The Evolution of Primate Intelligence

by Scott Rifkin

There may be as many causalities suggested here as there are interested observers, and there may be no foolproof way to distinguish `correct' from `erroneous' suggestions.

- Peters 1976 (in Martin 1984)

Introduction

Primate brain evolution has been a topic of considerable controversy in the past twenty years, especially since the publication of Jerison's Evolution of the Brain and Intelligence (1973). Since that time, research has been directed at uncovering the selection pressures that led to increases in primate brain size. The two main hypotheses advanced to answer the question of why encephalization has occurred focus on foraging strategies and social intelligence (e.g. Clutton-Brock and Harvey 1980, Byrne and Whiten 1988). The two are not necessarily exclusive, but both claim dominion over the arrow of causality. Doubtless no one factor led exclusively to larger brains. Dunbar attempted to integrate the ecological and the social arguments in a hypothesis about the evolution of group size (1993), and Sawaguchi (1992) proposed a paradigmatic synthesis in his notion that brain evolution has proceeded along multiple, parallel paths, but the interaction between various socio-ecological factors has yet to be determined.

This interaction has tremendous implications for research because it cannot be untangled after the relationships between brain changes and the factors have been set. The measure of brain size used depends upon assumptions about the relative importance of different factors, and different measures lead to incomparable data. To solve this problem, researchers must define what they are measuring and must identify the category of stimuli on which their research focusses. If they intend to arrive at some measure of intelligence, they must determine the salience of that category to the animal, for differential salience of stimuli defines the animal's ecology, and in that way will determine the nature of its intelligence.[1]

Measuring the Brain

An accurate measure of "brainpower" is central to assessing evolutionary implications of selection events. However, scientists only have a rudimentary understanding of how physical characteristics map onto mental ones; researchers simply do not know how best to measure cognitive ability across species, but a measure of brain size is a good first approximation until more further research can suggest better characteristics. Various measures of brain size have been used throughout the literature, and although some have been generally dismissed, many are still in widespread use. Difficulties in measuring brain size are methodological and conceptual, and because studies of encephalization are comparative studies, lack of standardization in brain size measurement can undermine the validity of the field. Three interrelated methodological problems associated with a reference point from which to compare taxa weaken cross-study comparisons: the anatomical variable used to control for ontogenetic variation; the taxon used as a base taxon; and the assumed normal allometric relationship between variables (Harvey and Krebs 1990).

Larger animals tend to have larger brains than smaller animals, so absolute brain

size is not an accurate comparative measure.[2] Instead, researchers can either limit their comparisons to animals of similar body sizes, or control for the effect of body size in order to parse out ontogenetic influences on brain size (Harvey and Krebs 1990). Is body size the best choice for a reference variable? Body weight, controlled by proximal ecological factors, can fluctuate to a significant degree throughout an animal's life, although its range may be limited by ultimate considerations. Brain size, however, remains relatively constant in adulthood. The problem lies not in accurately assessing an animal's body weight, but in the evolutionary implications of the differences in variability between the body weight and brain size. The stability of brain size compared to the malleability of body size suggests that brain size evolution may be a more conservative process than body size evolution. If this is the case, then some measure is needed to control for this difference in rate of evolution between the two structures (Deacon 1990; Jerison 1973; Harvey and Krebs 1990).

How much of brain size can be attributed to body size? How much results from other factors? Jerison's concept of "extra-cortical neurons" follows a long tradition of attempts to divide brain size into a fraction necessary for somatic maintenance (dependent on body size), and a fraction that reflects actual encephalization -- the neurons developed to deal with extracorporeal pressures (somatic and psychic brain functions after Dubois (1897, 1898) in Deacon 1990 and Jerison 1973).[3] The determination of how much of the brain is dedicated to somatic maintenance is informed by assumptions of how the brain develops (Aiello and Wheeler 1995; Deacon 1990; Dunbar 1992). These assumptions determine the allometric equation used to relate brain size and body size and have a history that dates back to 1867 (Brandt 1867 in Deacon 1990). The issues involved extend across taxa and involve an extraordinary degree of complexity that lies well out of the scope of this investigation (see Deacon 1990; Shea 1988).[4] Thus, in this paper, I will forsake the theoretical battle over the general validity of these allometric equations and concentrate on the dispute between two allometric measures which have very practical implications for the investigation of primate encephalization.

