Simulation of concept acquisition according to Posner's theory using artificial neural networks

Details Simulation of concept acquisition according to Posner's theory using artificial neural networks Simulation of concept acquisition according to Posner's theory using artificial neural networks

Jun 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011spie.8008e..27g&link_type=abstract

Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2011. Edited by Romaniuk, Ry

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The prototype model of classification assumes that categories are stored in human mind as abstracted summary representations formed in the process of experiencing specimens. Classification of new exemplars is based on their similarity to the abstracted prototype. From studies using Michael Posner's dot-pattern recognition paradigm, we selected several empirical observations, like category size effect, category breadth effect or prototype-exemplar similarity effect, and tested them on artificial neural networks. In this work we show that the properties of human categorization process can be very well simulated and observed on artificial neural networks.

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