The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of (...) activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed. (shrink)

Computational models will play an important role in our understanding of human higher‐order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher‐order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of (...) models to quantitative experimental data, although valuable for other reasons, do not address these criteria. Further, using analogies with other sciences, the history of cognitive science, and examples from modern‐day research programs, this article identifies five activities that have been demonstrated to play an important role in our understanding of human higher‐order cognition. These include modeling within a cognitive architecture, conducting artificial intelligence research, measuring and expanding a model's ability, finding mappings between the structure of different domains, and attempting to explain multiple phenomena within a single model. (shrink)

Goal‐directed cognition is often discussed in terms of specialized memory structures like the “goal stack.” The goal‐activation model presented here analyzes goal‐directed cognition in terms of the general memory constructs of activation and associative priming. The model embodies three predictive constraints: (1) the interference level, which arises from residual memory for old goals; (1) the strengthening constraint, which makes predictions about time to encode a new goal; and (3) the priming constraint, which makes predictions about the role of cues in (...) retrieving pending goals. These constraints are formulated algebraically and tested through simulation of latency and error data from the Tower of Hanoi, a means‐ends puzzle that depends heavily on suspension and resumption of goals. Implications of the model for understanding intention superiority, postcompletion error, and effects of task interruption are discussed. (shrink)

To understand the mind, we need to draw equally on the fields of cognitive science and neuroscience. But these two fields have very separate intellectual roots, and very different styles. So how can these two be reconciled in order to develop a full understanding of the mind and brain.This is the focus of this landmark new book.

A theme that emerged at the Sheffield Conference with particular force, to my way of thinking, was a new way of recognizing, and then avoiding, a seductive bad idea. One of its many guises is what I have called the Cartesian Theater, but it also appears in the roles of Central Processing, or Central Executive, or Norman and Shallice's SAS, or Fodor's non-modular central arena of belief fixation. What is wrong with this idea is not (just) that it (apparently) postulates (...) an _anatomically_ discernible central region of the brain--maximally non-peripheral, one might say--but that it supposes that there is a functionally identifiable _subsystem_ (however located or even distributed in the brain) that has some all too remarkable competences achieved by some all too remarkable means. There are many routes to it. Here is one that starts off in an excellent direction but then veers off. The mistaken fork is not explicitly endorsed by anybody that I can think of, but I daresay it has covertly influenced a lot of thinking on the topic. (shrink)

Artificial Intelligence as a discipline has gotten bogged down in subproblems of intelligence. These subproblems are the result of applying reductionist methods to the goal of creating a complete artificial thinking mind. In Brooks (1987) 1 have argued that these methods will lead us to solving irrelevant problems; interesting as intellectual puzzles, but useless in the long run for creating an artificial being.