Circuit sharing and the implementation of intelligent systems

One of the most foundational and continually contested questions in the cognitive sciences is the degree to which the functional organization of the brain can be understood as modular. In its classic formulation, a module was defined as a cognitive sub-system with (all or most of) nine specific properties; the classic module is, among other things, domain specific, encapsulated (i.e. maintains proprietary representations to which other modules have no access), and implemented in dedicated neural substrates. Most of the examinations—and especially the criticisms—of the modularity thesis have focused on these properties individually, for instance by finding counterexamples in which otherwise good candidates for cognitive modules are shown to lack domain specificity or encapsulation. The current paper goes beyond the usual approach by asking what some of the broad architectural implications of the modularity thesis might be, and attempting to test for these. The evidence does not favor a modular architecture for the cortex. Moreover, the evidence suggests that best way to approach the understanding of cognition is not by analyzing and modelling different functional domains (visual perception, attention, language, motor control, etc.) in isolation from the others, but rather by looking for points of overlap in their neural implementations, and exploiting these to guide the analysis and decomposition of the functions in question. This has significant implications for the question of how to approach the design and implementation of intelligent artifacts in general, and language-using robots in particular.
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