225 Evolutionary psychology and the brain Bradley Duchaine*, Leda Cosmides† and John Tooby‡ The human brain is a set of computational machines, each of they solve adaptive problems. This requires theories of which was designed by natural selection to solve adaptive adaptive function. These are engineering specifications, problems faced by our hunter–gatherer ancestors. These which provide analyses of what would count as good machines are adaptive specializations: systems equipped with design for a particular problem [1]. In so doing, they also design features that are organized such that they solve an provide the criteria necessary to decide whether a property ancestral problem reliably, economically and efficiently. The of an organism is a design feature, a functionless search for functionally specialized computational adaptations by-product, a kludge in the system, or noise. has now begun in earnest. A host of specialized systems have recently been found, including ones designed for sexual The functional organization of the brain can be illuminated motivation, social inference, judgment under uncertainty and by applying the same biological theories and principles. The conditioning, as well as content-rich systems for visual task of cognitive neuroscience is to reverse-engineer the recognition and knowledge acquisition. brain: to dissect its computational architecture into function- ally isolable information-processing units, and to determine Addresses how these units operate, both computationally and physically. *‡Department of Psychology, University of California, Santa Barbara, A correct dissection is one that illuminates the design of these CA 93106, USA units and explains their presence. The engineering specifica- *e-mail: [email protected] tions that a good theory of adaptive function provides are ‡e-mail: [email protected] †Center for Evolutionary Psychology, University of California, Santa essential to this enterprise. Theories of adaptive function can Barbara, CA 93106, USA tell one several things. First, they can suggest what kind of computational machines to look for (e.g. units designed for Current Opinion in Neurobiology 2001, 11:225–230 tasks such as: choosing a fertile mate; minimizing contagion; 0959-4388/01/$ — see front matter foraging; caring for children; predicting the trajectories of © 2001 Elsevier Science Ltd. All rights reserved. inanimate objects; predicting the behavior of predators, prey, and other members of one’s own species). Second, they can Introduction tell us what would count as a good design for solving each of In this article, we shall review some of the recent evidence for these problems. Third, they can reveal when a unit designed functionally specialized problem-solving machinery in the for solving one task will be unable to solve another. The brain. We shall also discuss how theories of adaptive function latter is important: when the computational requirements of have been used to uncover their presence and design. two tasks differ, one expects selection to have created a different computational system for accomplishing each —that The phenomenon that Darwin was trying to explain is the is, two different functionally specialized adaptations [2,3••]. presence of functional organization in living systems — the kind of organization that one finds in artifacts that were This expectation is at variance with associationist and designed by an intelligent engineer to solve a problem of other unitarian views of the brain, which many cognitive some kind. Darwin showed how the feedback loops of a neuroscientists have inherited from the parent disciplines blind causal process —natural selection —could create of neuroscience and cognitive psychology. As Gallistel structures that looked like they were designed by an intelli- [4••] puts it, gent engineer to solve a problem. His detailed studies of plants and animals revealed complex structures composed “It is odd but true that most past and contemporary of parts that appeared to be organized to overcome repro- theorizing about learning does not assume that ductive obstacles (e.g. the presence of predators) or to take learning mechanisms are adaptively specialized for advantage of reproductive opportunities (e.g. the presence the solution of particular kinds of problems. Most of fertile mates). And this made sense: although selection theorizing assumes that there is a general-purpose can inject problem-solving machines into the architecture learning process in the brain, a process adapted only of a species, the only problems it can design machines for to solving the problem of learning. There is no solving are ones that had an impact on reproductive rates attempt to formalize what the problem of learning is in the environments in which a species evolved. and thereby determine whether it can in fact be conceived as a single or uniform problem. From a Discovering how to dissect the architecture of a species in biological perspective, this assumption is equiva- a way that illuminates its functional organization and lent to assuming that there is a general-purpose explains its presence has been a foundational task for evo- sensory organ, which solves the problem of sensing.” lutionary biology ever since. To arrive at the appropriate construal, one must conceptualize this architecture as com- Gallistel [4••] has analyzed various learning problems solved posed of nonrandom parts that interact in such a way that by desert ants, bees, pigeons, and other animals, showing: 226 Cognitive neuroscience first, that they are incommensurate; second, that each is Note that although these systems can operate on a wide solved by a different computational machine that is special- array of contents, they are designed for solving problems ized for that task; and third, that associative theories of that arise in a particular domain —foraging —and they are learning are incapable of explaining the animal learning data. specialized for solving foraging problems. Other adaptive We will begin with this last result, because associationist problems, such as learned taste aversions and danger avoid- expectations about the brain persist and continue to organize ance, also rely on the computation of temporal research agendas in cognitive neuroscience, despite demon- contingencies and may use some of the same machinery. strations that associationist mechanisms are not capable of Despite some overlap, however, the solution to these prob- explaining many features of human cognition [4••,5••,6,7]. lems requires additional, functionally specialized machinery (with different brain regions implicated) [11,12]. Selection Content-general systems: functional can specialize the performance of a computational machine specialization and adaptive design by giving it what amounts to innate knowledge about a A computational adaptation can be content-general —that domain, and this appears to have happened in many is, it can operate on information drawn from many different domains that come under the heading of ‘conditioning’ domains —yet still be functionally specialized. Classical [13,14]. This would include the existence of domain- and operant conditioning provide a case in point. According specialized unconditioned stimulus–unconditioned to traditional accounts, conditioning is produced by response (US–UR) relationships (e.g. toxins and enzyme associative mechanisms that track spatio-temporal induction [15], snakes and avoidance [16]), as well as contiguity —the paradigmatic general-purpose learning content-specific, privileged hypotheses about conditioned process. In contrast, adaptationist analyses suggest that the stimulus–unconditioned stimulus (CS–US) relationships mechanisms that produce conditioning are functionally (e.g. taste and nausea [13,17]). Accordingly, some forms of specialized for efficient foraging in the wild [5••]. Rates of conditioning enjoin the neostriatum, others the neocortex reward differ at different foraging sites and under different and amygdala, and yet others the cerebellum [11,12]. foraging conditions: a well-designed mechanism should be sensitive to these differences in rates, detect changes in Content-specific systems: functional them, and take into account the statistical uncertainties specialization and adaptive design inherent in a limited number of observations. Foragers need Another way to specialize and thereby improve the perfor- to compute temporal contingencies, not contiguities. Based mance of a computational machine is to restrict its domain on this evolutionary task analysis, Gallistel and Gibbon [5••] of application. A domain-restricted device can be endowed argue that, formally, this problem corresponds to multivari- with content: with assumptions, privileged hypotheses, and ate nonstationary time-series analysis; they demonstrate that inference procedures that are appropriate to a domain, but associative mechanisms are incapable of performing the may be irrelevant or even misleading when applied outside necessary computations, and provide an alternative compu- that domain. Configural information about the human face tational model that is. Their model predicts many known may help infants to recognize their parents, but will be conditioning phenomena that associationist models cannot useless for recognizing plants; assuming that invisible account for, such as the time-scale invariance of condition- mental states (beliefs and desires) exist may help predict ing, the failure of partial reinforcement to influence the the behavior of people