Unified Theories of Cognition: modeling cognitive competence

摘要

In his recent text, Unified Theories of Cognition, Allen Newell offers an exciting mixture of theoretical and methodological advice to cognitive scientists on how to begin developing more comprehensive accounts of human problem solving. Newell's perspective is at once both exciting and frustrating. His concept of a unified theory of cognition (UTC), and his attempt to illustrate a UTC with his Soar problem solving architecture, is exciting because it suggests how scientists might use the computational methods of cognitive science and artificial intelligence to formulate and explore both broader and deeper aspects of intelligence in people and in machines. Newell's perspective is equally frustrating because it dictates a behaviorist methodology for evaluating cognitive models. Newell views a UTC as a simulation of behavior. I explore the surprising similarity of Newell's approach to theory to the approaches of classical behaviorists such as John Watson and Edward Chace Tolman. I suggest that Newell's behaviorist methodology is incompatible with his commitment to building theories in terms of complex computational systems. I offer a modification to Newell's approach in which a UTC provides an architecture in which to explore the nature of competence—the requisite body of knowledge—that underlies an intelligent agent's ability to perform tasks in a particular domain. I compare this normative perspective to Newell's commitment to performance modeling. I conclude that his key theoretical concepts, such as the problem space hypothesis, knowledge level systems, and intelligence as approximation to the knowledge level are fundamentally competence constructs. I raise specific concerns about the indeterminacy of evaluating a UTC like Soar against performance data. Finally, I suggest that competence modeling more thoroughly exploits the insights of cognitive scientists like Newell and reduces the gap between the aims of cognitive science and artificial intelligence.