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Ling, Charles X.; Marinov, Marin – Cognitive Science, 1994
Challenges Smolensky's theory that human intuitive/nonconscious cognitive processes can only be accurately explained in terms of subsymbolic computations in artificial neural networks. Symbolic learning models of two cognitive tasks involving nonconscious acquisition of information are presented: learning production rules and artificial finite…
Descriptors: Grammar, Intuition, Learning Processes, Mathematical Formulas
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Hattori, Masasi; Oaksford, Mike – Cognitive Science, 2007
In this article, 41 models of covariation detection from 2 x 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi-coefficient under an extreme rarity assumption, which has been shown to be an important factor in…
Descriptors: Stimuli, Responses, Computer Simulation, Heuristics
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Forbus, Kenneth D.; And Others – Cognitive Science, 1995
Presents MAC/FAC, a model of similarity-based retrieval that attempts to capture psychological phenomena; discusses its limitations and extensions, its relationship with other retrieval models, and its placement in the context of other work on the nature of similarity. Examines the utility of the model through psychological experiments and…
Descriptors: Cognitive Processes, Comparative Analysis, Information Retrieval, Models