Journal
TRENDS IN COGNITIVE SCIENCES
Volume 14, Issue 8, Pages 357-364Publisher
ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.tics.2010.05.004
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Funding
- Economic and Social Research Council [RES-538-28-1001] Funding Source: researchfish
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Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind faces involve induction. The probabilistic approach to modeling cognition begins by identifying ideal solutions to these inductive problems. Mental processes are then modeled using algorithms for approximating these solutions, and neural processes are viewed as mechanisms for implementing these algorithms, with the result being a top-down analysis of cognition starting with the function of cognitive processes. Typical connectionist models, by contrast, follow a bottom-up approach, beginning with a characterization of neural mechanisms and exploring what macro-level functional phenomena might emerge. We argue that the top-down approach yields greater flexibility for exploring the representations and inductive biases that underlie human cognition.
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