Foundations & Cognitive Architecture

A third cluster, which cuts across the first two areas and also encompasses other theoretical fields in which RuCCS has particular strength, concerns the question of the architecture of the cognitive system. This includes the nature of the underlying (computational) mechanisms of cognition, as well as the way in which these mechanisms encode information and the way that both the information and the mechanisms may change over time through learning or development. These are among basic theoretical and foundational questions on which RuCCS members have written extensively. For example, Jerry Fodor, Zenon Pylyshyn, Brian McLaughlin and Robert Matthews have written on constraints on Cognitive Architecture, including its modularity and the requirements placed on it by the empirical facts of systematicity and productivity. These writers have argued, among other things, that the representational systems provided by the architecture must meet certain general but powerful constraints which exclude, for example, "prototypes" or other noncompositional representational system, from being by itself adequate to the task. Similarly, Pylyshyn has written extensively on the inadequacy of such special forms of representations as those that we are intuitively drawn to in explaining the nature of mental imagery, known as the "picture theory" of representations underlying mental imagery. Areas in which RuCCS faculty have been leaders involve an analysis of the nature of concepts and their mental representation, the nature of mental representations, and the fundamental problems faced by Connectionist and other nonsymbolic models in addressing the phenomena of human cognition (on which Fodor, Pylyshyn, McLaughlin and Alan Prince have written extensively).

Some RuCCS members or associates are concerned with the problem of representing knowledge in general (Thorne McCarty, Hyam Hirsh, Alex Borgida) or in particular domains, such as language or vision or planning and problem-solving (see the description of relevant research clusters). Some are concerned with understanding how representations change in response to relevant information -- i.e., learning in persons and machines (Hirsh and Schmidt in the case of general knowledge; Fodor, Jane Grimshaw, Matthews, Prince, Karin Stromswold in the case of language; Leslie and Kovacs in the case of the development of the cognitive and visual system in infancy). Others are concerned to show how certain mechanisms are innate and/or universal or how they develop ontogenetically. For example, Alan Leslie has shown that sophisticated cognitive mechanisms are present at very early stages of infancy (e.g. perception of causation and objecthood), whereas other mechanisms take a number of years to develop (e.g. ascribing mental states, such as beliefs and desires, to others). Rochel Gellman’s work on the acquisition of numerical competence falls in this category since it seeks universal cognitive mechanisms that can form the basis of this competence. Other RuCCS members (Ilona Kovacs, Randy Gallistel) have a strong connection with the neuroscience community and are concerned with the question of how behavioral and neural methods and findings can inform one another in the pursuit of more adequate theories.

The study of universals of language and perception also sheds light on the nature of the cognitive architecture. In this vein Grimshaw and Prince have argued that both phonological and syntactic differences among languages can be accounted for by the ordering which they place on a finite set of universal but violable constraints. Stromswold has analyzed empirical evidence showing that certain structures are acquired despite impoverished evidence that lacks negative feedback. Mark Baker has extensively documented the enormous commonality that lies below the surface of the world’s languages. Michael Leyton, Jacob Feldman have shown that certain primitive form properties may be the (possibly innate and universal) basis for the encoding of shape in general.

The development of computational models of cognition raises both philosophical and methodological questions that many RuCCS members have addressed (e.g. Steven Stich, Fodor, Pylyshyn). From the particular perspective of RuCCS researchers, the attempt to build specific computational models in domains such as language comprehension and visual perception (especially in the work of Michael Leyton, Feldman and Manish Singh) goes hand in hand with designing an underlying architecture that satisfy strong computational, linguistic, geometrical and psychological constraints. When this is done then the model's match to the observed human behavior is seen to be an inescapable consequence of that architecture, rather than based on mere mimicry. For example, Grimshaw and Prince have investigated the architectural requirements of a model of human parsing based on principles developed from Prince et al's Optimality Theory.

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