistic persons
have been reported to demonstrate savant abilities in music, drawing, or
calculation (Happe, 1994). Since autistic characteristics may occur in
children with superior intelligence, it seems likely that a neurologic
insult causing autism is not always accompanied by mental retardation (Ciaranello
and Ciaranello, 1995).
Evidence for an Organic Cause
There is evidence that many autistic children have severe brain
dysfunction. Steffenburg (1991) found that almost 90 percent of a sample
of autistic children had evidence of a brain abnormality. Recently it has
been reported that a substantial proportion of cases are associated with
megalacephaly, an abnormal enlargement of the head (Bailey et al., 1995;
Bailey et al., 1993). Autistic persons may demonstrate a variety of other
abnormal behaviors and cognitive deficits. These may include rigid adherence
to routines, stereotypical movements, and bizarre use of objects. These
findings, however, are not specific to autism, and serve to illustrate
multiple etiologies of the syndrome.
Autistic behavior has been observed in association with a number of
well-defined medical conditions. These conditions include the fragile X
syndrome, congenital rubella, and tuberous sclerosis. None of these conditions,
however, has a consistent association with autism (Rapin, 1991).
There is a 4:1 male preponderance in the incidence of autism, which
suggests a genetic influence in a significant number of cases. A genetic
liability is also supported by evidence from twin and sibling studies (Bailey
et al., 1995; Folstein and Piven, 1991). There seems to be no single etiology,
however, and it is not likely that a single pathophysiologic mechanism
will account for all cases. It is more reasonable to consider a pathophysiologic
model where damage to a certain brain region, called Region X, produces
features found in autistic children. This model may be consistent with
the observation that there is a direct relationship between the severity
of autism and progressively lower IQs (Ciaranello and Ciaranello, 1995).
The degree of severity may reflect the extent of insult to Region X. In
fact, it is possible that a number of different events, such as genetic
abnormality or birth trauma, may also cause damage to Region X, resulting
in autism (Happe, 1994).
Searching for the Site of Dysfunction
“Region X,” the site of a critical insult to the brain that uniformly
causes autism, has not yet been identified. Various anatomic sites in the
brain have been hypothesized as the primary source of pathology. Five approaches
have been used to study the neurobiology of autism. These methodologies
include genetic, radiological, neurophysiological, neurochemical, and neuropathological
studies. Genetic analyses include epidemiological studies, twin studies,
family studies, linkage and association studies, and karyotypical analyses
(Ciaranello and Ciaranello, 1995). Radiological studies use in vivo brain
imaging techniques. These include computerized tomography (CT), magnetic
resonance imaging (MRI), and positron emission tomography (PET). Most neuroimaging
studies have reported structural abnormalities in the brains of autistic
persons, but the findings are inconsistent both within and across investigations.
A variety of neurophysiological methods, such as electroencephalography
(EEG) and event-related potentials (ERP), have been used to study patterns
of neural processing in patients with autism. Neurophysiologic deficits
are widespread, but not specific to autism. Neurochemical studies attempt
to link abnormal levels of neurotransmitters or their metabolites to autism.
No consistent abnormality has emerged from these analyses. A number of
reasons have been proposed to explain the inconsistency in the findings
in various studies of the neurobiology of autism. Rutter and Bailey (1993)
speculate that the inconsistencies in neuroimaging studies may result from
heterogeneity in the clinical groups studied, methodologic shortcomings,
and the lack of precise quantification of the findings. It is possible
that medical conditions associated with autism give rise to their own structural
brain abnormalities, for reasons unrelated to autism. Holttum et al.,.
(1992) report that matching between autistic and control groups has been
inadequate for variables such as age, IQ, gender, and socioeconomic status,
which are likely to have a significant impact on the structure of the central
nervous system. In fact, control groups have often been selected from radiologic
files of individuals scanned for other neurologic complaints, but judged
by a neuroradiologist to have a normal scan. Rutter argues that it is unlikely
that a unilateral localized lesion could give rise to the cognitive and
behavior deficits in autism. He hypothesizes that autism derives from a
system abnormality. Therefore, any abnormalities revealed by neuroimaging
may represent regions downstream from the basic underlying affected part
of the brain.
Neuropathology of Autism
In recent years, it has been possible to perform quantitative histologic
examination of autistic brains. Autopsy studies provide much greater resolution
than neuroimaging. These studies have two main shortcomings. First, there
is an inability to control for incidental factors that can affect the structure
of the brain, such as seizures, mental retardation, and psychotropic medications.
It is possible that the brain abnormalities identified in autopsy studies
are associated not with autism itself, but with one of these incidental
factors. A second limitation is the number of subjects studied. If autism
has multiple etiologies, there is limited significance in a single pathological
study. The results obtained from these studies could be little more than
artifacts inherent in a small data base. Nevertheless, microscopic abnormalities
noted to date have been consistent in type and location. Therefore, it
is very likely that the neuropathologic data will provide insight into
the organic nature of autism.
In 1985, Bauman and Kemper reported anatomic abnormalities in the brain
of a 29-year-old man with well-documented autism. He was treated with antipsychotic
and anticonvulsant medications. It was not known if he had been tested
for any specific etiology of autism. In this study, serial sections of
the brain were compared with those of an identically processed age- and
sex-matched control. Since that report in 1985, five additional cases of
patients with well-documented autism have been studied using the same methodology
(Bauman and Kemper, 1994). The patients range in age from 9 to 29 years.
Five of the autistic individuals were mentally deficient. One twelve-year-old
boy was of average intelligence. Four of the six patients studied had a
seizure disorder and were treated with anticonvulsant medications. Nevertheless,
anatomic findings in all six cases are similar.
All of the brains were well-developed and showed no gross abnormalities.
Gyral configuration and myelination appeared normal. Multiple areas of
the cerebral cortex were examined, and no abnormalities of cellular structure,
lamination, or number were found. Microscopic study of the thalamus, hypothalamus,
basal ganglia, and basal forebrain found no differences compared to the
controls. Subtle alterations in the size of neurons and the complexity
of their processes were confined to the limbic system and cerebellum. In
the limbic system, the hippocampal complex, subiculum, entorhinal cortex,
amygdala, mammillary body, anterior cingulate gyrus, and septum are connected
by neuronal circuits. In comparison with the brains of control subjects,
the autistic brains showed reduced neuronal cell size and increased cell-packing
density in these areas. Bauman and Kemper also observed that CA1 and CA4
pyramidal cells in the hippocampus showed decreased complexity and extent
of dendritic arbors. The authors state that these features are characteristic
of an immature brain. Therefore, there may be a curtailment of normal development
in these structures in autism.
In the cerebellum, there was a marked decrease in the number of Purkinje
cells and granule cells throughout the cerebellar hemispheres. There was
no evidence of gliosis, suggesting that the lesion was acquired early in
development. The anterior lobe and the vermis were largely spared. The
most significant cell decrease was found in the posterior inferior neocerbellar
cortex and adjacent archicerebellar cortex. Atrophy of the neocerebellar
cortex was noted in the biventer, gracile, tonsil, and inferior semilunar
lobules. Examination also revealed abnormalities in the emboliform, fastigeal,
and globose nuclei in the roof of the cerebellum. These abnormalities appeared
to differ with the age of the patient. In the three older autistic brains,
these nuclei are characterized by small pale neurons, which are decreased
in number. However, in the younger brains, the neurons in these nuclei
are enlarged and are present in adequate numbers. Coincident with this
finding was the observation that neurons in the inferior olivary nuclei
of the brainstem failed to show evidence of the expected atrophy that is
seen following perinatal or postnatal Purkinje cell loss. The olivary neurons
appeared to differ in size and in number with the age of the patient, showing
the same features as mentioned above.
The hypothesis that emerged from these findings was that the ontogeny
of cerebellar circuitry is disrupted by the early loss of Purkinje cells.
The enlarged neurons in the deep cerebellar nuclei and inferior olivary
nucleus may represent the maintenance of a nonfunctional fetal circuit
pattern. With maturation into adult life, the retained immature circuit
is lost, and it is not replaced by adult circuitry. Therefore, the normal
circuitry of the cerebellum does not develop, and the deep cerebellar nuclei
and olivary nucleus show a reduction in cell size and number.
Interpreting the Neuropathologic Observations
The objective of this paper is to identify a link between neuroanatomic
defect and autistic symptomatology. Studies to date indicate abnormalities
in both the limbic system and the cerebellum of the autistic person.
Significance of Limbic System Abnormalities
Limbic structures of the forebrain include the limbic association cortex,
the hippocampal formation, and the amygdaloid complex. The limbic association
cortex includes the cingulate gyrus, parahippocampal gyrus, and other regions.
It receives information from the higher order sensory areas, and conveys
this information to the hippocampal formation and the amygdala.
The hippocampal formation consists of the subiculum, the hippocampus
proper, and the dentate gyrus. Information flow through the hippocampal
formation is unidirectional. First, the limbic association cortex, especially
the cingulate gyrus, projects to the entorhinal cortex, which then relays
information to the dentate gyrus. Then the dentate gyrus projects to the
hippocampus, which connects to the subiculum. Finally, the subiculum then
sends input back to the entorhinal cortex. Nearly all efferent projections
from the hippocampal formation emerge from the subiculum. Fibers from the
subiculum terminate in the mammillary body, which projects to the anterior
thalamic nuclei. In turn, these nuclei project back to the cingulate gyrus.
The subiculum also projects to the septal nuclei. This projection is part
of a system of connections thought to be important in the expression of
emotions, such as the body’s response to pleasure and pain (Roberts, 1992).
The amygdaloid complex can be grouped into three regions: the central
nucleus, the medial nucleus, and the basolateral nucleus. Like the hippocampal
formation, the central and medial nuclei of the amygdala are related to
the limbic neocortex. In addition, the central and medial amygdaloid nuclei
project to the reticulate core of the basal forebrain and brainstem. The
basolateral nucleus is reciprocally connected with the cerebral cortex,
receiving input from both higher order sensory areas and from association
cortical areas. Therefore, the basolateral division may serve to attach
emotional significance to a stimulus. Because of these closely interrelated
circuits, abnormalities in the forebrain may disrupt both the circuitry
and function of the amygdala and the hippocampal formation, the limbic
and sensory association cortical areas, and their relationship to the reticulate
core of the brain (Bauman and Kemper, 1994).
Experimental Lesion Studies
Patterns of abnormal behavior in autism have been compared with
the severe disturbances observed in non-human primates that have undergone
neonatal removals of the limbic system, specifically the amygdala and hippocampus.
These animals failed to develop normal relationships, displayed blank facial
expression and poor body language, showed memory deficits, and exhibited
locomotor stereotypes such as twirling in circles and doing somersaults
(Bachevalier, 1991). Like autistic children, the experimental monkeys exhibited
considerable variability of specific symptoms. Bachevalier also bilaterally
lesioned the amygdala in infant monkeys, and found that with maturation,
these animals exhibited behavior similar to high-functioning autistic children
(Bachevalier and Merjanian, 1994). These observations are consistent with
the idea that variability in the extent of damage to Region X, in this
case portions of the limbic system, may reflect the variation in autistic
symptoms. Bachevalier acknowledges that the brain lesions inflicted to
the infant monkeys are different from the neuropathology found in the medial
temporal lobe structures. However, she believes that the behavioral similarities
between autistic children and these monkeys are close enough to encourage
future study. These monkeys may serve as an animal model of infantile autism.
Animal research has revealed that insults to the developing brain result
in widespread reorganization of cortical architecture and connectivity.
Restructuring connections may have an adverse effect on the organization
of the brain, by stimulating the development of alternative neural pathways
that do not work as well. Thus, in the developmental context, there is
no reason to assume that specific cognitive deficits may be correlated
with specific neuroanatomic structures in a static fashion (Berstein and
Waber, 1990). Damage to the developing brain should result in vastly different
cognitive and behavioral sequelae that damage to a mature brain. Adult
damage studies are useful since they reveal what kinds of behaviors are
mediated by particular structures. Damage to other regions found to be
abnormal in the autistic brain have elucidated the roles of specific limbic
structures. Bilateral lesions to the amygdala in adult monkeys have caused
behavioral abnormalities (reported in Bauman and Kemper, 1994). These monkeys
withdrew from social interactions, showed no fear of aversive stimuli,
examined objects compulsively, and demonstrated a diminished ability to
attach meaning to a specific situations after prior experience. Like autistic
children, these monkeys were unable to adapt to new environmental settings.
Murray and Mishkin (1985) report that monkeys with bilateral amygdalectomies
show severe impairment in crossmodal associations. This finding suggests
that abnormal circuitry in the amygdala could cause an impairment in generalizing
information from one experience to another. Many autistic individuals demonstrate
this problem.
Damage to the temporal lobes in adult animals has also produced behaviors
that resemble those seen in autism (Damasio and Maurer, 1978). Kluver and
Bucy (1939) performed bilateral surgical ablation of the medial temporal
lobes in macaques, producing purposeless hyperactivity, impaired social
interaction, hyperexploratory behavior, and the inability to recognize
or remember the meaning of familiar objects. Bilateral hippocampal lesions
to rats resulted in hyperactivity, sterotypic motor behavior, and a disordered
response to novel stimuli (Kimbel, 1963).
Structures in the medial temporal lobe are critical to normal memory
functions. These structures include the amygdaloid complex, the hippocampal
formation, and the surrounding cortical areas. The role of the medial temporal
lobe in memory has been illustrated by examination of amnesic patients
and nonhuman primates rendered amnesic. For example, H.M. had the medial
portion of the temporal lobes removed bilaterally for the relief of epilepsy.
Despite the preservation of intelligence, perception, and language, H.M.
could no longer consolidate short-term memory into long-term memory. However,
amnesic patients like H.M. can learn certain motor or procedural skills.
H.M. performed a mirror tracing task with increasing accuracy, though he
denied trying the task before. This observation and others led to the proposal
of two different neural systems for memory: habit or procedura memory,
and representational or declarative memory. Habit memory is preserved in
amnesic patients, and it is thought to be mediated by stimulus-response
connections rather than by conscious memory. Habit memory includes the
acquisition of skills and habits, certain kinds of conditioning, and the
phenomenon of priming. Representational memory involves recognition, retention,
and recall of information. Representational memory is accessible to conscious
awareness, and it enables information to be integrated for higher-order
cognition. These two systems are thought to be anatomically separate. Habit
memory may be subserved by a “cortico-striatal system,” while representational
memory may be subserved by “corticolimbic system.” In autism and amnesia,
the neurobiologic substrate for representational memory appears to be abnormal.
The substrate for habit memory, however, seems to be spared.
Why do autistic children suffer from impairments not seen in amnesic
adults? This difference is likely to be due to the fact that damage has
occurred during different periods of development (Bachevalier and Merjanian,
1994). The medial temporal lobe memory system performs a critical role
beginning at the time of learning, since it encodes representations into
long-term memory (Squire and Zola-Morgan, 1991). The hippocampus and related
structures may “bind together” ordinarily unrelated events or stimulus
features that have been processed by distinct cortical sites. This system
is crucial for the acquisition of new information. Therefore, early memory
loss would cause severe damage to the development of cognitive abilities.
Unlike amnesic adults, “amnesic infants” have never had the ability to
form long-term memories. Unable to acquire and integrate information from
novel stimuli, the autistic child would suffer severe deficits in cognition,
social interaction, language. The preservation of the habit memory system
accounts for many characteristics of the autistic child. It is consistent
with Kanner’s observations of an “anxiously obsessive desire for the preservation
of sameness,” “excellent rote memory,” and “limitation in the variety of
spontaneous activity” in autism. The preservation of the habit memory system
might even account for the acquisition of skills by autistic savants.
Significance of Cerebellar Abnormalities
Courchesne and colleagues (1988) reported reported selective hypoplasia
of cerebellar vermal lobules VI and VII on midsagittal MR images. Courchesne
hypothesized that damage to the cerebellum may cause the cognitive and
behavioral manifestations of autism. This study has several methodologic
limitations, including the lack of matching between autistic and control
groups on age, gender, IQ, the selection of controls from radiologic files
of individuals scanned for other neurologic complaints, and the lack of
screening for fragile X syndrome, which is associated with cerebellar hypoplasia.
It was initially assumed that these findings were consistent with the neuropathologic
changes. On closer inspection, however, it is clear that the histologic
abnormalities are not equivalent in nature or location to the MRI abnormalities.