The power function y = bxa where y is the variable trait, x represents body size, a is a measure of the relation between the trait and body size, and b is a constant determined by the value of y when x = 1, expresses the allometry of the trait under examination (Jolly 1985). Allometric analysis distinguishes between ontogenetic effects related to differential body size and actual adaptive shifts in the brain that place an animal on a certain allometric grade.[5] The above dispute centers around the value of a, the slope of the grade: what is the relationship between the trait and body size? Jerison (1973) proposed that a=2/3. This exponent results from curve-fitting to various data sets, a method which assumes regularity, a conservation of function, in the way various taxa regulate somatic processes. Jerison explained his finding by saying that 2/3 represents a surface to volume relation. In essence, the three dimensional world of the body is projected two dimensionally onto the brain (Harvey and Krebs 1990). But what aspect of the body's world is being projected? What is the appropriate measure of "body size"? Passingham (1975) contends that the size of the brain must be evaluated with respect to its inputs and outputs (I/O), thereby alleviating some of the confounds introduced by the interconnectedness between body and brain size and also representing a more accurate picture of what is actually occurring. Passingham advocated using medulla size as the measure of I/O activity in the brain, claiming that the medulla represents fairly well the information-processing system for somatic function, although it does not it comprise entirely (Passingham 1975). However, this approach is confounded by assumptions of evolutionary changes in spinal cord dimensions and therefore must only be applied with extreme caution (Martin 1990).

Metabolic Influence

Another camp contends that brain size scales not to 2/3 but to 0.75 which is the allometric exponent for basal metabolic rate determined by Kleiber (1961 in Martin 1981). Consequently, Martin proposes that the brain size scales to metabolic rate.[6] Since post-natal growth of the brain is limited to expansion of pre-existing neurons and glial cells, Martin claims that brain size of an animal does not scale directly with an individual's metabolic rate but rather varies allometrically with its mother's metabolic rate, and indeed, neonatal brain size strongly correlates with adult brain size suggesting that brain development is limited by pre-natal factors (Martin 1981, Pagel and Harvey 1988).[7] Martin's hypothesis clearly implies that the more energy an animal can invest in her offspring, the larger brains they will have. In such a scenario, length of gestation and the efficiency of the mother's metabolic turnover would play a crucial role in determining neonatal brain size.[8] Support for the effects of metabolic turnover comes from comparisons between placental mammals and birds and reptiles. The latter two are oviparous and thus do not transfer energy as efficiently as do placental mammals. Birds and reptiles exhibit similar allometric exponents, and even though avian brains are up to ten times as large as reptilian brains, the limiting factor seems to be the efficiency of energy transfer: oviparity versus viviparity (Martin 1981). This difference suggests that a thermoregulatory factor may separate taxa into these allometric grades (levels of phenotypic organization). Within placental mammals, a distinction must be drawn between altricial (helpless at birth and requiring parental care) and precocial (requiring little post-natal parental care) mammals, but Martin claims that both conform to an exponent close to 0.75, although they lie on different grades (Martin 1981). This difference in grade between precocial and altricial mammals introduces a new suite of problems involving length of gestation. If maternal metabolic rate varies allometrically with body size, and if neonatal brain size depends on maternal metabolic rate (through maternal body size), then offspring of precocial and altricial female mammals of the same body size should have brains of the same size (Pagel and Harvey 1988). Consequently, such a hypothesis needs to take into account gestation length and perhaps other life-history factors. These will not be reviewed here and are mentioned simply to demonstrate the complexity and tangled interplay between various ecological variables (see Shea 1987, Pagel and Harvey 1988 and references therein).

