The paper outlines some of the broad architectural implications of the modularity thesis, and reports on an attempt to test for them. The method involved analysing 472 functional magnetic resonance imaging experiments in eight cognitive domains to discover which brain regions co-operated with which others, under what conditions. The results indicate that the same brain regions contribute to functions across various cognitive domains, but in each domain co-operate with one another in different patterns. This does not appear to be compatible with the modularity thesis. The paper discusses the implications of the finding for the best approach to the design and implementation of intelligent systems in general, and of language-using robots in particular. Implications for the best approach to analysing and modelling cognitive functions will also be discussed.

Maintaining adequate performance in dynamic and uncertain settings has been a perennial stumbling block for intelligent systems. Nevertheless, any system intended for real-world deployment must be able to accommodate unexpected change—that is, it must be perturbation tolerant. We have found that metacognitive monitoring and control—the ability of a system to self-monitor its own decision-making processes and ongoing performance, and to make targeted changes to its beliefs and action-determining components—can play an important role in helping intelligent systems cope with the perturbations that are the inevitable result of real-world deployment. In this article we present the results of several experiments demonstrating the efficacy of metacognition in improving the perturbation tolerance of reinforcement learners, and discuss a general theory of metacognitive monitoring and control, in a form we call the metacognitive loop. ||This research is supported in part by the AFOSR and ONR.

This essay describes a general approach to building perturbation-tolerant autonomous systems, based on the conviction that artificial agents should be able to notice when something is amiss, assess the anomaly, and guide a solution into place. This basic strategy of self-guided learning is termed the metacognitive loop; it involves system monitoring, reasoning about, and, when necessary, altering its own decision-making components. This paper (a) argues that equipping agents with a metacognitive loop can help to overcome the brittleness problem, (b) details the metacognitive loop and its relation to ongoing work on time-sensitive commonsense reasoning, (c) describes specific, implemented systems whose perturbation tolerance was improved by adding a metacognitive loop, and (d) outlines both short-term and long-term research agendas.