The MRI studies emphasize a reduction in the area of the neocerebellar
vermis, while the autopsy studies indicate that cell loss is greatest in
the lateral inferior regions of the cerebellar hemispheres (Holttum et
al., 1992). Varying degrees of cell loss were reported throughout the cerebellum,
primarily in the posterior inferior neocerebellar cortex and adjacent archicerebellar
cortex. There was cell loss in the vermis, but there was no selective hypoplasia
of lobules VI and VII.
Several other imaging studies failed to find selective hypoplasia of
vermal lobules VI and VII in autism (Ritvo and Garber, 1988; Holttum et
al.., 1992; Kleiman et al.., 1992; Piven et al.., 1992). In 1994, Courchesne
et al. reported two different subtypes of autistic patients: a subgroup
with hypoplasia and a subgroup with hyperplasia. There was a large degree
of overlap between these two groups. Courchesne et al. suggest that the
negative results of other imaging studies may have resulted from the inclusion
of a small sample of autistic subjects with hypoplasia and hyperplasia.
Averaging the values would result in failure to detect abnormalities. Further
research is necessary to address this issue.
The Cerebellum and Higher Cortical Functions
The role of the cerebellum in motor control is well established,
yet the possibility that the cerebellum participates in higher cortical
function has been proposed by Leiner et al. (1986). These authors focus
on the role of the dentate nucleus, a region of the cerebellum that is
prominent in human brain development in comparison with the same nucleus
in lower primates and subprimate species. Leiner maintains that the connections
between the dentate nucleus and the frontal cortex enable the human cerebellum
to modulate such processes as language, memory, and emotional states, as
well as motor-sensory processes. Evidence from animal investigations indicates
that there are direct projections of the cerebellar fastigial nuclei to
the septal region, hippocampus, and amygdala (Heath and Harper, 1974; Heath
et al., 1978). Heath and fellow investigators believe that the cerebellum
serves an important role in the modulation of emotion and higher functions.
Heath reports that stimulation of the cerebellum has been used to treat
patients with seizures, depression, anger, and aggression. Like persons
with infantile autism, patients with acquired lesions to neocerebellar
structures are impaired in their ability to shift direction of attentional
focus (Courchesne et al., 1994).
An examination of the cerebellar connections to cortical association
areas, in the parietal, temporal, and frontal lobes, provides an explanation
for the role of the cerebellum in higher brain function. Cortical association
areas convey information to the cerebellum by projecting in the pons, which
then relays information to the cerebellar cortex. From the cerebellar cortex,
information is projected to the dentate nucleus, which in turn sends its
axons to the thalamus, via the red nucleus. The feedback loop is completed
by thalamic projections to the cortical association areas. Anatomical studies
also reveal the existence of pathways linking the cerebellum with regions
of the limbic system. The cingulate gyrus, hypothalamus, mammillary bodies,
and the posterior parahippocampal region have been shown to project to
the pons (Schmahmann, 1994). Schmahmann proposes that these limbic connections
might underlie a cerebellar involvement in certain aspects of memory and
learning. The precise mechanism of cerebellar modulation of higher function
is unknown. It is possible that information from the cortical association
areas and the limbic system undergoes modulation in the cerebellum (Schmahmann,
1994). Schmahmann suggests, “In the same way as the cerebellum regulates
the rate, force, rhythm, and accuracy of movements, so it may regulate
the speed, capacity, consistency, and appropriateness of mental or cognitive
processes.” He speculates that the cerebellar abnormalities in autism may
represent damage to part of the neural circuitry that underlies behaviors
such as language, societal interaction, and emotion. Thus, cerebellar lesions
may cause disturbances in emotion, behavior, and learning. Future studies
should address the effect of comparable lesions during development.
Discussion
Neuropathologic studies of autism identify abnormalities in regions
that may be critiocial interaction, language, and memory. Bauman and Kemper
maintain that these defects are acquired early in development and are be
related to the difficulties in social interaction, language, and learning.
There is scant evidence, however, to support the hypothesis of a well localized
“Region X” that results in autism when insulted. Postmortem studies have
been conducted on subjects afflicted with other disorders, in addition
to autistic behavior, such as grand mal seizures. Virtually all of these
subjects had been treated long term with a variety of medications. Obviously,
it is difficult to identify the specific loci of anatomic pathology in
autism in subjects afflicted with a number of diseases. In fact, it is
possible that autopsy studies have revealed brain abnormalities characteristic
of mental deficiency in general, rather than the specific diagnostic entity
called autism. In future studies, case histories must be matched to modern
neuropathological and neuroimaging techniques.
Due to the limited availability of autopsy material, researchers will
continue to rely on neuroimaging studies. The development of increasingly
advanced brain imaging techniques will facilitate the search for “Region
X,” which will more likely be found to be “Regions X.” For example, functional
neuroimaging of memory function in autistic persons may reveal a relationship
between the clinical symptoms and both limbic and cerebellar pathology.
It must be recognized, however, that autism is a clinical presentation,
a syndrome of findings, that probably occurs as a consequence of a number
of different diseases.
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To the Table of Contents
Early
and Late Onset as Subdivisions of Alzheimer's Disease
Elizabeth Kensinger
Alzheimer’s Disease is the leading cause of senile dementia. At one
time, all forms of Alzheimer’s Disease were lumped into one category. More
recent research reveals the possibility of subtypes of the disease, manifesting
themselves at different points in an individual’s lifetime. Early onset
and late onset Alzheimer’s differ in terms of their symptomatic, biological,
genetic, neurophysiological and neurological characteristics.
Symptomatic Differences
Although research on symptomatic differences in Alzheimer's Disease
has been conducted since the 1970’s, the bulk of the research has occurred
since the 1980's in response to a 1983 article by Seltzer and Sherwin.
The article posited the question of whether early and late onset Alzheimer’s
were “one entity or two.” The authors discussed characteristics of the
disease which seemed to vary depending on the age of onset. Notable differences
between the groups led Seltzer and Sherwin to conclude that early onset
(classified as patients whose symptoms arose before the age of 65) was
a different type of Alzheimer’s than late onset.
A crucial difference between early and late onset patients which Seltzer
and Sherwin studied was language dysfunction. Early onset was associated
with more language deficits. Early onset patients had more cases of aphasia
(the loss or impairment of ability to use or understand speech) than the
late onset patients. Filley et al. (1986) also found increased language
impairment in early onset patients (those under 65) and found decreased
visuoconstructional functioning, as determined by increased errors in judging
spatial-relations in drawings, in late onset patients. This double dissociation
(early onset with worse language impairment; late onset with worse spatial
relations) suggests two separate subtypes of the disease. The groups of
patients perform at opposite levels within each area; therefore, the relative
impairment of their functions must be different.
Previously, researchers had hypothesized that early onset might simply
be an accelerated form of Alzheimer’s Disease. However, this dissociation
shows that the damage in early onset is not always more severe; in the
case of visuoconstructional functioning, the early onset patients actually
have retained more ability than the late onset patients. Therefore, early
onset may not be merely a more malignant type of Alzheimer’s. Rather, the
two subtypes differ in the areas of impairment. Since language skills are
associated with the functioning of the left hemisphere and visuoconstructional
skills with the right, this finding also supports Seltzer and Sherwin’s
assertion of selective hemispheric vulnerability, with early onset associated
with a left hemisphere vulnerability and late onset more influenced by
right hemisphere damage.
Jacobs et al (1994) also found notable differences in the areas of
impairment between the early and late onset patients. Once again, 65 years
was the cut-off point for early onset. The Modified Mini-Mental State Examination
(MMMS) was used to test the two groups of patients. This test is divided
into two different sections: attention-related tasks and recall/naming
tasks. The attentional tests consist of repeating digits forwards and backwards,
mental calculations, and sentence repetition. The recall/naming tasks involve
item recall, where a number of items are shown to the patient who must
then recall what he/she has just seen. Other recall/naming tasks require
the patient to recall the current and past four presidents. Confrontation
naming of items is another task.
Early onset patients performed worse than late onset patients on the
attention-related parts of the exam, while late onset subjects scored worse
than early onset patients on recall/naming sections. Jacobs et al. therefore
established another double dissociation between the early and late onset
patients. Overall, the scores on the MMMS were similar; thus, the impairment
of the early and late onset patients was equal. The early onset patients
obtained an average score of 38.00, while the late onset patients had a
comparable score of 38.30. The differences between the groups were in the
areas of impaired and preserved abilities. On the attention sections, the
late onset patients scored 21, while the early onset only scored 18. On
the recall/naming sections, the late onset patients’ scores dropped to
15, while the early onset patients had an average score of 17.8 (Jacobs
et al., 1994).
Since the early onset patients did not show a greater general dementia,
these findings imply that early onset differs in the areas of the brain
which are targeted, rather than only in the rate of progression. The early
onset patients appear to be hit harder in attention-related areas of memory,
while the late onset patients appear to have more damage in areas related
to recall and recognition.
Evidence pointing towards subtypes is substantial. Early and late onset
cases seem to differ in rate of progression and extent of language dysfunction
or visuoconstructional impairment. Additional evidence for the subtypes
comes from neurological and neurophysiological studies which confirm that
early and late onset Alzheimer’s have different neurochemical characteristics.
Neurological, Neurophysiological and Biological Characteristics
All Alzheimer’s patients share some common neurological damage.
The distinctive features within the brain (as determined by autopsy) are
neocortical senile plaques and neurofibrillary tangles (Rosser et al.,
1984). Alzheimer’s patients tend to have a decreased volume of gray matter
when compared to controls of equal age (Shear et al., 1995). The Alzheimer’s
brain also has numerous neurochemical deficiencies. A reduction in somatostatin
may be responsible for the uncontrolled growth-promoting processes which
commonly occur in the cortex of those afflicted with Alzheimer’s (Selkoe,
1992). Somatostatin acts to inhibit the production of the growth hormone.
Therefore, whensomatostatin levels are reduced, the hormone is not effectively
impeded, leading to unregulated growth (Palmer et al., 1988). Serotonin
levels are also reduced, which has been associated with the greater aggression
seen in some of the patients (Palmer et al., 1988). One other neurotransmitter
which is markedly reduced is acetylcholine. Acetylcholine reduction has
been noted even within the first year of symptom onset (Katzman, 1986).
All of these neurological abnormalities are present in both early and late
onset patients.
However, patients with earlier onset tend to have more widespread cholinergic
deficits (the cholinergic system consists of the nerves which receive messages
via the neurotransmitter acetylcholine), whereas in late onset patients,
the cholinergic deficits are confined to the temporal lobe and hippocampus
(Chui et al., 1985). Early onset patients also tend to have more abnormalities
of noradrenaline and other neurotransmitters. Such observations led Rosser
and colleagues (1984) to conclude that Alzheimer’s disease in younger patients
may be a distinct form of dementia which differs from the later onset dementia.
This assertion has been backed by further studies. Shear et al. (1995)
discovered more volumetric abnormalities with early onset patients and
found that these people tended to have more rapid neuronal deterioration
than the older patients. In addition to these findings, studies by Prohovnik
and colleagues (1989) showed a greater loss of gray matter in early onset
patients.
Biological factors may also contribute to age of onset. All Alzheimer’s
patients have biological changes occurring in cells outside of the central
nervous system. Many of these changes alter the cell membrane structure
or function (Zubenko et al., 1988). However, the types of biological changes
which occur depend on the age of onset. Zubenko et al. (1988) found that
patients with increased platelet membrane fluidity tended to have an earlier
age of onset. These patients showed a more rapid decline. One other interesting
discovery was that these patients were more likely to have a family history
of dementia.
Genetic Differences
Early onset is characteristic of the familial form of Alzheimer’s
(Heston et al., 1981). First degree relatives (children) of Alzheimer’s
patients have an increased risk of developing the disease themselves (Nalbantoglu
et al., 1990). Many Alzheimer’s cases have been linked to a mutation of
the amyloid precursor protein gene on chromosome 21. Early onset has been
shown to be connected with another mutation on chromosome 21. No such marker
has been associated with late onset Alzheimer’s (Nalbantoglu et al., 1990).
Breitner et al.(1986) found that Familial Alzheimer’s is a rare form
of the disease, highly correlated with early onset. In addition, he showed
that most of the non-pedigree Alzheimer’s cases had a much later onset.
In further studies he proposed that genetic factors could also be responsible
for late onset Alzheimer’s (Breitner et al., 1988). In his 1992 article,
he suggested that late onset Alzheimer’s could be caused by autosomal dominant
inheritance just as with the early onset form, but with reduced penetrance,
so not all individuals with the disease develop symptoms. While early onset
Alzheimer’s may not be the only form of inherited Alzheimer’s, early onset
Familial Alzheimer’s can still be separated from inherited late onset Alzheimer’s
by the specific locations of genetic mutations and the extent of their
penetrance.
Future Implications
Further exploration of whether early and late onset are different
subdivisions could suggest important differences in the treatment of patients
based on their type of Alzheimer’s. Because of the differences in neurological
damage, it seems likely that the disease may stem from different causes
or affect different areas of the brain. Especially in the cases of Familial
Alzheimer’s, different genetic defects may be responsible for early and
late onset. More concrete divisions of these two areas may facilitate future
research for treatments such as gene therapy.
Pharmacological approaches to Alzheimer’s Disease focus on altering
the specific biochemical pathways of the disease and trying to arrest symptoms
at different stages of the disease by lessening the cholinergic deficits
(Selkoe, 1992). Since early and late onset have different areas of selectivity
and different amounts of cholinergic deficits, it seems likely that different
treatments should be utilized in the separate cases.
Transmitter replacement therapy may also help lessen the widespread
neuronal pathology in the intermediate and late stages of the disease by
promoting neurotransmitter production. This therapy could be used to affect
neurons that release a number of neurotransmitters, including acetylcholine,
norepinephrine, serotonin, somatostatin, corticotropin releasing factor,
and glutamate (Selkoe, 1992). Davis and Mohs (1982) found that patients
showed memory enhancement when given acetylcholine precursors to help increase
the production of acetylcholine, thus improving the cholinergic system.
Palmer et al. (1988) determined that patients with behavioral problems
have damage to the serotonergic system. The loss of serotonin may cause
the aggressive behavior displayed by these patients. Additional research
could determine other neurotransmitters which are responsible for the different
symptoms or stages of Alzheimer’s and whether different ones are involved
in early and late onset. This knowledge could help determine which treatments,
or therapies, to try first to alleviate some of the symptoms.
Because of the complex pattern of neurotransmitter and neuropeptide
abnormalities in early onset patients, it would be prudent to look for
sources of treatment other than simple cholinergic replacement (Filley
et al., 1986). While such treatment might work with the later onset patients,
success would be more difficult with the early onset patients since their
neurological damage is widespread, affecting more areas of the brain. Their
symptoms could also be difficult to appease by transmitter therapy, since
there are so many neurotransmitter reductions responsible for their cognitive
deficits.
A more concrete subdivision of early and late onset could recommend
certain treatments above others. Some treatments may be more damaging to
early onset patients. Antidepressants are helpful in cases with clinical
depression. While early onset patients are most likely to suffer from depression,
they also tend to have the most widespread cholinergic damage (Selkoe,
1992). Antidepressants are known to accelerate cholinergic damage and to
enhance the intellectual symptoms of the disease. Therefore, while the
natural assumption may be to administer antidepressants in the early onset
cases, it may be prudent to refrain from giving these patients antidepressants
until their depression demands that such a step be taken. Rovner (1992)
found that many behavioral difficulties, including depression, may be managed
more effectively by interventions with the caretakers to help the patient,
rather than by management with medication.
Rovner (1992) also found that patients with brain damage tend to be
highly sensitive to some of the side effects of medicines . Since early
onset patients have more brain damage than late onset patients, they may
be affected by more of the adverse side effects of medications. By understanding
the different reactions patients could have to medications, more educated
decisions can be made about the methods of treatment which should be used.
Until the symptoms of early onset patients reach incredibly severe states,
it may be more beneficial to try family therapy techniques or other non-medical
strategies for treatment.
On the other hand, some medical treatments may benefit early onset
patients more than late onset. For example, memory problems in early onset
patients are heightened by alertness and attention deficiencies. Specific
neurotransmitters are involved with attention, mainly acetylcholine and
histamine (Oken et al., 1994). Therefore, induced production of these neurotransmitters
by medication or transmitter replacement therapy could alleviate some of
the attentional problems of the early onset patients and help their memory
retrieval.