Given that an exact relationship is not known, linking metabolism and brain size is plausible for two reasons. The continually running brain, which cannot store energy, is an incredibly expensive organ to maintain -- consuming much more energy than would be expected for an organ its size -- and primates devote more of their energy resources to their brains than do most other mammals (Aiello and Wheeler 1995; Dunbar 1992; Armstrong 1983). Because this maintenance depends on the efficiency of an animal's metabolic system, brain size evolution would have been constrained by the amount of energy available for an animal to devote to growing and maintaining a larger brain. However, there is no causation implied in this relationship. Just because an animal can grow a larger brain does not mean it will . For example, the animal could use the extra energy to grow a larger body as has been speculated for the mysticeti (Purves 1988). Thus the argument that metabolism limits brain growth cannot be supported as a causal hypothesis. Energy availability sets bounds on encephalization, but what justification is there to claim that brain grows to these bounds and that these constraints correspond to a reliable measure of mental complexity (Deacon 1990)? Where did selection act? Was there selection for increased energy intake, or some other metabolic change which allowed for increases in brain size? Did the same pressures act upon both brain size and metabolism, causing a coevolution?[9] Did selection for a larger brain force adjustments in the metabolic system? The difference between these possibilities is subtle, and the degree to which brains and metabolic processes coevolved may be impossible to determine with certainty. But the distinction is crucial to untangling the theoretical dilemma that will be outlined below. Which came first? A picture of ancestral primate energy intake and expenditure would shed light on the issue, but data detailed enough to allow for causality determinations from such vertical comparisons would be very difficult to procure. Much research has focussed on horizontal comparisons among living groups of primates, using data from today's diversity to extrapolate back into the past.

Foraging Hypotheses

Researchers examining correlations between ecology and brain size in primates -- including ecological variables such as diet, stratification, activity timing, home range size, and breeding system -- have concluded that grades of encephalization depend on taxonomic family and are correlated with body size and home range (Bauchot and Stephan 1966, Clutton-Brock and Harvey 1980). Intra-family differences reflect dietary differentiation and differences in home range size: foliovores have smaller brain to body ratios and smaller home ranges than do frugivores (Clutton-Brock and Harvey 1980). Two hypotheses were proposed to explain the intra-family differences. Foliovores may simply have large digestive tracts thus elevating their body size without affecting brain size and consequently lowering their brain to body ratio without any implications for relative intelligence, or the differences may relate to the distribution of food: fruits may be more scattered and less predictable spatially and temporally than foliovore food, requiring increased memory ability and consequently a larger brain (Clutton- Brock and Harvey 1980; Deacon 1990). The two possibilities are not exclusive and may indeed be operating at different levels of analysis. Whereas the first deals with developmental issues, the second involves interaction with ecology, implying selective pressures. Why did frugivores specialize to a high-quality diet? Did they need the energy to support a larger brain, or did the brain develop to keep track of fruit needed for other purposes? If the brain developed first, what triggered the process, what selected for the larger brain? For now the discussion will focus on hypotheses that implicate ecological pressures, primarily foraging, in the evolution of the primate brain.

Food-related ecological hypotheses take three forms (after Dunbar 1992). (1) Larger brains reflect a cognitive demand on frugivores to monitor the availability of a temporally and spatially dispersed food supply -- the ephemeral food supply hypothesis (Clutton-Brock and Harvey 1980; Milton 1988). Dunbar proposed to test this by examining the correlation of brain size with the relative importance of fruit in the animal's diet (1992). (2) The dispersed nature of the food supply selects for increased memory capacities -- the mental map hypothesis (Clutton-Brock and Harvey 1980). Memory of stored food items is prevalent throughout the animal kingdom, and so it would be difficult to support the claim that in primates mental maps would drive cognitive evolution. (3) Primate foraging strategies, moreso than strategies of other orders, involve complex extractive techniques requiring extensive sensorimotor coordination presumably subject to cortical control -- the food extraction hypothesis (Gibson 1987). If food requiring extraction were an important resource, such cortex-mediated coordination would be strongly selected for, presumably reflected by a larger cortex. The three hypotheses are not mutually exclusive and may even implicate, in part, different brain structures. Whereas monitoring a food supply and extracting food may implicate cortex-based cognitive abilities, there is good evidence that the hippocampus as well as cortical structures (the prefrontal and parietal lobes) store spatial information and thus would be responsible for constructing mental maps (Sawaguchi 1989, 1992). However, since primate encephalization is primarily a result of increased neocortical size, and since intelligence and `higher' cognitive abilities are generally associated with neocortical function, the neocortex should be used as the relevant brain structure in studying the evolution of intelligence (Jerison 1973; Dunbar 1992; Passingham 1975; Sawaguchi 1989,1990,1991; Sawaguchi and Kudo 1990).[10,11] Because extensive mental maps in themselves are not uncommon, and because the "extra" information they may contain relates to the other two hypotheses, it will not be discussed at length here. Additionally, the levels of difficulty of food-extraction hypothesis are not well-defined, and so that hypothesis will not be evaluated. Consequently, only the first hypothesis will be discussed.

