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The Architecture of CognitionRethinking Fodor and Pylyshyn's Systematicity Challenge$
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Paco Calvo and John Symons

Print publication date: 2014

Print ISBN-13: 9780262027236

Published to MIT Press Scholarship Online: September 2014

DOI: 10.7551/mitpress/9780262027236.001.0001

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How Limited Systematicity Emerges

How Limited Systematicity Emerges

A Computational Cognitive Neuroscience Approach

(p.191) 8 How Limited Systematicity Emerges
The Architecture of Cognition

Randall C. O'Reilly

Alex A. Petrov

Jonathan D. Cohen

Christian J. Lebiere

Seth A. Herd

Trent Kriete

The MIT Press

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.

Keywords:   Systematicity, Context sensitivity, Neural networks, Prefrontal cortex, Basal ganglia, Variable binding

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