Another interesting method which may aid in memory enhancement is a
spaced-retrieval technique (McKitrick, 1992). While this method does not
repair any of the physical damage caused by Alzheimer’s, researchers are
optimistic that the technique may allow better memory retention for at
least some Alzheimer’s patients. Once again, the ability to further subtype
the groups of patients into different classes or stages of the disease
could allow better ideas about which groups of people would benefit from
such training and at what stages of the disease the technique should be
attempted.
In addition to determining which medications or therapies to try, subtyping
Alzheimer’s may also help to establish different progression rates or different
courses of decline, depending on the particular division of the disease.
Looking at the overall course of the disease may eventually help preventative
measures to be taken. If the progression of the disease can be mapped,
either by symptoms present or by the biological, neurological, or neurophysiological
damage present, it may be possible to ameliorate some of the symptoms,
or to lessen the physical damage, by taking preventative measures or attempting
transmitter therapy.
Weiner (1991) suggested that pharmacological treatments are not successful
because the patients have already had Alzheimer’s Disease for a long period
of time. With a clearer map of when symptoms might arise, warning signs
will be recognized, allowing treatment before the symptoms have become
debilitating.
It has been discovered that the rate of decline, at least in cases
of inherited Alzheimer’s, is severely lessened if the age of onset is increased
by even just five years. Therefore, the morbidity from Alzheimer’s could
be reduced if new strategies succeed in determining those people who are
predisposed to the disease and if their development of the disease could
be delayed by even just a few years (Breitner, 1992).
In cases of Familial Alzheimer’s, it would be helpful to determine
which family members are most at risk. If the people at risk could be pinpointed
at an earlier age, it might be possible to treat some of the symptoms before
they have completely manifested themselves. It may be possible to isolate
certain genes, or to determine certain precursor symptoms, which could
determine people who are most likely to develop Alzheimer’s. If this information
were available, it might be possible to conduct gene therapy or to determine
ways of preventing further neurological damage.
A better map of progression could help prepare family members to watch
for future symptoms and certain warning signs. It could also aid in determining
when a nursing home environment should be looked into as an option rather
than continuing home care. Heyman et al. (1987) found that results of tests
for language disability, memory loss, and other cognitive functions served
as good predictors for when the patients would be admitted into nursing
homes. Therefore, even at this early stage, when the data is still fairly
inconsistent, some form of progression map has been made with enough accuracy
to predict the future course of the disease.
A more definitive map of the progression for different subtypes could
also lead to a more accurate assessment of the success of certain treatments.
For example, some patients who may seem to be responding positively to
a certain treatment may actually have a course of the disease which progresses
more slowly. In such cases, their seeming response to the treatment may
actually be from the effect of their more benign form of the disease (Mayeux
et al., 1985).
Conclusion
Alzheimer’s Disease confronts us with many unanswered questions,
including the question of the validity of subdivisions within the disease.
Future investigations should continue to focus on symptomatic differences
as well as the specific neurological or biological deficits present in
the two groups of patients. In the near future researchers will likely
isolate specific physiological differences for early and late onset Alzheimer’s.
These differences could be important clues for determining which medical
or therapeutic treatments to administer. Further research could also help
in finding preventative measures to stop neurological damage from occurring
or to lesson some of the symptoms. Symptoms may even be caused by different
neurological deficits depending on the subtype of the disease. Increased
understanding about these types of differences would allow the administration
of different preventative medicines.
Additional tests may allow better estimates of rate of decline. If
the two subtypes do follow different patterns of decline or have symptoms
appearing at different points, understanding the different progressions
could allow better predictions about rate of deterioration. Clearer maps
of symptomatic progression and rate of decline for each subdivision would
increase the opportunity for preventative medicine and help recommend when
medical treatments should be administered to be most beneficial.
REFERENCES
Breitner, J.C.S. (1992). Clinical genetics and genetic counseling
in Alzheimer’s Disease. Journal of Geriatric Psychiatry 25(2): 229-245.
Breitner, J.C.S. and Magruder-Habib, K.M. (1989). Criteria for onset
critically influence the estimation of familial risk in Alzheimer’s Disease.
Genetic Epidemiology 6: 663-669.
Breitner, J.C.S., Murphy, E.A., and Folstein, M.F. (1986). Familial
aggregation in Alzheimer dementia - II. Clinical genetic implications of
age-dependent onset. Journal of Psychiatry 20: 45-55.
Breitner, J.C.S., Silverman, J.M., Mohs, R.C., and Davis, K.L. (1988).
Familial aggregation in Alzheimer’s Disease: Comparison of risk among relatives
of early- and late-onset cases, and among male and female relatives in
successive generations. Neurology 38: 207-212.
Chui, H.C., Teng, E.L., Henderson, V.W., and Moy, A.Y. (1985). Clinical
subtypes of dementia of the Alzheimer type. Neurology 35: 1544-1550.
Filley, C.M., Kelly, J., and Heaton, R.K. (1986). Neuropsychologic
features of early- and late-onset Alzheimer’s Disease. Archives of Neurology
43: 574-576.
Heston, L.L., Mastri, A.R., Anderson, V.E., and White, J. (1981). Dementia
of the Alzheimer type: Clinical genetics, natural history and associated
conditions. Arch Gen Psychiatry 38: 1085-1090.
Jacobs, D., Sano, M., Marder, K., Bell, K., Bylsma, F., Lafleche, G.,
Albert, M., Brandt, J., and Stern, Y. (1994). Age at onset of Alzheimer’s
Disease: Relation to pattern of cognitive dysfunction and rate of decline.
Neurology 44: 1215-1220..
Elizabeth Kensinger '98 (ekensing@fas.harvard.edu) is a resident
of Lowell House. She is concentrating in Psychology and Biology through
the MBB program.
I'm
Alive! Fundamental Strategies for Solving the Puzzle of Human Consciousness
Sameer Chopra
Consciousness is the essence of being human. It has allowed us to create
brilliant works of artistry and make extraordinary progress in science
and technology, yet remains as mysterious as ever. Its subjective qualities
continually fascinate us, while its elusiveness has presented one of the
greatest challenges for centuries of scientists and philosophers alike.
We want to know how it is we feel and interpret, and how can we “get into
someone else’s head;” we often wonder if animals have consciousness, or
what it would be like if one were a bat, frog, or hawk. The primary question
resounds: from what does human consciousness arise? Its association through
modern science with neural activity has already answered the “where.”2
In doing so, however, it has prompted not the first steps toward greater
understanding but rather a long leap into the realm of confusion and conjecture.
Descartes wrestled with the mind-body problem, trying to explain how
something non-physical could exist inside or be produced by a completely
physical, biological entity, only to generate mental frustration: “So serious
are the doubts into which I have been thrown... that I can neither put
them out of my mind nor see any way of resolving them. It feels as if I
have falled unexpectedly into a deep whirlpool which tumbles me around
so that I can neither stand on the bottom nor swim up to the top” (Decartes,
1986). He eventually presented the hypothesis of dualism, suggesting that
everything in the universe can fit into two categories, physical or mental,
and that these exist semi-independently of one another. The brain belongs
to the physical realm of existence, while consciousness belongs to the
mental; when they meet in space (and perhaps time?), there is what Humphrey
calls a “handshake across a metaphysical divide”.
Other philosophers have been reluctant to support dualism and instead
acknowledge monism, the idea mind and brain are of the same fundamental
material of existence,3 and in some extreme cases, physicalism (or functionalism),
that consciousness and the accompanying neural processes are in fact one
and the same. Searle comments, “we have to abandon dualism and start with
the assumption that consciousness is an ordinary biological phenomenon
comparable with growth, digestion, or the secretion of bile” (1995, 60).
From the general philosophical idea of monism stems the more modern and
scientifically-based idea of neuroreductionism, that consciousness and
mental states alike can be reduced, or broken down, to specific neural
processes or elements of which they are fundamentally composed. This idea
presents a dilemma, however, as modern British philosopher Colin McGinn
aptly noted: “Somehow, we feel, the water of the physical brain is turned
into the wine of consciousness, but we draw a total blank on the nature
of this conversion. Neural transmissions just seem like the wrong kind
of materials with which to bring consciousness into the world... The mind-body
problem is the problem of understanding how the miracle is wrought”(McGinn,
350). Searle corroborated the problem with a question, “How exactly do
neurobiological processes in the brain cause consciousness?” (1995, 60)4
Both suggest that the current search is for a hint as to how subjective
mental states arise from or are equivalent to electrical activity in the
brain.
It seems that the vast array of speculations, ideas, and hypotheses
regarding consciousness stems from the various differences that scientists
have in defining or describing the phenomenon. In their book Wet Mind,
Kosslyn and Koenig cite one of the major impediments to a productive discussion
of consciousness as being a disagreement of what exactly is being discussed
. Unfortunately, “It is impossible to fix the referent of the term - one
cannot point to consciousness the way one can point to a book or even a
brain, so there is no way to resolve disagreements. . . However, people
can be conscious of making a decision, being in love, seeing red, having
a pain in the lower back, and so forth. And they clearly can distinguish
being conscious of these events from not being conscious of them. The fact
of consciousness, should not be in doubt; there is something to be explained,
not merely explained away”(431).
Some suggest that consciousness is simply “the state of being aware
of our thoughts and behaviors” (Bloom, 272). Searle reflected on the very
nature of conscious experience and when it arises: “The enormous variety
of stimuli that affect us - for example, when we taste wine, look at the
sky, smell a rose, listen to a concert - trigger sequences of neurobiological
processes that eventually cause unified, well-ordered, coherent, inner,
subjective states of awareness or sentience” (1995, 60). Chalmers possesses
a similar view, “when you look at this page, you are conscious of it, directly
experiencing the images and words as part of your private, mental life...
At the same time, you may be feeling some emotions and forming some thoughts.
Together such experiences make up consciousness: the subjective, inner
life of the mind”(80). Baars, on the other hand, asserts that consciousness
is knowing or being aware of external “events.” He uses an operational
definition of consciousness that considers people to be conscious of an
event if (1) they can say immediately afterwards that they were conscious
of it and (2) we can independently verify the accuracy of their report
(15). His definition presupposes language and volition (the ability and
inclination to verbally describe a conscious experience) and the use of
metacognition (to become conscious of the conscious experience, in order
to tell of the event). Mountcastle (1975) defined consciousness as awareness
of one’s own physical actions. Language is the vehicle by which thought
and action are made available to conscious awareness. The common thread
to all these definitions of consciousness appears to be either some kind
of inner awareness or personal experience, brought about by our ability
to sense our external environment. It would seem prudent, then, that we
indeed begin with human sensory systems in our attempt to grasp some understanding
of an brain-based entity whose vagueness ironically arises from overdefinition.
And yet, by placing a primary focus on sensory input and processing
we immediately come upon our first major obstacle: the “binding problem.”
Suppose that an individual is walking on a street in a city. He is bombarded
by a number of simultaneous stimuli of the senses, including sights, sounds,
smells, temperature, and the body’s own activity or movement. Each sense,
however, each provides information about a different element of the environment.
If we had additional sensory ability, like the chemical sensitivity of
dogs’ noses, then we would be able to detect different things from our
environment and further enhance our “picture” of the world around us. Additional
information provides better interpretation. Sensory organs, like the eyes,
do not transmit whole images of the visual domain, but rather features,
or different characteristics such as color, contrast, shape, and motion.
These fragmented pieces of information are processed in parallel and sent
via nerves to the brain.
The sensory data from various domains proceed to the thalamus, a relay
station of sorts, that categorizes and sends the information to various
locations in the cerebral cortex on the outer surface of the brain. Each
sense has its own specific relay apparatus in the thalamus, as well as
general location on the cortex. Thus, at each given moment, neurons all
across the cortex are activated, based upon various sensory inputs from
our immediate environment. The “binding problem,” thus, is that we have
no definitive explanation for how these various bits of sensory information
are integrated to form a complete whole, a single human perception or image
of the world around us. To some neurobiologists, this synthesis of sensory
information is what is known as consciousness.
There is no “screen” in the brain. There is no area where sensory information
from various modalities could be integrated to form a complete picture
of the external world. Further, there is no viewer to observe it even if
there were. This has led multiple neurobiologists to explore other non-physical
means of sensory integration. Jacobsen suggested that “perhaps there is
not such an integrative control center, only a sequence of events entrained
by local coupling combined with more widespread coupling between events
resulting in a process which is a large-scale pattern of events in space-time”
(121). This is not as abstract as it sounds, for according to Jacobsen,
“reductionist methodology should, in principle, be able to discover the
couplings between events, and to reduce the large-scale pattern of events
to small-scale patterns” (121).
This general notion of sensory integration in the form of “patterns”
of electrical activity in the brain is not uncommon among modern neurobiologists.
In attempting to formulate a neurobiological model of human consciousness
(which seems entirely plausible given its definition as a synthesis of
sensory input) researchers have focused on neural organization and processing
within the cerebral cortex itself as a starting point. Take the cells of
the visual cortex, for example, which are arranged in columns corresponding
to different elements in a specific part of the visual field. Individual
columns become activated upon proper stimulation by impulses arriving from
the thalamus. The result is essentially a rhythm of electrical activity
coordinated and synchronized by the visual relay station of the thalamus.
Neurobiologists Crick and Koch suggest that herein lies consciousness,
arising from oscillations in the cerebral cortex which become synchronized
as neurons fire 40 times a second; two pieces of information as essentially
bound in time by synchronous neural firing (84).
Llinás proposed a mechanism early in 1995 for sensory integration that
also has to do with this “rhythm” of neuron firing in the cerebral cortex.
He believes that the brain has a scanning system that sweeps across all
areas of the cerebral cortex every 12.5 thousandths of a second. The scan
manifests in a wave of nerve impulses created by the intralaminar nucleus,
a circular grouping of cells in the thalamus. What the electrical scan
does is stimulate all of the synchronized, active cells of the cerebral
cortex which at that instant are recording specific sensory information.
These cells then respond by instantaneously sending signals back to the
thalamus, all of the signals at that precise moment in time together reflecting
the specific pattern of neural activation based on the precise sensory
stimulus.
In the example presented earlier, suppose a moving car is in the visual
field of the man walking down the street in the city. Certain columns of
cells in the visual cortex are being stimulated, and respond to the electrical
scan of the thalamus. As the car moves away and is no longer seen, the
cells stop being stimulated and do not respond to the scan. Hence, Llinás
suggests, all of the responses received by the thalamus for one scan cycle
constitute a single instant of consciousness, with continuity maintained
by the sheer speed of the process. “The data from all the body’s senses
are could come together not in place. . . but in time, the time of the
thalamus’ scanning cycle. Consciousness, by this theory, is the dialogue
between the thalamus and the cerebral cortex, as modulated by the senses”(Blakesee,
1).
Hypothesizing solutions to the binding problem address the synthesis
of sensory information into a complete, internalized mental picture of
the environment, but do not seem to address the very nature of consciousness
as others see it. For those like Chalmers, an answer to the binding problem
is an answer to one of “the easy problems of consciousness ..”(81). The
answers to “easy” questions, suggests Chalmers, elucidate mechanisms of
the cognitive system that are associated with consciousness. The “hard
problem” remains: how can a vast network of electrical impulses within
the brain produce the subjective, personal, indescribable feelings associated
with sensory experience? More than a handful of theorists refer to the
“phenomenon” of being moved by the intense red of a rose, the feeling of
pain upon pinching oneself, or hearing the twang of a harp. Is this inner
mind the real “mystery of consciousness”(Searle, 1995, 60) and not merely
sensory integration?
For many the answer is yes, but the problem is now more difficult than
we imagined. Neuroscience and neuroreductionism provide physical or structural
explanations by virtue of their nature and may not best be suited to explaining
the subjective experience that we seek to understand. Hypothetically, even
if scientists could functionally understand every neural or cognitive process
associated with consciousness, we would still not know how or why these
functions are accompanied by subjective experience. Does that mean we have
no choice but to accept the dualism that Descartes proposed centuries ago?
