How Limited Systematicity Emerges: A Computational Cognitive Neuroscience Approach
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O'Reilly, R. C.,
Petrov, A. A.,
Cohen, J. C.,
Lebiere, C. J.,
Herd, S. A., & Kriete, T.
(2014)
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How Limited Systematicity Emerges: A Computational Cognitive Neuroscience Approach.
In P. Calvo & J. Symons (Eds.),
The architecture of cognition: Rethinking Fodor and Pylyshyn's
Systematicity Challenge
(pp. 191-226). Cambridge, MA: MIT Press.
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Abstract:
Is human cognition best characterized in terms of the systematic nature of classical symbol processing systems (as argued by Fodor & Pylyshyn, 1988), or in terms of the context-sensitive, embedded knowledge characteristic of classical connectionist or neural network systems? We attempt to bridge these contrasting perspectives in several ways. First, we argue that human cognition exhibits the full spectrum, from extreme context sensitivity to high levels of systematicity. Next, we leverage biologically-based computational modeling of different brain areas (and their interactions), at multiple levels of abstraction, to show how this full spectrum of behavior can be understood from a computational cognitive neuroscience perspective. In particular, recent computational modeling of the prefrontal cortex / basal ganglia circuit demonstrates a mechanism for variable binding that supports high levels of systematicity, in domains where traditional connectionist models fail. Thus, we find that this debate has helped advance our understanding of human cognition in many ways, and are optimistic that a careful consideration of the computational nature of neural processing can help bridge seemingly opposing viewpoints.