Clutton-Brock and Harvey (1980) argued that frugivores are more encephalized than foliovores because they need to monitor and store information about food resources with which they are not in direct, nor even in daily contact.[12] They would need to evaluate the degree of ripeness of the fruits and fruit location and then develop a plan for harvesting the fruit and subsisting until it is ripe. Foliovores, perhaps because of the nature of the distribution of their food, have much smaller home ranges and so could more easily monitor availability thereby eliminating the need for analogous cognitive structures. Dunbar (1992) plotted the percentage of fruit in an animal's diet against neocortex size and determined that there was no correlation between the two, then claimed that the importance of fruit in the diet is not a possible factor in determining neocortex size. However, the percentage of fruit eaten may not accurately represent the importance of the food in a diet; some fruits may supply essential nutrients, and thus be vitally important, even if they do not make up a large part of the diet. However, Dunbar's test does question the validity of the proffered explanation for differences in brain size between frugivores and foliovores.

Social Hypotheses

Primates are predominantly social animals. Although many prosimians live solitarily, all monkey and apes live in social groups (Jolly 1966; Galdikas 1994). Complex sociality may invoke its own selection pressures, favoring the evolution of social problem solving skills and other social adaptations. What would be the anatomical manifestations of such adaptations? The prefrontal and temporal cortices have been implicated in social interactions, so the degree of development of these areas in different species might reflect different levels of sociality (Sawaguchi 1992). Sociality, however, is a behavioral phenomenon and must be evaluated in behavioral terms. Two likely interconnected ideas have been proposed for investigation: complexity of social behavior and group size (presumably governing quantity of social interactions) (Byrne and Whiten 1988; Harcourt and DeWaal 1992; Cheney and Seyfarth 1990; Dunbar 1992, 1993; Sawaguchi 1988; Clutton-Brock and Harvey 1980).

Social Complexity and Machiavellian Intelligence

The Machiavellian intelligence hypothesis is an extension of speculations, especially of Jolly (1966) and Humphrey (1988), about the almost unique complexity of primate social interactions. In its present formulation, it is similar to a social version of Gibson's extractive foraging idea. Gibson proposed that increased coordination between actions governed by cortical sensorimotor areas in foraging reflected a flexible feeding strategy involving the manipulation of a matrix to acquire the food within (1987). Tool use for the purpose of extracting foods from otherwise inaccessible locations is a striking example of such a foraging strategy. Byrne and Whiten pose the ability to use other individuals as tools, manipulating the social environment in order to meet preconceived goals, is an important factor in the evolution of primate intelligence (1988). Studies of such social manipulation are for the most part confined to single species or groups of related species, in part because of the vagueness of the definition of Machiavellian intelligence itself (Byrne and Whiten 1988; Dunbar 1992). Descriptions generally fall into three subcategories: (1) transmission of novel behaviors (Caro and Hauser 1992 and references therein); (2) deception (Byrne and Whiten 1988 and references therein); and (3) alliance formation (Harcourt and DeWaal 1992 and references therein). The latter two may predicate a knowledge of rank relations between other individuals which is more complicated than simply knowing who is above and below oneself. Moreover, all three may involve altruistic interactions which could vary in kind and complexity, for example, in the time course and the nature of the objects being exchanged. Comparative studies of alliances with respect to cortex size are somewhat more advanced than work in the other two areas, and so only alliance formation will be discussed below.