Not necessarily, according to Searle, who asserts that our greatest impediment
to properly approaching consciousness is our inability to recognize “non-event
causation,” and that cause (brain processes) and effect (consciousness)
do not necessarily have to be two different things. Brain processes could
“cause” consciousness in the sense that consciousness “is itself a feature
of the brain,” similar to the solidity of wood being a feature of wood
that is caused by the behavior of its molecules (60). It is this fine distinction
that allows us to trespass on the great explanatory divide we faced earlier:
that is, how subjective states of sentience arise from physical processes
in the brain. Neurobiology can explain sentience, but not in the direct,
functional way that we had hoped.
Hence, we are left to conclude one of three things: 1) consciousness
is an emergent property of the whole system, a property that could not
be deduced either from the properties of the parts studied in isolation,
or from the properties of parts that could be predicted to come into play
as a result of interactions between the parts5; 2) consciousness itself
is a nonreducible fundamental entity accompanied by constant activity of
the brain’s physical processes; or 3) consciousness is neither an emergent
property nor a fundamental entity,6 but somehow directly caused by neural
elements and processes in a way that is currently beyond our realm of understanding.
I prefer to focus on the first two conclusions. Thefirst notion does not
imply reductionism because emergent properties are not equatable to the
systems or processes from which they arise. Rather, the notion suggests
that there is a specific type of causality that does not involve discrete
events occurring one before the other. The second idea does not assume
dualism, because dualism would assert that consciousness lies outside the
realm of that which is explainable by science. Rather, it asserts that
when existing, fundamental laws cannot explain a common entity, new laws
must be devised to do so. Neither one of our two conclusions, I believe,
necessarily exists exclusively of the other: an emergent property can be
one that is fundamental (explainable by but unable to be reduced a specific
system), and a fundamental entity can be one that (unexpectedly) arises
from a uniquely organized system.
Perhaps the best way to approach this modified view of consciousness
is to organize a framework or set of guidelines for any proposed theory.
Kosslyn and Koenig have done that by presenting a specific set of criteria
for an adequate theory of consciousness in their book, Wet Mind. Kosslyn
and Koenig refer to consciousness as the “phenomology of experience”, and
as I suggested earlier, how easy it is to skirt the issue or “conflate
the information-processing or neural correlates of consciousness with the
phenomenological texture of the experience itself”. They focus on the nature
of the private “feel” of humans without attempting to characterize in detail
the various phenomena. Rather, their consciousness differs from brain states.
They propose the following requirements for a proper theory on consciousness,
as I have summarized and commented upon below:
1) Nonreducability: Consciousness and brain events are completely different
in nature, and that “phenomenological experience cannot be described in
terms of ion flows, synaptic connections, etc.”(432). They provide the
analogy of consciousness being the light produced by a hot filament in
a vacuum. The specific events that cause the light can explain how it physically
arises, but cannot provide an accurate description as to the phenomenon
itself. Similarly, consciousness cannot be equated with a series of events
in the human brain.
This first criterion satisfies my view of consciousness as either being
an emergent property (essentially a feature of a system of neural processes)
, a fundamental entity (inexplicable with existing physical laws or properties),
or both. Nonreducability, however, may be harmful if incorrectly perceived
as upholding the idea of vitalism - that the workings of the human mind
will never yield to the reductionism of modern science. Vitalism is extremist
because it ignores the possibility of discovery: perhaps we are only limited
by what we have already learned, or our present scientific capabilities.
Asserting that consciousness is “nonreducible” to specific events in the
brain does not intrinsically posit that consciousness cannot be understood
in a more fundamental way, as a feature of those very events or an entity
deserving of new fundamental laws altogether.
The light analogy that the authors present is fascinating, (essentially
portraying light as an emergent property that is nonreducible to the physical
effects that create it), but some might argue it in a different way: At
one point in time visible light was largely believed to be non-reducible,
but the advent of the prism clearly revealed that visible light is composed
of electromagnetic waves of different wavelengths. Einstein further theorized
that light is comprised of photons (or packets of energy) moving as electromagnetic
waves - hence, an explanation for light’s seemingly unresolvable dualistic
wave-like and particle-like properties. These advances in our understanding
of light came with time and scientific achievement. Hence, is it advisable
to assume that consciousness cannot be reduced? Well, yes. . . not reduced
to physical events in the brain, that is. Doing so would not be reduction
in the same manner we reduced light (to parts with similar fundamental
characteristics) because neural, electrical activity possesses characteristically
different properties than consciousness and simply cannot comprise the
ingredients to our subjective and afferent inner mind7. The intrinsic nature
of consciousness simply defies reduction to brain activity.
With the purpose of making an even more important clarification, there
is room to even further expand upon the authors’ “light” analogy. Assuming
the supposition that light is fundamental and cannot be reduced would not
have any impact on the admission that there are actually multiple ”causes”
(defined as ways of producing light, the hot filament in a vacuum merely
being one of them). Similarly, the acceptance of consciousness as nonreducible
does not ipso facto exclude the idea that consciousness results or emerges
from some system or process. Because nonreducibility does not deny the
existence of causation, both concepts must be treated separately. In this
way, those who uphold belief in nonreducibility of human consciousness
can ponder whether animals are conscious, the possibility of consciousness
in non-living entities, etc., without facing any sort of inherent contradiction
in ideology. Granting nonreducibility, science’s foremost pursuit should
be to discover the specific relationship between human neurophysiological
activity and human consciousness, (whether be simply associative, that
of emergent property and system, or even that of direct event-related cause
and effect), with the hope that something fundamental about the nature
of the mysterious entity might be revealed in the process.
2) Unique Role of Consciousness: Consciousness can be non-functional,
epiphenomenal. Like steam emerging from a pot of boiling water, consciousness
may simply arise out of brain processes with specific functions. Alternatively,
a theory can assert that consciousness has some definitive function, which
is far more difficult to explain. This is because, as a corollary to point
one, any function of consciousness must not be able to be accomplished
by brain events. From the evolutionary viewpoint as well, it would seem
unclear why consciousness would emerge and be maintained in the event its
functions can easily be carried out by brain processes.
3) Selectivity: Its interesting to note that only particular mental
activities are accompanied by some sort of conscious experience, and a
theory of consciousness should explain why. For example, Kosslyn and Koenig
remind us that we have no idea how we construct images of objects a little
at a time, how we reach an instantaneous decision, or how we identify something
we have seen before. We simply don’t know or aren’t aware of the sequences
of events behind many specific mental outcomes.
4)Association with brain states: We do know for certain that consciousness
arises from activity in the brain, especially after years of research on
changing brain states and the effect on consciousness. Additionally, consciousness
is altered when one takes drugs, and it is a fact that electrical activity
in the brain always precedes an individual’s consciousness of a certain
stimulus. Thus, brain processes are requisite for consciousness although
we cannot equate the explanation of one with the other, or explain why
there is a requisite relationship.
5)Impact on Brain States: This is obviously the most controversial
element of the authors’ consciousness construct. Kosslyn and Koenig reason
that in a theory where consciousness is functional, it would affect behavior
in some way by affecting the brain. Thus brain activity and consciousness
effect each other. They admit that this characteristic is a terribly challenging
one to incorporate into a theory; indeed, although consciousness is not
a physical event (not that we know of), it arises from them and even has
the capability to alter them. Hence the puzzle remains.
Kosslyn presents a speculative theory to demonstrate how a model of
consciousness can comply with five, seemingly stringent and disparate tenets.
Under the “Parity Theory,” as it is known, consciousness essentially serves
as a mechanism for determining whether the brain is functioning properly
or not. The idea arises out of a simple computer metaphor. Computers store
information in bytes, each of which are composed of eight bits. Because
computers use binary code, each bit can signify either a 0 or a 1, and
the eight bits will together identify something in particular, such as
a word or a symbol. Often, however, this will be done with only seven bits,
and the eighth becomes what is known as a “parity bit.” Depending on the
machine, this last bit will be either a 0 or a 1 to make the sum of all
the bits odd or even. By essentially labeling bytes in this way, the computer
can readily detect whether some error has occurred in its storage of information.
By analogy, Kosslyn suggests that consciousness is a parity check, an indicator
of proper neural function. “According to this conception, consciousness
is an interaction among physical energies produced by the brain, which
provides a sign that neural activity in diverse locations is mutually consistent”(436).
The theory seems to present a possible function for consciousness,
but has neither provided a causal mechanism nor stated anything definitive
about the phenomenon and its properties. Kosslyn proceeds to systematically
and logically fill in the blanks. Neural discharges in different brain
areas that process the same stimulus oscillate in phase together at 40
Hz (Gray and Singer, 1989). These oscillations may in fact be responsible
for associating different representations in the various regions of the
brain, (as Crick and Koch, 1995, have suggested as well). For example,
take the visual stimulus of a large, blue automobile moving to the right.
Each of the automobile’s characteristics has a specific representation
in a specific brain area, and if all the stimulated neurons oscillate in
firing in the same way, “this could indicate that representations should
be conjoined in associative memory” (438).
According to Parity Theory, consciousness has the same relation to
these brain events as a chord does to individual notes that are played
on a guitar. Kosslyn finally proposes that “consciousness arises from the
interaction of the electromagnetic rhythms set up in individual brain loci,
but cannot be reduced to the individual brain events any more than a chord
can be reduced to individual notes”(438). Hence, we have satisfied rule
number one. Consciousness also has the unique function of signifying discord,
if certain neural processes do not function together properly. A neural
process cannot monitor another neural process, because some mechanism would
always be needed to check the monitor mechanism. Thus, “consciousness has
a unique role; it performs a function that cannot be replaced by a brain
process”(439).
In terms of selectivity, Kosslyn refers to the speed of the neurological
process as being the deciding factor in whether it will be consciously
detected or not. He bases this assertion on a study that demonstrated that
conscious experiences always lag behind the brain events we presume to
evoke them (Libet ,1987; Libet, 1967). Thus, a delay period is necessary
before each conscious experience, and we are able to experience only those
processes that are relatively slow. In evolutionary terms, functions essential
(such as instantaneous decision-making) to virtually all multicellular
organisms will proceed fast and remain undetected, because of millions
of years of fine-tuning and improvements in efficiency. Those abilities
or neurological processes that are relatively new and unique to humans
are the slower ones that we consciously experience. The brain state simply
has to exist long enough for us to become conscious of the result. As Kosslyn
comments, “we are conscious of the relatively slow processes that coordinate
perceptual, memory, and motor events during reasoning, a relatively recent
development on the evolutionary scale”(440).
The fourth rule requires association with brain states. Indeed, the
theory proposes that consciousness cannot exist without electromagnetic
rhythms in different parts of the brain. Lastly, there must necessarily
be an impact on brain states. Continuing the guitar analogy, a dissonant
chord results from a note being out of tune. The Parity Theory asserts
that dissonance arises if different portions of the brain are not oscillating
compatibly. When this happens, the brain areas that produce the electromagnetic
rhythms are affected and hence they do not process information normally.
Thus, consciousness seems fragmented or distorted. Drugs can have a similar
effect by disrupting rhythms directly, producing abnormal rhythmic interactions.
The most common result is a “reset” or starting over.
Kosslyn’s conception of consciousness is truly unique, for he does
not stop at proposing a solution to the binding problem of sensory integration,
but satisfies his own five criteria for a theory of consciousness by explaining
the phenomenon without neuroreduction or any sort of equation to a neural
state. His theory, however, is not free of problems. First, there is a
point of contradiction between his Parity Theory and one of his five consciousness
criterion. Kosslyn, without using the actual buzzwords, defines consciousness
as a non-reducible emergent property or feature of the system of oscillating
neural firing in the cortex; therefore neural processes effectively cause
consciousness and subjective experience. The implication of the principle
of emergent property, however, is that neural processes cannot be influenced
in any way by the emergent property. This conflicts with Kosslyn’s consciousness
construct (which presents consciousness as having the ability to affect
brain states) and hence his Parity Theory.
Second, there is a complete disregard of sensory experience in his
notion of consciousness. It seems as if he perceives consciousness as simply
being awake or aware of external stimuli or certain, slow-moving thought
patterns. This is problematic because I indicated earlier that most researchers
in the field define the problem of consciousness in terms of the subjective,
inner responses that accompany sensory processing in the brain. The feeling
one gets from hearing a certain piece of music, or eating a piece of chocolate
cannot readily be explained by “Parity Theory,” causally or otherwise.
If the function of consciousness is simply to be a check on normal brain
function, then why do we experience such elaborate, rich and diverse inner
subjective states? Surely, having personal feelings couldn’t have anything
to do with maintaining proper function of the neural system. How would
the private, subjective feeling of pleasure upon eating cake be an indicator
of normal neural function? Furthermore, where would one draw the line as
to what is an indicator of normality and what is irregular? Kosslyn never
addresses these notions. Perhaps the problem here is again one of the definition
of consciousness, and in that event, the preceding questions shouldn’t
even be asked. In any event, it seems that satisfying the five elements
of Kosslyn’s consciousness construct is not enough: a theory on consciousness
must inherently address private, subjective, sensory experience.
At this juncture, I feel as if I’m at a loss as to how I could proceed
any further than I have already come. Until science reveals something more
penetrating about the relationship between brain processes and consciousness
(the neurobiologist’s “holy grail”) we can only cross the explanatory gap
by means of one of the three ways I mentioned earlier: that consciousness
is either an emergent property or feature of a neural system (epiphenomenalism),
and/or it is a fundamental entity incapable of reduction to physical events
in the brain(nonreductionism/fundamentalism), or it is reducible in a way
that science cannot yet explain. In any case, we can identify a “cause”
for consciousness, simply using our earlier loose interpretation of the
term. Each of the first two schools of thought presently remains incomplete:
most epiphenomenalists cannot explain how mental processes can arise from
the brain’s physical activity, but maintain that emergent properties only
appear when matter is organized a certain way; fundamentalists have yet
to present any substantive hypothesis for what the “new laws” of consciousness
might be. In response to the question of whether animals have minds, epiphenomenalists
would suggest that different mental phenomena arise as the brain gets more
and more complex. Yet it is difficult for them to explain how two organisms
with similar brain size and complexity, and similar nervous system organization,
have such disparate abilities to learn in laboratory situations (Jacobsen,
120). Fundamentalists, interestingly, would not be able to answer the question
at all solely using the implications of their theory that consciousness
is completely irreducible to anything. They have, as of yet, placed no
constraints on the exact nature of its existence other than an association
with neural processes.
That is why, in part, Jacobson, aligns his comments on consciousness
with my third option, (which I discarded earlier), or the theory of materialists
who regard their concept of the mind as fully deducible from the laws of
chemistry and physics, “if the necessary data were in our possession and
if we were able to understand their meaning”(120). He cautions the scientist-philosopher
in pursuit of the seemingly unsolvable question of the existence of the
mind and the inner private, subjective states of humans: “We are not exempt
from the limitations imposed by the process of evolution of the human brain.
Our mental capacities are only what they now are by virtue of the contingencies
of survival of the fittest of a multiplicity of forms. There is no basis
for wishful thinking that the human brain is endowed with powers to understand
everything that may ever be discovered”(120).
So is that it? Shall we give up on the idea that consciousness can
be precisely defined or broken down? Nonsense. One of the most remarkable
characteristics of the pursuit of scientific knowledge by humans is that
we don’t know the inherent limits or boundaries to our understanding. Some
suggest the irony in the notion that we have been able to elucidate so
many complex phenomena in the world that surrounds us, yet still remain
bereft of any convincing knowledge of that mysterious and intimately familiar
phenomenon that lies within our own brains. Perhaps that is what continues
to make the search so desperate and relentless.
REFERENCES
Baars, Bernard J. (1988). A Cognitive Theory of Consciousness.
(Cambridge, England: Cambridge University Press).
Blakeslee, Sandra. (March 21, 1995). Behind the Veil of Thought: A
New Theory of Consciousness and How the Brain Might Work. Science Times,
The New York Times, C1, C10.
Chalmers, David J. (1995). The Puzzle of Conscious Experience. Scientific
American December: 80-86.
Crick, Francis and Koch, Christof. (1995). Why Neuroscience May Be
Able to Explain Consciousness. Scientific American December: 84-5.
Descartes, René. (1986). Meditations on First Philosophy. trans. John
Cottingham. (Cambridge, England: Cambridge University Press).
Gray, C.M., and Singer, W. (1989). Stimulus-specific Neuronal Oscillations
in Orientation Columns of Cat Visual Cortex. Proc. Natl. Acad. Sci. USA
86: 1698-1702.
