From : The Cognitive Neurosciences

The human brain is a biological system produced by the evolutionary process, and so cognitive neuroscience is itself a branch of modem evolutionary biology. Accordingly, cognitive neuroscientists can benefit by acquiring a professional knowledge of the recent technical advances made in evolutionary biology and by applying them to their research. Useful tools include the biologically rigorous concept of function that is appropriate to neural and cognitive systems; a growing list of the specialized functions the human brain evolved to perform; and criteria for distinguishing the narrowly functional aspects of the neural and cognitive architecture that are responsible for the brain's organization from the much larger set of properties that are by-products or noise. With such tools, researchers can construct biologically meaningful experimental tasks and stimuli. These are more likely to activate the large array of functionally dedicated mechanisms that constitute the core of human brain function, but which are at present largely unstudied. Nothing in b i o l o ~ makes sense except in the light of evolution. T. Dobzhamky It is tiu theory which decides what we can observe. -Einstein Seeing with new eyes: Toward an evolutionarily informed cognitive neuroscience The task of cognitive neuroscience is to map the information-processing structure of the human mind, and to discover how this computational organization is implemented in the physical organization of the brain. The central impediment to progress is obvious: The human brain is, by many orders of magnitude, the most complex system humans have yet investigated. Purely as a physical system, the vast intricacy of chemical and electrical interactions among roughly one hundred billion neurons defeats any straightforward attempt to build a comprehensive model, as one might attempt to do with particle collisions, geological processes, protein folding, or host-parasite interactions. At present, the underlying logic of the system seems lost among the torrent of observations that have been accumulated to date, and obscured by the inherent complexity of the system. Historically, however, well-established theories from one discipline have functioned as organs of perception for others. They allow new relationships to be observed and make visible elegant systems of organization that had previously eluded detection. I t seems worth exploring whether the same could be true for the brain sciences. In fact, the brain is more than a physical system: I t is both a computational system and an evolved biological system. Although cognitive neuroscience began with the recognition that studying the brain as a computational system would offer important new insights, the field has so far failed to take equal advantage of the fact that the brain is an evolved system as well. Indeed, the brain is a computational system that was organized and specifically designed to solve a narrowly identifiable set of biological information-processing problems. For this reason, evolutionary biology can supply a key missing element in the cognitive neuroscience research program: a list of the native information-processing functions that the human brain was built to execute. Our computational architecture evolved its distinctive sets of structured information-processing relationships JOHN TOOBY Department of Anthropoiogy, and LEDA COSMIDES Department of Psychology, Center for Evoludevices Or perform this particular tartionary Psychology, University of California, Santa Barbara, geted set of adaptive functions. In turn, our neural Calif. architecture evolved its distinctive physical configuTOOBY AND COSMIDES: MAPPING THE EVOLVED FUNCTIONAL ORGANIZATION 1185 ration because it brought these targeted sets of functional information-processing relationships into existence. By providing the functional engineering specifications to which human brains were built to conform, evolutionary biology can help researchers to isolate, identify, activate, and map the important functional aspects of the cognitive architecture, aspects that would otherwise be lost among the myriad irrelevant phenomena in which they are embedded. The resulting maps of the computational structure of each device will then allow researchers to isolate, identify and map the functional aspects of the neural architecture. The biologically implausible view that the brain is a generalpurpose information-processing system provides little guidance for research in cognitive neuroscience. In contrast, an evolutionary approach allows cognitive neuroscientists to apply a sophisticated body of new knowledge to their problems. In short, because theories and principled systems of knowledge can function as organs of perception, the incorporation of a modern evolutionary framework into cognitive neuroscience may allow the community to detect ordered relationships in phenomena that otherwise seem too complex to be understood. Over the last 30 years, evolutionary biology has made a number of important advances that have not yet diffused into allied hisciplines such as the cognitive and neural sciences. These advances constitute a potent set of new principles relevant to dissecting and understanding the phenomena studied by cognitive neuroscientists (Tooby and Cosmides, 1992). Central to these advances is the modem technical theory of evolution. This consists of the logically derivable set of causal principles that necessarily govern the dynamics of reproducing systems. These principles account for the properties that reproducing systems cumulatively acquire over successive generations. The explicit identification of this core logic has allowed the biological community to develop an increasingly comprehensive set of principles about what kinds of features can and do become incorporated into the designs of reproducing systems down their chains of descent, and what kinds of features do not (Hamilton, 1964, 1972; Maynard Smith, 1964, 1982; Williams, 1966; Dawkins, 1976, 1982, 1986; Cosmides and Tooby 1981; Tooby, 1982). This set of principles has been tested, validated, and enriched through its integration with functional and comparative anatomy, biogeography, genetics, immunology, embryology, behavioral ecology, and a number of other disciplines. Just as the fields of electrical and mechanical engineering summarize our knowledge of principles that govern the design of human-built machines, the field of evolutionary biology summarizes our knowledge of the engineering principles that govern the design of organisms, which can be thought of as machines built by the evolutionary process (for overviews, see Dawkins, 1976, 1982, 1986; Daly and Wilson, 1984; Krebs and Davies, 1987). Modern evolutionary biology constitutes, in effect, a foundational organism design theory, whose principles can be used to fit together research findings into'koherent models of specific cognitive and neural mechanisms. First principles: Reproduction, feedback, and the antientropic construction of organic design Within an evolutionary framework, an organism is describable as a self-reproducing machine, and the defining property of life is the presence in a system of devices or organization that cause the system to construct new and similarly reproducing systems. From this defining property of self-reproduction, the entire deductive structure of modem Darwinism logically follows (Dawkins, 1976; Williams, 1985; Tooby and Cosmides, 1990b). Because the replication of the design of the parental machine is not always error-free, randomly modified designs (i.e., mutants). are introduced into populations of reproducers. Because such machines are highly organized so that they cause the otherwise improbable outcome of constructing offspring machines, the great majority of random modifications will interfere with the complex sequence of actions necessary for self-reproduction. Consequently, such modified designs will tend to remove themselves from the population-a case of negative feedback. However, a small residual subset of design modifications will, by chance, happen to constitute improvements in the design's machinery for causing its own reproduction. Such improved designs (by definition) cause their own frequency to increase in the population-a case of positive feedback. This increase continues until (usually) such modified designs outreproduce and thereby replace all alternative designs in the population, leading to a new species-standard design. After such an event, the population of reproducing machines is different from the ancestral population: The populationor species-standard design has taken a step "uphill" toward a greater degree of functional organization for reproduction. This spontaneous feedback process-natural selection-is the only known process by which functional organization emerges naturally in the world, without intelligent design and intervention. Hence, all naturally occurring functional organization in organisms must be ascribed to its operation and must be consistent with its principles. Over the long run, down chains of descent, this feedback cycle pushes the design of a species stepwise "uphill" toward arrangements of elements that are increasingly improbably well organized to cause their own reproduction in the environment the species evolved in. Because the reproductive fates of the inherited traits that coexist in the same organism are linked together, traits will be selected to enhance each other's functionality (but see Cosmides and Tooby, 1981; Tooby and Cosmides, 1990b, for the relevant genetic analysis and qualifications). Consequently, accumulating design features will tend to fit themselves together sequentially into increasingly functionally elaborated machines for reproduction, composed of constituent mechanisms-called adaptations-that solve problems whose solutions either are necessary for reproduction or increase its likelihood (Darwin, 1859; Williams, 1966, 1985;