Alliance formation and maintenance requires an animal to analyze a significant amount of information, including the relations between individuals involved in the alliance as well as their relations to other individuals. Alliances can also operate on different levels of associations, and an individual must be able to weigh and compare the costs and benefits of actions that may differentially affect different levels of alliances (Connor et. al 1992). These nested alliances entail a certain amount of mental sophistication, potentially involving predictions of others' actions before a situation exists. Primates in particular seem to groom their relationships with potential allies before an actual contingency arises (Harcourt 1992). The motivations underlying alliance formation -- and the degree to which intentionality comes into play -- are undoubtedly difficult or even impossible to assess, but it is clear that animals do rely on some base of knowledge about social relationships to guide them. Unfortunately, the paucity of data on alliance formation in non-primates may simply reflect expectations of researchers not to find such social complexity, instead of a real difference between taxa, and so at this stage, conclusions involving comparative social complexity can only be tentative (Harcourt 1992). Connor et. al. (1992) found evidence of nested levels of alliance formation within a population of bottlenose dolphins in Shark Bay, Australia. Because odontocetes, like primates, have relatively large brains, they proposed that there may be a relationship between brain size and social complexity, especially as revealed in alliance formation. Harcourt (1992) hypothesized that primates, more than non-primates, choose their allies based on competitive ability, not necessarily on kin relations as might be supposed. Furthermore, while many animals form alliances against individuals or parties from other groups, primates, more than other taxa, form intragroup alliances which take the form of mutualistic or protective support. These intragroup interactions allow for the manipulation of support -- rank often corresponds to desirability as an ally -- including solicitation, coercion, reciprocation, and friendships (Harcourt 1992). Alliances thereby become not necessarily a means to an end, but rather ends in themselves. However, alliances are competitive in that they do lead to tangible benefits, and so for alliances to be adaptive, these benefits must be contestable. Therefore, alliance formation must not be seen as a strictly social phenomenon; it depends ultimately on the nature of the resources contested, and differences in resource type may explain variation in degrees of alliance formation. Certain types of resources may select for alliance formation, but the degree to which alliances drive encephalization or simply build on preexisting brain structures cannot yet be tested.

Group Size, Social Structure, and the Multiple Factor Hypothesis

If social interactions act as selection pressures, the strength of the pressure probably depends in some way upon the salience of social interactions in an animal's life. An animal that does not interact with others regularly faces a different environment from one surrounded by conspecifics, so social pressures will shape the two differently. Also, the nature of the interactions can be directly related to socio-ecological variables.[13] Whether behavioral changes co-opt anatomical structures or whether social pressures mold ontogeny involves currently untestable causal relationships.

Clutton-Brock and Harvey (1980) found that brain size correlated with home range size in the cercopithecines[14] and that monogamous species have significantly smaller brains than polygynous ones. These two socio-ecological factors are related, for home range varies with troop size, and monogamous species have smaller troops than polygynous ones. Dunbar (1992) claimed that after separating the various related factors, only group size, of a host of behavioral ecology variables, remained important. However, Sawaguchi's work demonstrates that not only do social structure and diet correlate with neocortex size across primates -- separating the primates into three grades -- but also that within each grade selection pressures may be differentially influential (Sawaguchi 1989, 1990, 1992; Sawaguchi and Kudo 1990).

Sawaguchi divided primates depending on their social structure (solitary and troop-making for the prosimians and monogynous and polygynous for the anthropoids), habitat (arboreal, terrestrial), and diet (foliovorous, frugivorous), into congeneric groups in order to eliminate phylogenetic influence.[15] Using indices of `extra' cortical parts (ECIs) (Sawaguchi 1989 after Jerison 1973 and Hofman 1982),[16] relative brain size (RBS) (Sawaguchi 1990 after Clutton-Brock and Harvey 1980), and relative size of the neocortex (RSN) (based on allometry between neocortex volume and brain size) (Sawaguchi and Kudo 1990; Sawaguchi 1992; Dunbar 1993),[17] Sawaguchi examined correlations between brain and neocortex size and social structure and ecology and found that the different cerebral measures gave different results. Only the ECI results and the 1992 RSN results will be discussed here.

