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Prototypes and portability in artificial neural network models

Published online by Cambridge University Press:  30 August 2019

Thomas R. Shultz
Affiliation:
Department of Psychology, McGill University, Montreal, Quebec, Canada H3A 1B1 shultz@psych.mcgill.cawww.psych.mcgill.ca/labs/lnsc/html/Lab-Home.html

Abstract

The Page target article is interesting because of apparent coverage of many psychological phenomena with simple, unified neural techniques. However, prototype phenomena cannot be covered because the strongest response would be to the first-learned stimulus in each category rather than to a prototype stimulus or most frequently presented stimuli. Alternative methods using distributed coding can also achieve portability of network knowledge.

Type
Brief Report
Copyright
2000 Cambridge University Press

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