Humphrey, Nicholas. (1993). A History of the Mind: Evolution and the
Birth of Consciousness. (New York, NY: Harper Perennial Publishers).
Jacobsen, Marcus. (1993). Foundations of Neuroscience. (New York, NY:
Premium Press).
Kosslyn, Stephen M. and Koenig, O. (1992). Wet Mind. (New York, NY:
The Free Press Publishers).
Libet, B. (1987). Consciousness: Conscious, Subjective Experience.
In Encyclopedia of Neuroscience. G. Adelman, ed. (Boston, MA: Birkhauser
Press).
Libet, B., Alberts, W. W., Wright, E.W., and Feinstein, B. (1967).
Response of human somatosensory cortex to stimuli below threshold for conscious
sensation. Science 158: 1597-1600.
Longuet-Higgens, Christopher. (1992). Vagaries of Thought. Nature 360:
117.
McGinn, Colin. (1989). Can We Solve the Mind-Body Problem? Mind 98:
349-66.
Searle, John R. (1992). The Rediscovery of the Mind: Representation
and Mind. H. Putnam and N. Block, series eds. (Cambridge, MA: The MIT Press).
Searle, John R. (1995). The Mystery of Consciousness. The New York
Review of Books 152, 17-18: 60-66/54-61.
Wolff, Simon P. (1992). A History of the Mind. The Lancet 340: 95.
Wolpert, Lewis. (1992). A Consciousness of Ourselves. New Scientist
135: 45.
NOTES
1. New Scientist, September 26, 1992, v135, n1840, 45.
2. Actually, the idea that all sensations are united somewhere in the
brain, the so-called “sensus communis,” originated sometime in the fourth
and fifth centuries with St. Augustine (354-430 AD)(Jacobsen, 19).
3. Reussel (1921, 1927) combined the two extremes in what has been
called “double-aspect monism,” asserting that mental events and neural
events are simply two refletions of some deeper underlying reality.
4. His use of the word “cause” would for now be better as “accompanied,”
simply because causality between brain activity and consciousness has never
been proven. By virtue of its manifestation in the brain, consciousness
has by too many been explained as “caused” by the microlevels of neurons,
synapses, neuron columns, and cell assemblies that lie at the heart of
the brain.
5. Not because it is difficult to do so in practice, but impossible
to do so in principle(Jacobsen 119).
6. According to Chalmers, a fundamental entity possesses its own fundamental
laws that must not interfere with those of the physical world. Rather,
“the laws serve as a bridge, specifying how experience depends upon underlying
physical processes.” A complete theory will have two components: “physical
laws,” telling us about the behavior of physical systems, and “psychophysical
laws,” explaining how those systems are related to conscious experience.
7. Perhaps one day we may find that consciousness is indeed comprised
of parts with similar fundamental characteristics, but we haven’t even
finished defining those characteristics yet.
Sameer Chopra '97(schopra@fas.harvard.edu) of Lowell House, is an
AB/AM candidate in Biological Anthropology and Neuroscience. He is currently
working on a project to determine the evolutionary significance of interspecific
cortical neuron number variation in mammals.
Back
To the Table of Contents
Machine
Analogies and Categories of Consciousness Consciousness as Serial Emulation
by the PDP Brain:
Dennett's Idea, Churchland's Rebuttal, the Real
Debate, and a Resolution
Robin S. Goldstein
I. Consciousness as serial emulation: The question at hand
In his 1995 book The Engine of Reason, the Seat of the Soul (hereafter
Engine), philosopher Paul M. Churchland enters the recently-rekindled debate
over what type of process makes up consciousness. He takes issue with aspects
of Dennett's explanation of consciousness. Dennett presented his ideas
about consciousness in his 1992 work Consciousness Explained (hereafter
CE). In sum, Dennett proposes that perceived consciousness consists in
a virtual serial machine implemented on top of the parallel-distributed-processing
(PDP) mechanism of the brain, and Churchland finds this notion “deeply
confused” (265).
Here I will first explain the differences between PDP and serial processing
and define emulation (section II). Next, I will set out the theory of Dennett
(section III) and the criticism and alternate theory proposed by Churchland
(section IV). I will then investigate the real debate and its differences
from the superficial dispute (section V), including an analysis of how
Dennett’s argument holds up to Churchland’s seven criteria for a theory
of consciousness. I find that while Churchland partially attacks a slightly
different hypothetical argument over whether true serial emulation occurs
and whether that matters, Churchland and Dennett only substantively disagree
on whether language is the key aspect of our consciousness, and thus on
whether humans and animals enjoy broadly the same kind of consciousness.
Finally, in section VI, I will set out my own opinion on the debate:
First of all, the speed of PDP networks makes emulation of a serial machine
much faster than the converse, so it is quite possible that such emulation
does occur. I find that Dennett’s virtual-serial-machine theory in itself
stands up to Churchland’s attack. But the second part of Dennett’s theory—the
emphasis on linguistic value and thus the “severely truncated” nature of
animal consciousness—does not withstand such scrutinization unscathed.
Since consciousness, at our current level of understanding, is a subjective
phenomenon anyway, linguistic knowledge does radically change the kind
of consciousness any human enjoys, but it does not necessarily follow that
higher significance—difference in level rather than kind—can be judged
from the capacity for language: Broad distinctions of categories of consciousness
do not carry much weight.
II. Preliminary Explanations
In order to analyze the arguments of Dennett and Churchland, I
will first explain the differences between Parallel Distributed Processing
and Serial Processing, and then I will mention computer-style emulation.
§ 1. Parallel Distributed Processing vs. Serial Processing
Numerous complex definitions of Parallel Distributed Processing
now circulate, but basic definitions of PDP do not make up the main part
of CE upon which Dennett and Churchland disagree. It is thus fair enough
to take Churchland’s rough and simple explanation, found on pages 11–15
of Engine, as our operative definition in this case. Parallel Distributed
Processing is, in a sentence, “transforming one pattern into another by
passing it through a large configuration of synaptic connections” (Churchland,
11). Below I have sketched out a comparison of the traits of parallel-processing
versus serial-processing machines:
§ Table 1.[PDF version only]
PDP systems may have been selected by evolution for humans and
“throughout the animal kingdom” (Churchland, 11) because of their greater
plasticity, accommodation for error due to imperfections in biological
life, and different strengths and weaknesses in types of computations.
But it is not hard to see why current desktop computers are serial machines.
Serial machines are capable of being explicitly instructed to behave in
a certain way, while parallel-distributed-processing machines must (generally)
be taught certain patterns of behavior through adjustment of synaptic weights.
(One such method that exists for PDP neural-network models emulated on
serial machines is called back propagation, or backprop, by which the system
of synaptic connections is slowly fine-tuned to give a certain kind of
output from a certain kind of input. But it still remains the case that
serial systems are vastly easier to program than parallel ones.) Of course,
it is accepted by both Dennett and Churchland that there is no external,
thinking agent willfully adjusting synaptic weights of the brain’s massive
PDP network.
§ 2. Emulation
A Turing Machine (a label which all serial desktop computers fit),
because of its nature, can emulate any other kind of machine. For modern
serial computers, emulating a different processing method means writing
a program in the kind of code native to the computer (in this case, serial
code) that, through software, has a front-end (input-output) behavior of
the emulated machine. This is commonly done with different varieties of
serial computer architecture (for example, the emulation of 680x0-type
code for PowerPC machines which have a different native instruction set
than 680x0 machines), and it is also (less commonly) done with entire processing
methods (such as parallel processing emulators implemented on PC-compatibles).
Speed is the main bottleneck of emulation, of course, as can be seen above
in Table 1. Thus the only problem with the emulation of a parallel system
B by a serial one A is that the emulated B is generally so much slower
than B that its usefulness is drastically damaged. The converse—the emulation
of a serial system by a parallel one—is also generally accepted by both
philosophers as at least possible.
III. Dennett’s serial-emulation theory as set out in Consciousness
Explained
One of the two most important claims in Dennett’s CE can be summarized
as follows:
Human consciousness . . . can best be understood as the operation of
a “von Neumannesque” virtual machine implemented in the parallel architecture
of a brain that was not designed for any such activities. The powers of
this virtual machine vastly enhance the powers of the organic hardware
on which it runs, but at the same time many of its most curious features,
and especially its limitations, can be explained as the byproducts of the
kludges that make possible this curious but effective reuse of an existing
organ for novel purposes [Dennett 210; his italics].
Dennett claims that human consciousness is “realized in the operation
of a virtual machine created by memes in the brain” (210). The term memes,
coined by Richard Dawkins in his popular 1976 book The Selfish Gene, refers
to a sociocultural analogue to genes. Memes can be cultural ideas, phrases,
thought-processes or ways of doing things, transmitted through communication
with a replication, mutation and selection speed higher than that of genes
but with a copying fidelity significantly lower. Specific human languages
(though likely based upon an innate instinct) fall under the meme category.
The memes, claims Dennett, create the brain’s equivalent of a special-purpose
machine, implemented on top of its existing architecture but more specifically
geared toward a certain purpose.
In explaining the implementation of consciousness on top of the brain’s
PDP architecture, Dennett draws a parallel with computer software: software
is “made of rules rather than wires” (Dennett, 211) just as he claims consciousness
is made of meme-rules on top of a synaptic network.
Dennett supports his claim with two basic arguments:
(1) The human mind is the inspiration for the Turing and von Neumann
machines, the basic serial architecture for computers. (2) Computer programmers
find it dramatically easier to code a serial von Neumann machine than it
is to program the parallel computers currently in development.
He continues that our mind-processes in solving computational problems
are superficially serial: there is a conscious window, the “von Neumann
bottleneck” (214), in which the current calculation or idea is processed,
and, as we can tell by simple introspection, we think in an essentially
sequential way.
Much later, in the final chapter of his book, Dennett extends his theory
to include implications that are much broader in scope: “Language … plays
an enormous role in the structuring of a human mind, and the mind of a
creature lacking language … should not be supposed to be structured in
these ways” (Dennett, 447). Dennett concludes that, although he claims
to believe consciousness is not a black-or-white property either fully
possessed or not possessed by any creature, the presence of language is
a particularly important feature of the kind of consciousness we know and
speak of: “The sort of consciousness such [languageless] animals enjoy
is dramatically truncated, compared to ours” (Dennett, 447). As we shall
see, this is where Churchland’s biggest disagreement will come in.
IV. The dispute as set out by Churchland’s response
Churchland vehemently opposes Dennett’s theory: “I think [Dennett’s
view of consciousness] is deeply confused … its central mistake … embodies
a profound irony” (265).
§ 1. Argument against the “Virtual Serial Machine” model
In the first aspect of his argument, Churchland takes issue with
the idea of the PDP brain working in emulation mode in the formation of
consciousness. Churchland claims that Dennett does not mention at all the
theory of recurrent PDP networks—the idea that descending feedback is a
mechanism within the system for adjusting weights, much as a modern serial-emulated
neural-network system adjusts synaptic weights through externally-computed
back-propagation (based on how suitable an output is to the external homeostatic
mechanism). He claims that this offers a better explanation of the “temporal
dimension” of consciousness.
Churchland’s next criticism is that input-output behaviors do not imply
anything about the process going on to achieve them. That is, just because
the end product of a computational process—in this case, the output “Joycean
stream” (as Dennett terms it) that we seem to associate with consciousness
does not imply anything about the process gone through to achieve such
an output. Even if the input-output behavior of what we term “consciousness”
seems to have emulated a serial process, Churchland claims, emulation does
not require that the PDP neurons actually engage in discrete-state, serial
processes. Churchland asserts that Dennett must find genuinely serial procedures
within the biological brain to support his theory.
§ 2. Argument against language as the foremost aspect of consciousness
In the second aspect of his argument, Churchland construes Dennett’s
theory as a misguided fall back to the disproved Aristotelian prototype
of conscious human cognition, which he claims is a “prototype of language-like
activity” (Churchland, 265). Churchland points out that PDP has come to
dominate modern neuroscientific theory here in the closing decade of the
twentieth century:
(4.) Dennett (1) attempts to pull that failed [Aristotelian language-like]
prototype back into the spotlight, (2) makes it the model for human consciousness,
(3) gives parallel distributed processing a cursory pat on the back for
being able to simulate a “virtual instance” of the old linguistic prototype,
and (4) deals with his theory’s inability to account for consciousness
in nonlinguistic creatures by denying that they have anything like human
consciousness at all (Churchland, 266).
Finally, Churchland—in what I shall find to be the heart of his disagreement
with Dennett—attacks the implication of Dennett’s theory that animals could
not possibly enjoy the same kind of consciousness that humans enjoy. With
suitably recurrent PDP networks, and the PDP networks alone, accounting
for consciousness, Churchland concludes, not only are nonlinguistic kinds
of consciousness (such as mental imagery and auditory consciousness) slighted,
but nonlinguistic animals are slighted in being labeled as having a much
lower form of consciousness than humans.
V. The real disagreement
In the following section, I will first show that Dennett’s theory
does not contradict any of Churchland’s seven salient dimensions of consciousness.
Next I will investigate why Churchland demands not only that it stand up
to his seven dimensions but that it also explain them—I believe Churchland
is looking for a different category of theory than Dennett provides. I
will then look further into the heart of the disagreement—the real debate.
I will look at the fact that while both claim not to believe in consciousness
as a binary trait, Dennett comes dangerously close to accepting all-or-nothing
consciousness, and this is where Churchland likely begins to take serious
offense. Continuing on this route, I shall ultimately find the question
of how similar animal (and nonlinguistic human) consciousness is to linguistically-developed
human consciousness to be at the heart of the debate. The other arguments
superficially spanning Churchland’s dispute—mostly arguments over whether
the brain’s PDP system could be acting in emulation of a serial machine,
the superficial center of the dispute—are not directly against Dennett’s
position.
§ 1. Does Dennett’s theory stand up to Churchland’s seven requirements?
Churchland, citing his seven requirements upon a theory of consciousness
listed in Chapter 8 of Engine, calls Dennett’s theory inadequate in its
subsequent explanatory performance of those seven. But, upon investigation
of them, Dennett’s theory would agree with five and only possibly (but
not directly) disagree with parts of two:
“1. Consciousness involves short-term memory.” A system in serial emulation
could certainly involve short-term memory, as serial, Turing-like machines
involve a window of computation holding a sort of short-term memory of
the data being currently dealt with. The temporal aspect of consciousness
is actually emphasized in Dennett’s theory. Clearly the virtual serial
machine explanation would support this claim.
“2. Consciousness is independent of sensory inputs.” A serial, linguistic-like
stream of information that had been stored in the brain could exist without
any serial inputs. A man sitting alone in a dark room with little sensory
input of any kind would easily be able to carry on a linguistic stream
with a virtual serial machine—the idea of the machine itself says nothing
about requiring sensory input at all times to function.
“3. Consciousness displays steerable attention.” Again, the serial-emulation
explanation is all about steerable attention: the single focus point in
consciousness, much like the window of calculation which is a section of
tape in a Turing machine, is the most compelling support of the serial
theory. A linguistic stream implemented over virtual serial architecture
could be “steered.”
“4. Consciousness has the capacity for alternative interpretations
of complex or ambiguous data.” This is perhaps Churchland’s most viable
contention against Dennett’s theory; but it is not out of the range of
possibility that an emulated serial machine have a capacity for alternative
interpretations—the linguistic stream can certainly double back over itself
or retrace its steps and take a different path. This criterion, while not
explained by Dennett’s theory, is not in direct conflict with it.
"5. Consciousness disappears in deep sleep;
"6. Consciousness reappears in dreaming, at least in muted or
disjointed form.” While Dennett’s serial-emulation theory does not speak
of this phenomenon directly, the appearance and reappearance of the emulation
mode is, once again, certainly not ruled out by Dennett’s theory. There
is no logical reason why a virtual serial machine might not be implemented
during sleep, and there is no logical reason why the virtual serial machine
might not be switched off during certain modes of sleep, much as a computer
software program does not need run all the time.
“7. Consciousness harbors the contents of the several basic sensory
modalities within a single unified experience.” This final tenet of Churchland’s
theory hints at the fact that the center of the real disagreement between
Dennett and Churchland is over whether the linguistic aspect of consciousness
is of paramount importance. However, we must keep in mind that a serial
machine can process any type of information, not just linguistic-like information.
