Logic Journal of IGPL Advance Access published online on October 30, 2009
Logic Journal of IGPL, doi:10.1093/jigpal/jzp046
A symbolic/subsymbolic interface protocol for cognitive modeling
Princeton Neuroscience Institute, Princeton University, Princeton, NJ.
E-mail: psimen{at}princeton.edu
Department of Psychology, University of Michigan, Ann Arbor, MI.
E-mail: tpolk{at}umich.edu
Researchers studying complex cognition have grown increasingly interested in mapping symbolic cognitive architectures onto subsymbolic brain models. Such a mapping seems essential for understanding cognition under all but the most extreme viewpoints (namely, that cognition consists exclusively of digitally implemented rules; or instead, involves no rules whatsoever). Making this mapping reduces to specifying an interface between symbolic and subsymbolic descriptions of brain activity. To that end, we propose parameterization techniques for building cognitive models as programmable, structured, recurrent neural networks. Feedback strength in these models determines whether their components implement classically subsymbolic neural network functions (e.g., pattern recognition), or instead, logical rules and digital memory. These techniques support the implementation of limited production systems. Though inherently sequential and symbolic, these neural production systems can exploit principles of parallel, analog processing from decision-making models in psychology and neuroscience to explain the effects of brain damage on problem solving behavior.
Key Words: problem solving • production system • neural network • diffusion • decision making • symbolic • subsymbolic
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