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Representation and Computation in Cognitive Models
Author(s) -
Forbus Kenneth D.,
Liang Chen,
Rabkina Irina
Publication year - 2017
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/tops.12277
Subject(s) - analogy , cognition , cognitive science , cognitive architecture , representation (politics) , computer science , feature (linguistics) , cognitive model , cognitive psychology , artificial intelligence , psychology , epistemology , philosophy , linguistics , neuroscience , politics , political science , law
One of the central issues in cognitive science is the nature of human representations. We argue that symbolic representations are essential for capturing human cognitive capabilities. We start by examining some common misconceptions found in discussions of representations and models. Next we examine evidence that symbolic representations are essential for capturing human cognitive capabilities, drawing on the analogy literature. Then we examine fundamental limitations of feature vectors and other distributed representations that, despite their recent successes on various practical problems, suggest that they are insufficient to capture many aspects of human cognition. After that, we describe the implications for cognitive architecture of our view that analogy is central, and we speculate on roles for hybrid approaches. We close with an analogy that might help bridge the gap.