Due to inadequate comparative sample sizes, ECI correlations with social structure could not be evaluated for old-world monkeys or for diet and habitat for new-world monkeys. In new-world monkeys, polygynous groups had higher ECIs than monogynous groups, and in old-world monkeys, while terrestrial groups had higher ECIs than arboreal groups, differences in diet were not significant.[18] Furthermore, terrestrial old-world monkeys had larger troop and individual home ranges than arboreal ones. Therefore, based on the extra-cortical indices, Sawaguchi divided the anthropoids (excluding the apes due to a small sample size) into three grades: (1) the old-world terrestrial/ frugivorous/ polygynous monkeys; (2) both old and new world arboreal/ frugivorous/ polygynous monkeys; and (3) the new-world arboreal/ frugivorous/ monogynous monkeys.

RSN, as formulated in Sawaguchi (1992), although potentially confounded by body size effects, is theoretically an appropriate measure of neocortical expansion (Barton 1993). "Neocortical size relative to residual brain size is related to the allocation of brain material to neocortical functions." However, the exact neuroanatomical components reflected by this measure are not yet known (Sawaguchi 1992). With respect to RSN, frugivores displayed higher values than foliovores (contradicting the ECI based finding (1989)), and values for polygynous and monogynous species did not significantly differ. Within the frugivores, however, polygynous species exhibited higher values than monogynous ones. Habitat played no significant role, but troop size was significantly related to RSN.

These results suggest that the factors controlling primate neocortical expansion are not uniform across the order and vary in their strength according to the nature of the animal's environment. This conclusion, that the salience of different factors would depend to a large extent on the interplay between factors in an animal's environment, has too often been overlooked by researchers who tend to lump many primates together without adequately controlling for potential confounds and thus inadvertently test more than one variable at a time, invalidating the results. Sawaguchi (1992) emphasizes this interaction between factors and also delineates the limitations of gross measures of brain function in attributing causal relations stating that intelligence is an amalgamation of different processes:

Both diet and social interactions appear to be associated with the degree of neocortical development in anthropoids. The anthropoid neocortex consists of multiple, parallel circuitries which are involved in multiple parallel functions...Problems arising from foraging may differ from those associated with social interactions, and different neocortical circuitries may be responsible for solving different problems...It is, therefore, likely that multiple, parallel factors associated with diet and social interactions may have been associated with the development of multiple, parallel neocortical circuitries of anthropoids.

Conclusion

The study of the evolution of primate intelligence is still in its infancy. Despite various attempts to gauge differences in intelligence, not enough is known about the functioning and interconnections of areas within the brains of various species to allow for accurate measures of these differences. Although the techniques used for probing the human functioning brain (e.g. fast-NMR, PET) may not work on animals and because there is no a priori reason to expect that brain evolution has proceeded regularly without reorganization of structures or redistribution of functions, an understanding of the functioning of the brains of different species is essential for making highly accurate comparisons. However, these studies have not been done, and may not be conducted for a long time, leaving little choice but to assume regularity in brain evolution (see Appendix). Despite this fundamental limitation on comparative work, researchers have uncovered trends that are accurate on a gross level (e.g. frugivores have larger brains and neocortices than foliovores).

Extensions of measures of brain structures to intelligence must be tenuous and correlative at best until more is known about brain function. Riddell and Corl (1977) found that intelligence, as measured in lab behavioral studies, was highly correlated with extra-cortical indices.[19] However, this correlation does not shed light upon which factors affecting the ECIs are responsible for intelligence. As mentioned above, the brain is an information-processing system acting upon stimuli from inside and outside the body. Without knowledge of how information is stored in the brain, no inferences can be drawn regarding the meaning of the exact size of the brain. Presumably larger brains can store more information, but due to the limits of current knowledge, any formal claims based on this assumption can only be unfounded speculation.[20] Connor et. al. (1992) echo this point in refusing to claim that information-processing abilities should increase based on the number of levels of alliances a species displays. The way the brain categorizes information and the degree to which it can generalize and extrapolate methods of analysis to different situations significantly affects the amount of power needed for computation. On a cellular level, evolution faces a choice between time and connectivity which may be informed by more than extracorporeal stimuli (Deacon 1990). To this end, the studies of metabolic rate constraints are a step in the right direction (e.g. Aiello and Wheeler 1995).