While Dennett puts the most weight on the linguistic aspect for our type
of consciousness, he does not deny that the several basic sensory modalities
do seem to converge to a single unified experience that makes up that virtual
serial stream. So even Churchland’s seventh tenet does not directly oppose
Dennett or dive directly into the genuine disagreement.
In summary of the above analysis, I find Churchland’s #1 and #3 in
direct support of Dennett’s explanation; I find #2, #5, and #6 not at all
in opposition to the explanation; and I find #4 and #7 hinting at opposition
to, but still not in direct conflict with, Dennett’s explanation. Thus
no part of Churchland’s theory suggests why a virtual serial machine would
not be possible. Where the two really disagree, as I stated above and will
explain again below, is in which part of the stream of consciousness is
most important.
§ 2. What kind of a “theory of consciousness” is Dennett’s and how
does it compare with Churchland’s demand upon it?
Although above I have found that Churchland’s seven salient dimensions
of human consciousness do not directly oppose Dennett’s theory, let us
look more closely at the way in which Churchland finds Dennett’s theory
lacking. He states that the theory “offers no account of any of [the seven],
let alone a unitary account of all seven” (Churchland, 259). He also deems
the theory “inadequate in its subsequent explanatory performance” (269).
But why does Churchland demand that Dennett’s theory perform in a way
such that it explains or offers an account of his seven dimensions? The
theory merely proposes that consciousness is made up by the emulation of
a virtual serial machine. I suggest that Dennett’s theory is not in the
same category as the kind of theory Churchland wants, and it is because
of its different category rather than an inherent flaw (plus the ideological
dispute mentioned below) that Churchland finds it lacking. While Dennett’s
theory is a “framework” (what I will call theory type T1), Churchland demands
that it behave like an “explanation” (what I will call theory type T2):
(1) Theory type T1 of phenomenon P must explain all aspects P1 , P2
, P3 , … , Pn of P in order to have validity, because it claims to exist
as an explanatory device. Examples of theories of type T1 are physical
theories accompanied by equations, i.e. Special Relativity, etc.
(2) Theory type T2 of phenomenon P suggests a mechanism for the way
P works without having to, or trying to, explain why all characteristics
P1 , P2 , P3 , … , Pn of P occur. It is not a requirement of theory T2
that it have such explanatory power of all P1 , P2 , P3 , … … , Pn in order
to have validity. An example of a theory of type T2 would be the scientific
explanation of the food web—it explains the way a certain life cycle works,
but it does not need explain all salient aspects of animals’ eating habits
and eating behaviors in order to be valid and true.
(3) Of course, both theory type T1 and theory type T2 must not contradict
any characteristic Pk of phenomenon P.
While Dennett’s theory is of type T2 , Churchland demands type T1 behavior
from the theory, and, because of what he sees as inadequate performance
in the defense of the requirements above of a type T1 theory, he declares
it invalid.
I have shown above in § 1 that Dennett’s theory meets requirement (3)
above—it does not directly contradict any dimension of consciousness that
Churchland brings up. Churchland demands that Dennett’s theory not only
not contradict his seven dimensions, but that it explain and account for
them. Thus Churchland’s criticism of the serial-emulation part of Dennett’s
theory falls short.
§ 3. Dennett’s waffling stance over whether consciousness is a binary
trait
Dennett, as shown in his “Multiple Drafts Theory” argued elsewhere
in CE and referred to in his argument for serial emulation, does not want
to commit to the position that consciousness is a binary trait—that is,
that it either exists or it does not in a given being. Neither philosopher
is willing to officially assume that position. As Dennett clearly states
later in Chapter 14 of his book, he dismisses the question of the “assumption
that consciousness is a special all-or-nothing property that sunders the
universe into two vastly different categories: the things that have it
… and the things that lack it” (Dennett, 447).
So Dennett does not want to grant that beings either “have” or “lack”
consciousness. But elsewhere in his language, he is willing to come dangerously
close to admitting the all-or-nothing process he denies: “The sort of consciousness
[nonlinguistic] animals enjoy is dramatically truncated, compared to ours”
(447). His description here is befuddling: if he is talking about the content
of conscious thought, rather than consciousness itself, there is no contradiction;
but he goes on, using Oliver Sacks’ writings as evidence, to conclude the
same thing about nonlinguistic humans: “Without a natural language, a deaf-mute’s
mind is terribly stunted” (448). In order to remain consistent with his
previous assertion about the importance of the Joycean linguistic-ness
of the stream, Dennett accepts that drastically different (and, as implied
by his language—“stunted”—lesser) kinds of consciousness exist for animals
and nonlinguistic humans—if he did not, his earlier foundation would crumble.
But in extending the linguistic-importance idea to its natural conclusion
as he does, he slights animal consciousness and all but comes out and argues
for that binary switch of consciousness, the binary switch whose existence
he is so scared to admit. And it is this extension and virtual acceptance
of that dangerous binary-switch idea that, I believe, leads Churchland
to reject Dennett’s entire idea of seriality. Perhaps if Dennett had been
clearer about whether he was referring to the “stunted” nature of conscious
thoughts, rather than the whole of consciousness, Churchland would not
have taken such offense and the dispute would reveal itself as more obviously
trivial.
§ 4. The real disagreement: Linguistic value and animal consciousness
Above I have shown (1) that Dennett’s theory stands up to Churchland’s
seven criteria of consciousness, (2) that for Churchland to want more from
Dennett’s theory is unreasonable because it is a theory of type T2, and
(3) that Dennett gets to close to making consciousness a binary trait and
that Churchland thus rejects Dennett’s entire theory because he disagrees
with the unfairness to nonhuman animals that results from the weight on
linguistic value. What naturally follows from these conclusions is that
where the substantive debate truly lies is in what aspect of consciousness
is most essential to our definition. Dennett says it is the linguistic
aspect of consciousness, the “Joycean stream,” while Churchland disagrees:
“Dennett’s account of consciousness is skewed in favor of a tiny subset
of the contents of consciousness: those that are broadly language-like”
(Churchland, 269). Churchland feels that the mechanism of non-linguistic
consciousness could be explained non-serially.
Although, contrary to Churchland’s use of rhetoric, Dennett does acknowledge
that consciousness includes musical, visual, sensory, tactile, and motor
images, Churchland is right in the sense that Dennett weighs linguistic
consciousness very highly. Dennett perhaps could have separated his theory
into two different theories, with the serial emulation part standing alone
from the drastic emphasis on linguistic value in consciousness. But because
it is presented as a package deal, Churchland’s deep faith that higher
animals’ consciousness is very similar to ours leads him to oppose the
linguistic part too much not to reject all of it. He illustrates his claim
with a picture depicting a human and a monkey, both with smiling faces
and recurrent PDP-network consciousness. Dennett might instead draw the
monkey frowning, linguistically impaired and thus unable to enjoy the thoughts
comprising what we think of as the contents of our consciousness. Is this,
at its heart, an unresolvable debate?
VI. The Resolution: Separation of Dennett’s ideas and acceptance
of both the virtual serial machine and nonlinguistic animal consciousness
At the center of this heavily-rhetoricized dispute, then, is merely
an altered version of Nagel’s old question: What is it like to be a bat?
Although the superficial dispute is about the implementation of a virtual
machine on top of a serial one, the debate is really about whether animals
are conscious in the way that we are. It is a debate perhaps more ideological
than scientific; Nagel’s question of how animal consciousness differs from
our own is a debate that I believe cannot be resolved by questioning whether
consciousness is PDP acting in serial emulation or just recurrent PDP in
itself.
I believe that Dennett’s theory and Churchland’s antagonistic response
can each be drawn from to form a synthetic theory of consciousness. While
the opinions of the two with regard to animal consciousness will remain
opposed, my resolution lies in the simultaneous acceptance of Dennett’s
theory of virtual serial emulation and the acceptance of Churchland’s contention
that the serial stream of consciousness is not necessarily most importantly
linguistic in character.
§ 1. Acceptance of Dennett’s virtual serial machine in the wake
of Churchland’s criticism
Dennett’s virtual serial machine is feasible regardless of whether
the information that passes through the emulated serial processor is linguistic
or not. Earlier, I showed that the idea of the virtual serial machine in
itself (Nagel’s question aside) does not oppose Churchland’s seven criteria
for consciousness (which I also accept as valid observations of the way
the phenomenon works). I also explained why I do not believe that the virtual
serial machine hypothesis must explain all of them in order to be true.
Churchland’s most compelling argument against the actual serial-machine
idea (he spends most of his rebuttal instead arguing instead against the
language idea) is that one would need to locate genuinely serial processes
inside the PDP brain in order for it to behave as Dennett says it does.
As mentioned above in my summary of Churchland’s position, he instead believes
that recurrent PDP networks in themselves with their “continuously unfolding
temporal dimension” (267) can account for what Dennett describes.
This is not such a big disagreement as it seems; Dennett’s serial processes
might be emulated by way of the temporal dimension of recurrent network
feedback that Churchland describes. This is particularly feasible given
that a PDP network in emulation, because of its incredible initial speed
advantage in calculation (see Table 1), would not suffer the speed hit
that an emulation in the reverse direction would. Recurrent PDP networks’
temporal unfolding might well resemble a serial machine. We do not need
to find genuinely serial processes for this to be a valid claim. I therefore
grant Dennett his virtual serial machine. That machine, however, need not
be skewed, as he would have it, toward language-like operations.
§ 2. Acceptance of Churchland’s theory of animal consciousness
Where Churchland is most compelling is in his assertion that language
is not necessarily king when it comes to consciousness. The general human
idea, or meme, that consciousness exists comes from the correlations among
the linguistic communications of an innumerable number of human self-observers—the
similarities between a great number of individuals’ descriptions of watching
themselves do things such as make decisions, use language, and contemplate
themselves and the mysteries of their own existence. So consciousness,
at its core, is a subjective phenomenon and exists only in correlations
among its imperfect translation into human language: There is no direct
and inherently conclusive external evidence to any one self-contemplator
that any other being around him, whether of his species (human, in this
case) or not, has that same kind of consciousness—there is only evidence
that the output behaviors of describing that consciousness are correlated.
Linguistic knowledge, then, does radically change the description of
consciousness any being is capable of giving to a human—that is, from positive
to negative, from ability to inability. A binary switch is flicked—any
animal incapable of describing his consciousness to a linguistically-capable
observer scores a “0” in the mind of the observer on the scale upon which
the observer’s own idea of consciousness has been shaped. Since one’s entire
conception of consciousness comes from the correlation between others’
output—their descriptions of consciousness—and one’s own subjective experience,
it would be natural to assume that animals incapable of relating that subjective
consciousness in words similar to our own are incapable of enjoying a similar
consciousness (or, if Dennett would rather, similar contents of consciousness).
But the fact that it is a natural first reaction does not make it right.
It does not necessarily follow that higher consciousness—difference in
a being’s hierarchical level or category of consciousness rather than just
communication capacity—can be judged from the capacity for language and
animals’ consciousness thus unnecessarily dubbed “truncated”: Broad distinctions
of categories of consciousness cannot be drawn from the mere information
of whether an animal has linguistic capacity. We must get past our natural
anthropocentric inclinations and allow science’s discovery of the similarity
of recurrent PDP networks in humans and higher primates to guide us in
our conception of consciousness. We must at least allow for the possibility
that theirs is more like ours than it may seem. Dennett may be closer to
this opinion than his language reveals; he may see the “truncating” of
higher-primate linguistic-type serial-emulation consciousness as a mere
feature of the “contents” of consciousness and an unimportant aspect of
conscious processes as a whole. But his choice of words speaks otherwise,
particularly to Churchland: whether or not he intends to, Dennett implies
that deaf-mutes and other primates “suffer” in their non-Joycean-ness—an
inflammatory idea.
§ 3. What we cannot know Dennett and Churchland ultimately disagree
over a question that is extremely hard to resolve. As technology is developed
to monitor activity in different brain areas, it looks to many observers
that we are no closer to discovering whether or not higher animals sense
consciousness at all like we do—it still looks much of the time as if the
elusive knowledge of others’ consciousness lies in a category which we
cannot know.
Dennett’s theory casts some light on the similarities between consciousness
and the operation of a serial Turing machine. Churchland’s response, while
unnecessarily rejecting all of that theory, does illuminate the mistake
of generalizing too much on one’s own linguistic experience of consciousness.
What we cannot yet know is whether consciousness will ever be isolated
as a brain process, and whether these questions will be resolved in a more
scientific way.
REFERENCES
Dennett, Daniel C. (1991). Consciousness Explained. (Boston:
Little, Brown and Company).
Churchland, Paul M. (1995). The Engine of Reason, the Seat of the Soul.
(Cambridge, MA: MIT Press).
Chan, Sophia and Jenkins, David. “The Turing Machine.” From Brunel
University Artificial Intelligence Web Pages, World-Wide Web: http://http1.brunel.ac.uk:8080/research/AI/alife/al-turin.htm.
Notes
1. 680x0 processors, such as are found in the Macintosh II and Quadra
series, and Intel 80x86 processors, are based a Complex Instruction Set
Computing architecture, with more specific kinds of specialized instructions
but a larger number of possible instructions; the faster PowerPC processors,
used in the Power Macintosh machines and most UNIX-based machines, run
on Reduced Instruction Set Computing, with fewer possible instructions
and all instructions more basic. The slower 680x0 and 80x86 instruction
set is often emulated on PowerPC computers for backward compatibility.
2. Churchland casts Dennett as completely disregarding all non-linguistic
forms of consciousness: “Human consciousness, however, also contains visual
sequences, musical sequences, tactile sequences, motor sequences, visceral
sequences, social sequences, and so on. A virtual serial machine has no
especially promising explanatory resources for any of these things” (Churchland
269).
3. For more rhetoric one need look no further than Churchland’s comparing
Dennett to a spiritualist: “One might as well propose the discredited ‘vital
spirit’ as a substantive explanation for the phenomenon of Life, and then
cite … the ability of DNA molecules to simulate a ‘virtual vital spirit.’
Robin Goldstein '98 (rgoldst@fas.harvard.edu) of Dunster House,
hopes to become a Special Concentrator in Cognitive Neuroscience and Philosophy.
In his free time, he enjoys reviewing movies for his Web page, playing
ping pong, and traveling around Latin America writing for Let's Go.
Back
To the Table of Contents
The Emergence
of Orientation Selectivity in Self-Organizing Neural Networks
Josh McDermott
Introduction
The past three decades have witnessed a host of impressive findings
concerning the functional architecture of the brain. One trend that much
recent research suggests is that there is a high degree of specialization
within the brain: different parts of the brain do different, highly specific
tasks. Furthermore, most estimates place the number of cortical neurons
alone at 1010, with roughly 1014 total synapses connecting them. Given
this enormous number of neurons, and considering the extreme structural
complexity embodied by a healthy and fully-developed mammalian brain, the
project of elucidating this structure in some ways pales in comparison
to that of explaining how it came to be. It is clear that the mammalian
genome “cannot, in any naive sense, contain the full information necessary
to describe the brain” (von der Malsburg, 1990). Yet somehow, in the process
of development, the brain acquires its structure. The compelling nature
of this quandary makes the notion of self-organization very appealing.
The term “self-organization” is generally used to describe the evolution
of complex behavior in systems that consist solely of many very simple
parts. The brain is an excellent example of such a system, for while neurons
are by no means simple, many of their principle functional characteristics
are believed to be relatively easy to approximate and model. In this review
I will focus on research that suggests that the early stages of the mammalian
visual system can be implemented solely through the self-organizing properties
of neural networks.
Central to all such research is the assumption that synaptic modification
occurs via some form of Hebbian learning, whereby the strength of a synapse
between two neurons is increased if the activities of the neurons are correlated
in time, that is, if one tends to be active at the same time that the other
is. This learning rule was first suggested by Hebb in 1949 without an accompanying
brain mechanism or body of evidence for its existence. Since then, the
discovery of long-term potentiation (LTP) in the hippocampus has provided
evidence that a form of Hebbian learning does occur in the brain, although
how it is accomplished is still not clear. While skepticism about LTP remains,
it is generally agreed that some form of local learning is bound to control
connectivity in many cases, if only because the idea of completely central
control seems inconceivable. Most efforts to show that self-organization
can occur in structures resembling the brain thus make use of Hebbian learning.