Jerison (1973) assumed that the brain on a cytoarchitectonic level was uniform enough to validate the use of gross measures of brain size as meaningful statistics. While this assumption is highly debatable, more important is Jerison's disclaimer that brain size as a statistic is not equivalent to brain size as a parameter. Parameters are measures such as neuron density, numbers of connections per neuron, axon diameter, and number of glial cells. Jerison hoped that measures of brain size would serve as an accessible correlate of such parameters, admitting that until more is known, brain size should only be used in estimation of parameters. With such a disclaimer, it is entirely reasonable to investigate correlations between ecology and brain size and to speculate upon their implications for behavior. Brain size has increased over evolutionary time, and behavior has become more complex. The two are undeniably linked; as more stimuli become important, as an species' niche broadens, there will be selection for enhanced ability to cope with an increased amount of information. Whether this has taken the form of additional neural mass or the development of flexible constructs allowing for adaptability is not known. The only option is to keep moving forward, to refine research based on new findings from disparate fields, and to approximate when parameters are not known. It is too early to talk of causation, for only additional research will open that question. We must proceed carefully, always aware of the limitations.

From the other direction, while the field of behavioural ecology has blossomed in the last thirty years, especially with regard to primates, cognitive ethology is still being defined. The two are intimately connected, and to the extent that cognitive ethology examines the social behaviour of animals, its groundwork has been laid within behavioural ecology. Certainly only an extreme adherent of either an ecological or social hypothesis would claim that an animal's foraging strategy depends solely upon the nature of the food resource or upon its manoeuvrings within a social context. Which came first? Can we know? Harcourt (1992) points out that while both the social and physical environments probably mandate information-processing ability, the social environment involves strong simultaneous feedback to adaptive strategies. Competition in the social sphere has immediate consequences, and selection for efficient analysis and response to this feedback, whether as adaptability or in another form, would be highly favored.[21] What is the evolutionary relationship between the mechanisms responsible for interpreting and instigating social behavior? Similarly, did foraging strategies and social structures coevolve, or did one drive the evolution of the other? And finally, how did social interactions develop with respect to ecology and social structure? It is unlikely that the evolution of primate intelligence can be definitively shunted into one category or another. While ecology may have provided the initial conditions, cognitive evolution undoubtedly soon spiraled into a complex, interconnected web of adaptation, coevolution, and cooption of cerebral traits to cope with changing ecological and social conditions.

A Philosophical Dilemma

Studies of brain evolution are compelling because of their implications for understanding human evolution. Consequently, researchers are motivated by a desire to find the causes of intelligence. What is intelligence? It is inevitably described with respect to human attributes; we consider ourselves intelligent, and we therefore compare other species to ourselves. This view is legitimized by the fact that humans do have very sophisticated brains, exhibit extraordinarily complex behavior, and cope well in novel situations, generalizing from one problem to another. Unfortunately, criteria applicable to humans are not necessarily appropriate for evaluating traits of other organisms. There is no basis for the assumption that all intelligence is human-like intelligence, nor even for the preconception that all primate intelligence is human-like. To say that intellectual prowess is comparative across species and to use humans as the basis for comparison is a continuation of pre-Darwinian ideas of a scala naturae dealing with intelligence (Deacon 1990).[22] If ranking species in a single phylogenetic line according to criteria based on the extant member is questionable, then certainly since ecological conditions and selection pressures change over time, ranking contemporary species separated by millions of years of evolution based on the traits exhibited by one is unjustifiable. To assume a continuum of intelligence across today's species is incompatible with an evolutionary perspective, and this preconception must not be allowed to guide studies of brain evolution. The information-processing systems of different animals have been designed to respond to different stimuli, diverse "cognitive substrates," and therefore expectations of an interspecific regularity between these IPS and various other body measures are ill-conceived (Deacon 1990). What is lacking is a good definition of intelligence that will allow us to say something about how an animal copes with its own ecology and not how closely it approximates human behavior.[23] There are undeniable trends in the history of life -- towards larger brains in mammals and larger neocortices in primates -- but to generalize correlations of these trends into a concept of intelligence should not be attempted until an accurate definition is developed. Until that time, the most that comparative brain size studies can do is demonstrate correlations and thereby pose questions for scientists who focus on the evolution of species with one of these correlated characteristics.

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