Vision research has been among the most fruitful areas of neuroscience,
and the basic structure of the early levels of the mammalian visual system
are for the most part established. The research that I will be discussing
concerns the visual system up to and including the primary visual cortex
(V1). The visual pathway begins at the retina, where there are several
layers of cells which project via the optic nerve to the lateral geniculate
nucleus (LGN) in the thalamus, which in turn projects to layer 4C of V1.
Cells in the inner layers of the retina and in the LGN are characterized
by spatial-opponent receptive fields: Stimuli placed in a central, circular
region of the receptive field of a cell in these regions tend to excite
the cell, while stimuli placed outside the excitatory region tend to inhibit
the cell. In addition, the retina and LGN are topographic: the spatial
layout of their cells' receptive fields is topologically similar to the
physical layout of the cells themselves. V1 cells differ from those in
the LGN or retina in that many of them have orientation selective receptive
fields; a cell in V1 will tend to respond strongest to a line of a particular
orientation placed in its receptive field. Furthermore, as the Nobel Prize-winning
research of David Hubel and Torsten Wiesel revealed, orientation selective
cells are laid out in an orderly fashion. In addition to being retinotopically
organized, a group of cells whose receptive fields correspond to a certain
point in space will be arranged in a line such that their orientation preferences
vary continuously along that dimension in cortex. Many have speculated
that this layout has the favorable characteristic that each small portion
of V1 has all the machinery necessary to perform the first steps in extracting
information from the portion of the visual field which it represents. The
research reviewed here is exciting because it suggests that many of the
characteristics I have just described can be accomplished through usupervised
self-organization.
A host of experiments have shown the influence of the environment on
the development of the nervous system. Although many results in this literature
are controversial, it is generally agreed that kittens raised in visually-altered
environments developed abnormal patterns of orientation selective cells
in V1 ( see Blakemore and Cooper, 1970, Hirsh and Spinelli, 1970 for the
first such experiments, and Movshon and Van Sluyters, 1981 for a review).
Such results suggested to some that orientation selectivity was the result
of learning processes in the visual system during postnatal development.
This sparked several papers that described the development of orientation
selectivity with Hebbian learning in neural networks that were exposed
to structured “visual” input (see von der Malsberg, 1970, for one early
attempt). However, with a few exceptions, animals in most of the classic
visual plasticity studies do seem to develop some sort of orientation selectivity
regardless of the visual environment (see Hirsh and Spinelli for a controversial
exception). The environment seems only to be able to skew the distribution
of orientation selectivity, rather than completely determine it, suggesting
that the visual system is intrinsically biased towards developing cells
with this property. Furthermore, Hubel and Wiesel originally found that
some degree of orientation selectivity exists in kitten primary visual
cortex immediately after birth, before exposure to any structured visual
input (Hubel and Wiesel, 1963; Movshon and Van Sluyters, 1981). More recently
it has been found that orientation selectivity is fully developed at birth
in monkeys and sheep (Wiesel and Hubel, 1974; Ramachandran, Clarke, and
Whitteridge, 1977). Based on this it was generally accepted that although
the environment may be capable of affecting the properties of cells in
the early levels of the visual system, it is not their source. This left
the problem of how the visual system achieved orientation selectivity unsolved.
Self Organizing Neural Nets: Ralph Linsker’s Work
A series of three papers by Ralph Linsker and the work by several
others that followed them provide a possible explanation for the existence
of prenatal orientation selectivity. In these papers Linsker demonstrated
that spatial opponency and orientation selectivity could arise in an unsupervised
feed-forward network trained with Hebbian learning on completely unstructured
input. Linsker further showed that with the addition of lateral excitatory
connections within the layer that developed orientation selective cells,
the cells would develop their orientation preferences in a smoothly varying
fashion in orientation columns similar to those in V1. These are surprisingly
sophisticated properties for such a simple system to develop in the absence
of structured input, and they will be the focus of this article.
Linsker’s network is a crude approximation of the visual system, with
a two-dimensional input layer feeding to successive two-dimensional layers
that can be interpreted as corresponding to different levels in the visual
pathway. Each layer is composed of linear units and receives input from
the previous layer. The inputs for a unit come from a Gaussian distribution
over a local region of the previous layer. In the simulations he published,
Linsker allowed the weights on connections from a given unit to take both
positive and negative values. While this is biologically unrealistic, in
that neurons are believed to be either excitatory or inhibitory, Linsker
has reported that his results hold when the units are divided into classes
which are constrained to have either only positive or only negative weights
on their outgoing connections. Weights were also constrained to remain
within certain positive and negative limits, which, based on physiological
limitations, is biologically reasonable.
Linsker uses a version of the Hebbian learning rule:
[1] Ðwij = b + c(ai - d)(aj - e)
where wij is the strength of the connection from neuron j to neuron
i, ai and aj are the outputs of neurons i and j, respectively, and b, c,
d, and e are constants. The importance of this equation is that the weight
change is proportional to the product of ai and aj. Thus the weight change
is most positive if ai and aj are correlated over time, and is most negative
if they are anticorrelated. In order to prevent his simulations from being
prohibitively long, Linsker averaged Equ. 1 over a number of presentations
to the input layer, resulting in an equation for the time-rate-of-change
of a given synaptic weight which could be solved for the mature weight
values of a particular layer. His averaged equation can be understood as
follows: Ðwij is directly related to the correlation between the activities
of the postsynaptic (labeled i) and the presynaptic (labeled j) neurons,
and the activity of the postsynaptic neuron is a linear function of all
its inputs. Thus the time-rate-of-change of wij is proportional to the
degree to which the activity of neuron j is correlated with the other neurons
that give input to neuron i. Explicitly,
[2] ðwij/ðt ³ p + mÝk wik+ Ýk (Qjkwik)
where p and m are constants, k indexes neurons that provide input to
neuron i, and Qjk is proportional to the correlation function of the activities
at neurons j and k. This function is well-defined because each layer of
cells receives input only from the preceding layer, and the layers are
developed one at a time. Formulating the weight change this way allowed
Linsker to run his simulations more efficiently. Importantly, though, it
explicitly reveals what turns out to be a crucial consequence of Hebbian
learning in feedforward neural networks: change of the weights into units
of a given layer is determined by the form of the correlation in the activities
of the units of the previous layer.
The weights to layer B are developed based on the activity in the input
layer A, and once the weights in layer B reach stable values, the activity
in layer B, propagated from layer A, is used to develop the weights in
layer C, and so on. Because the network is developed in this manner, each
layer’s weights are a function only of the pattern of activity in the previous
layer. Also important is the property of Equation 2 that either all or
all but one of the inputs to any given cell will saturate to their limiting
values. This property falls directly out of the equation; see Linsker 1986a
for the proof.
Spatial Opponency
When the activity in layer A is uncorrelated, the correlation function
is close to zero for most pairs of neurons. Thus ðwij/ðt is close to constant
for each connection from layer A to a cell in layer B. For appropriately
chosen constants, this results in the saturation of all the weights from
layer A to layer B at the upper positive limit. Because each cell in layer
B receives input from a Gaussian distribution of cells in layer A, a given
cell in layer B will, at maturity, function to compute a spatial average
of a local region of activity in layer A. And because the receptive fields
of nearby cells in layer B overlap, cells that are close together will
include many of the same layer A neurons in their average, and will thus
have correlated activities. Linsker shows that this correlation function
is a Gaussian (whose peak is at the cell in question - obviously, the highest
correlation is between a cell and itself - and falls off for cells whose
receptive fields are far away).
Once the connections from layer A to layer B were mature, Linsker developed
layer C using the Gaussian correlation function of layer B. He found that
the morphology of layer C cells fell into a series of regimes depending
of the values of p and m. Cells developed to have either i) all excitatory
inputs, ii) all inhibitory inputs, iii) “ON-center” circularly symmetric
opponent connections: a core of excitatory connections surrounded by a
ring of inhibitory connections, iv) “OFF-center” circularly symmetric opponent
connections: a core of inhibitory connections surrounded by a ring of excitatory
connections, and v) spatially divided inputs such that approximately one
side of the receptive field was composed of excitatory and the other half
of inhibitory connections. I will focus on case (iii), where layer C develops
center-surround receptive fields strikingly similar to those found in the
retina and lateral geniculate nucleus, the two stages in the visual pathway
that lead to primary visual cortex.
The spatial opponent receptive fields develop because of the presence
of the Gaussian correlation function of layer B in Equation 2. For negative
m and positive p, the following occurs during the maturation process: positive
p values cause all weights to initially increase such that the wik contribution
to ðwij/ðt is positive. Then, since the sum in Equation 2 is over the synapses
to the postsynaptic neuron i in layer C, which have a Gaussian distribution
in layer B, ðwij/ðt has a greater contribution from the correlations that
the activity of neuron j has with neurons in the central region of neuron
i’s receptive field, and a smaller contribution from the correlations that
it has with peripheral neurons, simply because there are fewer of them.
But since the correlation function Q is a Gaussian for layer B, a given
layer B neuron is most correlated to neurons nearby, and less with neurons
farther away. Thus if neuron j is in the central region of neuron i’s receptive
field, its connection to neuron i will be increased more than will a peripheral
neuron’s. Although the correlation function between a given neuron and
the neurons surrounding it are identical for all neurons, the Gaussian
distribution of afferent inputs to postsynaptic cells causes the high portion
of a peripheral neuron’s correlation function to be sampled less frequently
than is the high portion of a central neuron.
As a comparison, consider what would happen if Q were a constant function.
Then each neuron in layer B would be equally correlated with all the other
neurons in layer B. Were this the case, the Gaussian distribution of afferent
inputs to the neuron i in layer C would not have the effect that it does,
since the Q contribution to the sum in Equation 2 would be independent
of where it was sampled.
Thus ðwij/ðt is larger for neurons in the center of neuron j’s input
distribution than for neurons in the periphery. Furthermore, the absolute
magnitude of ðwij/ðt can be shifted by varying the constant m. For sufficiently
large negative m, ðwij/ðt is negative for peripheral neurons, and positive
for central ones. Since Equation 2 causes all of the weights to a given
neuron to saturate, this causes the connections from layer B neurons in
the central region of a layer C neuron’s receptive field to mature to the
maximum excitatory value, while those in the periphery saturate to the
inhibitory limit.
To summarize, layer B computes the local spatial average of the random
activity in layer A, because of the Gaussian distribution of inputs. This
makes the correlation function for layer B Gaussian, and combined with
the Gaussian synaptic distribution, for appropriate values of the constants
in Equation 2, spatial opponency results. Thus there are two crucial dependencies:
the proper values of p and m, and the Gaussian synaptic distribution. These
will be discussed later.
Orientation Selectivity
Development of orientation selective cells proceeds in similar
fashion. Parameters are chosen such that layer C develops into “ON-center”
cells. Then the correlation function is computed for layer C. Since layer
C is, up to small deviations, uniform, the correlation function (which
in general form is a function of two variables, corresponding to any two
cells in a layer) is effectively a function of one variable. That is, because
of the layers uniformity, the correlations of a given cell Z with another
cell Y depends only on the distance between cell Z and cell Y, and not
on the actual position of the two cells in the layer. Linsker thus idealizes
the correlation function Qij to the function Q(s), where s is the distance
between two cells.
For a layer of spatial-opponent cells, Q(s) has a “Mexican-hat form”:
Q is positive for small s, when cells are close to one another and their
excitatory center cores overlap, negative for intermediate s, when the
inhibitory surround of one cell overlaps the excitatory core of another,
and zero for large s, when the cells are far apart and completely uncorrelated.
This correlation function is then used to develop layer D. Linsker reported
that there were a range of morphological options for layer D, including
the formation of orientation selective cells, but that the regime of orientation
selectivity was not very stable. One of the other regimes for layer D was
spatial opponency, with the cells in layer D differing from those in layer
C in that the “Mexican-hat” correlation function QD(s) for layer D had
deeper minima than did QC(s). Linsker showed that “Mexican-hat” correlation
functions with deeper minima produced in the following layer a more stable
regime of orientation selectivity. He thus set the parameters of layer
D such that it developed spatial opponent cells. He did this for layers
D through F, with each successive layer having a Mexican-hat correlation
function with deeper minima than the previous layer’s function.
Linsker simply presents the results of his simulations, and does not
discuss why the correlation functions develop deeper minima. However, some
of the data he presents suggests that this deepening occurs because the
cells develop receptive fields with larger inhibitory surrounds. Linsker
gives the minimal values and the location of the zero-crossing for the
Mexican-hat correlation functions of layers, and in addition to minimal
values decreasing with successive layers, the zero-crossing appears to
decrease. He also mentions that the minimal values of the correlation function
vary inversely with the average sum Ýjwij of all the weights on the afferent
connections to a unit i. In other words, there is a direct relationship
between the relative proportion of inhibitory connections and the depth
of the Mexican-hat minima. This is consistent with my observation that
the zero-crossing decreases as the minima depth decreases, for as the zero-crossing
decreases, the distance that two cells can be apart and still have positively
correlated values decreases. But this happens for center-surround cells
only if the center excitatory cores shrinks and the inhibitory surrounds
grows. Furthermore, layers of cells with core and surround that are of
approximately the same thickness will, logically, produce the deepest minima
for their correlation function, because the core and surround can then
completely overlap, causing the greatest anticorrelation. Since layer C
cells have excitatory cores that are large compared to their surrounds,
increasing the size of the inhibitory region in successive layers moves
the two regions towards being of equal size, and acts to increase the maximum
anticorrelation. Thus although Linsker doesn’t actually discuss why the
minima of the Mexican-hat function deepen with successive layers, it seems
likely that the minima depth increases because the inhibitory surround
of the cells’ receptive fields grows in size.
Why do the inhibitory surrounds increase in area for progressive layers
of cells? Again, Linsker doesn’t discuss this, but the answer can be found
by considering how the formation of center-surround cells from a Mexican-hat
correlation function differs from the formation from the Gaussian correlation
function of layer B. The main difference between the two functions is that
the Mexican-hat has a smaller average value than does a Gaussian. Recall
from my discussion of the center-surround cell formation process that the
inhibitory regions form because the sum Ýk (Qjkwik) is smaller for peripheral
connections than it is for central ones, and thus at some point in a cell’s
receptive field, ðwij/ðt drops below zero, which sends wij to the inhibitory
limit. If the average value of Q decreases, Ýk (Qjkwik) will decrease for
all connections wik, and the threshold dividing excitatory and inhibitory
connections will decrease, creating cells with smaller excitatory cores
and larger inhibitory surrounds. Thus because layer C has a Mexican-hat
correlation function, the cells in layer D develop smaller excitatory cores
and larger inhibitory surrounds. But as discussed in the previous paragraph,
increasing the size of the inhibitory surround in layer D causes QD to
have deeper minima than QC. This in turn causes layer E to have larger
inhibitory surrounds (assuming that the parameters for layer E are set
such that it develops center-surround cells), which causes layer E’s Mexican-hat
correlation function to be deeper than that of layer D, and so on.
Linsker reported that with a sufficiently deep correlation function
for a layer of cells, the succeeding layer will develop orientation selective
cells that are stable with respect to random changes in the initial weights.
In the network he describes, he developed four layers of center-surround
cells to obtain a suitably deep function.1 He describes the results obtained
by varying two parameters: the average sum Ýjwij, which we will call g,
and RG, where RG is the radius of the Gaussian distribution of afferent
connections to a cell in layer G. Varying g is equivalent to varying the
ratio of m and p, the two constants in Equation 2. Linsker found that g
and RG were the main parameters governing the development of orientation.
There were several broad classes of development characteristics, each
corresponding to a range of RG with respect to smin, the location of the
minimum value of the correlation function for layer F. I will discuss two
of these classes. First, for RG much less than smin, layer G expressed
morphology that was quite similar to that of layer C, which was discussed
earlier.2 Though Linsker does not discuss this, this class probably exists
because when the radius inside which a cell draws its input is much smaller
than the minimum of the Mexican-hat function, the cell can only “see” the
central portion of the function. Equation 2, which is where the influence
of the correlation function comes into play, sums over the correlation
function only for s-values within the radius of the Gaussian input distribution,
simply because the sum is over the cell’s inputs. So if the radius of the
Gaussian is much smaller than smin, Equation 2 includes values of the correlation
function for small s only, and over that restricted range, the Mexican-hat
function resembles the Gaussian correlation function of layer C.
For RG close to smin, decreasing g results in a more interesting range
of morphologies. For high g, the cells are obviously all-excitatory. As
g is lowered, isolated regions of inhibitory connections appear. As g is
decreased further, the G cells become bilobed: they have a central strip
of excitatory connections, with parallel inhibitory bands of connections
on either side. Such cells, clearly, are orientation selective. The orientation
that a cell develops is random. As g is further decreased, the inhibitory
side bands extend to enclose the excitatory region, thus forming a center-surround
cell.
What causes orientation selectivity? Linsker answered this by referring
to an energy function he created which has the property (common to other
energy functions) of decreasing with every weight change. The weight development
process is thus a process of gradient descent, and this sheds some light
on the orientation formation. In order to avoid introducing new equations
and variables, and in the interest of explaining the phenomenon in as biologically-grounded
terms as is possible, I will attempt an explanation based on the weight-change
equation instead of referring to Linsker’s energy function. Recall the
time-rate-of-change of a weight that was described by Equation 2:
[2] ðwij/ðt ³ p + mÝk wik+ Ýk (Qjkwik).
The first two terms on the right hand side of Equation 2 are the same
for all connections. The term Ýk (Qjkwik) is not, however. It will be similar
for connections from cells that are nearby in layer F, because the correlation
function will tend to multiply each weight by a similar number. For connections
from cells that are further apart in layer F, Ýk (Qjkwik)will tend to be
different, because the correlation function for one connection will have
a positive value when the correlation function of a second connection has
a negative value. Thus there is an overall pressure within the development
process for the connection from a cell in layer F to have a value equal
to that of the connections from nearby cells, and different from those
of connections from cells that are further away. However, from topological
considerations this obviously cannot be realized in full. What happens
instead is that contiguous regions of excitation or inhibition are formed
which satisfy the pressure imposed by the correlation function as best
as can be done. The stripes that produce orientation selectivity are one
possible arrangement, and, as Linsker comments, they probably minimize
the number of pairs of nearby cells which have different weight values,
although no proof of this is given.
Admittedly, this explanation of how orientation selectivity emerges
is far from rigorous. However, Linsker’s original papers sparked a great
deal of interest, and several papers have been written that analyze his
results. Linsker’s results are nicely formalized by MacKay and Miller,
who show that if the correlation function Q is turned into a matrix (a
minor change in definition), the principle eigenvectors of this matrix
resemble the weight ensembles in the various regimes of cell morphology
that Linsker observed (MacKay and Miller, 1990) . The weight-change equation
(Equation 2) can be viewed as multiplying a vector consisting of the weights
of the connections to a cell by the correlation matrix of the preceding
layer. The eigenvectors of a matrix M are the elements that are changed
only by scalar multiplication when multiplied by M, and the principle eigenvector
is the eigenvector that grows in length the most under multiplication by
M. Thus it makes sense that the final weight configurations are the eigenvectors
of the correlation matrix, because such configurations are the only ones
that will maintain their form over development. MacKay and Miller find
that the principle eigenvectors of Gaussian covariance matrices tend to
be of center-surround form, and that Mexican hat covariance matrices can
have principle eigenvectors that are either center-surround or bilobed
(having a positive stripe next to a negative stripe - i.e., morphologically
similar to orientation selective receptive fields). Although MacKay and
Miller do not state this explicitly, Miller’s comments in another article
(Miller 1990) indicate that the bilobed morphology (which would produce
orientation selectivity) is the principle eigenvector when the Mexican
hat covariance matrix has a narrow positive center (lower zero-crossings).
This is in accord with Linsker’s results, in that Linsker found that oriented
bilobed cells were stable only in the higher levels of his simulations,
when, as discussed above, the Mexican hat covariance function is of just
this form.
The work of MacKay and Miller is additionally important because it
reveals that cell morphologies in a layer of a feed-forward Hebbian networks
are determined by the correlation matrix of activities in the previous
layer. This provides an avenue for exploring self-organization, because
once the activity patterns of a layer of cells is known, the eigenvectors
of the correlation matrix can be computed, and compared to the receptive
fields of cells in the next layer. If simple self-organization occurs,
one might expect some match between the computed eigenvectors and the measured
receptive fields.3
Orientation Columns
In the network described in the previous section, orientation preferences
arose randomly from cell to cell, with no correlation between neighboring
cells. It is a well-known feature of mammalian primary visual cortex that
the orientation preference of cells varies in a continuous fashion over
the cortical surface while remaining constant over displacements perpendicular
to the surface (Hubel and Wiesel, 1963, 1974). The regularity of orientation
distribution prompted Hubel and Wiesel to suggest that the basic unit of
organization in V1 is the “hypercolumn,” a group of columns of cells whose
receptive fields represent the same point in visual space, with each column
of cells having a different orientation and ocularity preference, thus
providing the neural machinery to analyze a single point of the visual
field (Mason and Kandel, 1991).
Linsker found that by adding excitatory lateral connections between
the cells in layer G before the connections from layer F were developed,
the orientation that a cell developed was no longer random, but that cells
of a similar orientation organize into band-like regions. Due to computational
constraints, he did not explore the full parameter space to see how closely
the bands could come to resembling the patterns that are found in various
species. Fortunately, his initial result has been pursued by other researchers,
who have confirmed their robustness (Miller, 1992) . Furthermore, others
have shown that another primary feature of V1, ocular dominance columns,
can also arise from self-organization as a result of competition between
the activity patterns of the two eyes (Miller, Keller, and Stryker, 1989).
These results suggests that the principal features of V1 could result from
very simple self-organization.
Discussion
There are several issues pertaining to Linsker’s simulations that
deserve mention. First, consider the assumptions implicit in Linsker’s
network. His model depends crucially on the afferent connections to each
cell in a layer of his network having a Gaussian distribution about a point
in the previous layer. The retinotopy of the early levels of the visual
system is well established; the important question with regard to Linsker’s
model is whether retinotopy precedes the development of cell properties.
When and how retinotopy arises in biological systems is not yet known,
but there are several methods of developing retinotopy with unsupervised
neural networks (see von der Malsburg 1990, and Hertz, Krogh, and Palmer
1991 for reviews). In any case, this assumption is by far the most reasonable
of those implicit in Linsker’s model.
A second element of the model that is less biologically reasonable
is the uniformity of the cells - each cell can have both excitatory and
inhibitory connections. In particular, each connection is treated identically
by the development equations. Any given connection can assume the full
range of excitatory and inhibitory values. Since it is usually agreed that
neurons are either glutamatergic (excitatory) or GABAergic (inhibitory),
this feature of the model is troubling. Linsker did this simply to speed
the simulations. Since the development equation sends all the connections
in an inhibitory region to the inhibitory limit and all the connections
in an excitatory region to the excitatory limit, were there an even distribution
of excitatory and inhibitory connections throughout a cell’s transfer function
the excitatory connections that ended up lying in the inhibitory region
would be sent to zero, as would the inhibitory connections lying in the
excitatory region. This basically wastes half the connections, which is
why Linsker simplified the model by making each connection nonspecific.
More significant is the fact that retino-geniculate and geniculate-cortical
projections are believed to be purely excitatory, a fact not taken into
account by Linsker’s model. However, Ken Miller has conducted simulations
with a far more biologically plausible model, where the layer corresponding
to the LGN consists of two populations of center-surround cells, ON and
OFF, each of which make only excitatory connections to cells in the layer
representing V1 (Miller 1989). Miller achieves results strikingly similar
to those of Linsker. Miller’s model achieves orientation selectivity via
a mechanism inspired by his models of ocular dominance formation (Miller,
1992). Experiments depriving animals of the input from one eye suggest
that in addition to there being a Hebbian component to synapse formation,
there are also competitive influences: given two groups of correlated inputs,
the strongest activated group may “win out” and develop connections of
the greatest strength (Guillery, 1972). Making this assumption, Miller
shows that when there are separate populations of ON- and OFF-center cells,
and each cell in the next layer (representing V1) initially gets input
from the same local retinotopic area within each population, bilobed receptive
fields develop. This happens in his model because at small retinotopic
distances, cells of the same type (ON or OFF) are correlated, while at
larger distance, cells of opposite type are correlated. Combined with realistic
interactions among his “cortical” cells, Miller finds that this model develops
orientation selective cells whose arrangement within the layer is quite
similar to cortical orientation maps. While this model seems vastly different
from Linsker, the importance of correlation functions remains the crucial
factor, and Miller’s work thus suggests that in spite of the implausibility
of Linsker’s model, his results may reflect a property of neural networks
that could also occur in the brain.
Of key importance, then, to the biological relevance of these models,
is the covariance functions of layer activity in the brain. Limited work
has been done on this, but there is some indication that the appropriate
correlations are present in the visual system (Miller, 1991)
A final feature of the model that is not explicitly grounded in biology
is the layer-by-layer development process. Not enough is known about how
multilayer neuronal systems develop in the brain to evaluate this facet
of the model. Furthermore, similar weight patterns may result even if all
layers are developed simultaneously
Ever since the discovery of orientation selectivity in V1, much research
has been devoted to uncovering the mechanisms which underlie it. While
there is not complete agreement as to these mechanisms, a host of studies
are relevant to evaluating the extent to which Linsker’s results actually
explain orientation selectivity in V1. Here I briefly describe a few of
them. Some of the most well-known studies have been conducted by Sillito,
who showed that orientation selectivity was abolished if inhibition is
blocked with the GABA antagonist bicuculline, suggesting that orientation
selective cells receive both excitatory and inhibitory inputs, both of
which are important for their function (Sillito, 1979). Because LGN cells
are known to make only excitatory synapses with cells in V1, this was interpreted
as showing that orientation selectivity is solely a product of cortical
interactions. However, an alternative interpretation is that the application
of bicuculline to an area of cortex leaves only excitatory connections
between cortical cells. (thanks are due to Ken Miller for pointing this
out to me) This could account for Sillito’s result, in that the cells excited
by lines of a certain orientation would then excite other nearby cortical
cells, causing them to be active in spite of having orientation-tuned inputs
from the LGN. The results of Nelson et al. support this view. They observed
that orientation selectivity remains fully present in cat V1 cells when
only the neuron being recorded from has its inhibitory inputs intracellularly
blocked (Nelson et al. 1994). This suggests that excitatory inputs (which
could presumably be coming from the LGN in agreement with the self-organizing
models) are sufficient to generate orientation selectivity, and that intracortical
inhibitory inputs mediate, if anything, an indirect effect through other
neurons. David Ferster’s research provides additional evidence that patterns
of activity in LGN afferents do indeed play the crucial role in orientation
selectivity that was originally proposed by Hubel and Wiesel. Ferster recorded
intracellularly from cells in V1 while flashing oriented stimuli within
a cell’s receptive field, and by hyperpolarizing the cortical cell he was
recording from to the IPSP reversal potential (done by injecting current
through the recording electrode), Ferster enhanced the cell’s EPSPs while
suppressing its IPSPs. He found that the EPSPs of cortical cells are orientation
tuned, in that rotating the stimulus away from the optimal orientation
produced a decrease in the cell’s EPSP (Ferster, 1986). While this demonstrates
that excitatory connections are sufficient to produce orientation selectivity,
the excitation could conceivably be cortical in origin, since there was
no way for Ferster to determine its source. However, given the substantial
excitatory input that V1 receives from the LGN, it seems likely that Ferster
was observing LGN produced EPSPs, in which case the self-organizing models
would be entirely consistent. While excitation is thus sufficient for orientation-selectivity,
a role for inhibition is suggested by Hata et al., who find through a cross-correlation
analysis evidence for inhibitory interactions between two simultaneously
recorded neurons with slightly different orientation preferences, perhaps
implicating inhibitory interneurons in the formation of orientation columns
or in the fine-tuning of responses. (Hata et al., 1988). In summary, while
there is evidence that both inhibitory and excitatory inputs to cortex
play a role in orientation selectivity, the existing evidence for the mechanisms
of orientation selectivity is consistent with the assumptions that are,
broadly speaking, made by the self-organizing models.
Implications of the Self-Organization Research
Linsker’s work is intriguing because cells with spatial opponency and
orientation selectivity, properties that have been thought to be of tremendous
computational significance, seem to arise completely automatically in a
situation where their functional utility has no bearing on their development.
Given this observation, several points immediately come to mind. First,
Linsker’s work seems to suggest that orientation selective cells could
arise in any network that has sufficiently deep correlation functions in
a layer of spatial-opponent cells. Because he trained his network on completely
unstructured input, there is nothing inherently “visual” about his result.
Based on his results, one might expect to find cells with oriented receptive-fields
in all sensory systems, because sensory systems have roughly similar initial
structure to the networks Linsker used: they have a topographic input layer,
project to the thalamus, and then project to a primary sensory area. Interestingly,
orientation selective neurons analogous to those in V1 have been reported
in the somatosensory cortex, supporting this (Hyvarinen and Poranen, 1978,
cited in Sur,Garraghty, and Roe, 1988).
Relevant to this is a remarkable study by Sur, Garraghty, and Roe that
“rerouted” the retinal projections of ferret visual systems at birth to
the ferrets’ medial geniculate nucleus, the auditory portion of the thalamus
(Sur,Garraghty, and Roe, 1988). This was accomplished by ablating V1, V2,
and the superior colliculus of the ferrets’ visual systems as well as the
inferior colliculus (the area that the MGN gets most of its projections
from). Doing this causes the retina to project to the MGN and hence to
auditory cortex. They found that the MGN developed visually responsive
cells that were retinotopically organized, and that some of cells had spatially
opponent receptive fields. The primary auditory cortex in such ferrets
(whose connections from the MGN were not altered by the operation) also
developed visually responsive cells, about twenty percent of which were
orientation selective. It may have been the case that only those retinal
cells that had not already established stable connections with the LGN
were available to postoperatively project to the MGN, and thus there may
have been comparatively few projections from the retina to the MGN, which
might account for the low percentage of orientation selective cells. In
any case, this study corroborates one point that Linsker’s work suggests:
namely, that orientation selectivity can arise in any layer of cells that
receives input from a layer of spatial-opponent cells.
A final point that should be mentioned is that Linsker’s network suggests
that mammalian visual systems may have evolved several stages of center-surround
cells in order to deepen the minima of the correlation functions of the
layer preceding V1 such that it could develop orientation selective cells.
The presence of center-surround cells in the LGN when there are spatial
opponent cells in the retina does not currently have an explanation. Although
the four layers of the network in his paper were contrived to be directly
analogous to the levels of the early visual system, Linsker’s analysis
gives a reason why the redundant morphology may actually serve a functional
role.
In conclusion, there are a variety of reasons to think that self-organization
is one way that the brain’s structure is achieved. We have considered the
phenomenon of orientation selectivity in mammalian visual cortex. Linsker’s
self-organizing neural network develops orientation selective cells automatically
and without any environmental cues. Although there are some inconsistencies
with his results and experimental work on the brain, that his network develops
a computationally useful property completely on its own is remarkable.
More biologically realistic models that achieve similar results, such as
those by Ken Miller, make a strong case for the presence of similar principles
of organization in the brain, and there is reason to believe that these
principles may be behind other properties of neurons in the visual system.
Regardless of whether orientation selectivity arises in the brain through
mechanisms analogous to those described here, one of the most important
insights to come out of the self-organization models is the importance
of the covariance function of activities across a layer of cells whenever
any type of Hebbian learning occurs in a system. This principle alone has
great potential for helping to unearth the origins of organizaiton in the
brain. Indeed, if self-organization is a viable means of development, as
it seems to be, then it is probably used with frequency in the brain, and
self-organizing models will be of great use in identifying this.
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1 As I have mentioned and explained, Linsker notes that the Mexican-hat
minima depth can also be increased by lowering the average sum Ýjwij of
all the weights of the afferent connections to a unit i.
2 Recall that the development of layer C depended primarily on the
constants m and p. These are contained in the parameter g which was varied
in these simulations.
3 Clearly, strict feedforward networks do not exist in the brain. (Indeed,
only 10 percent of the inputs to the LGN come from the retina!) Linsker’s
work suggests, however, that many of the receptive field properties of
cells may arise from feedforward interactions.
Josh McDermott '98 (jmcderm@fas.harvard.edu) is a Special Concentrator
in Cognitive Science living in Leverett House. He has broad interests in
vision, computational and cognitive neuroscience, and the neural basis
of and philosophical issues surrounding consciousness.
Harvard Undergraduate Society for Neuroscience
husn@hcs.harvard.edu