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.

Date Published: 6 October 2011
PDF: 9 pages
Proc. SPIE 8008, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2011, 80080S (6 October 2011); doi: 10.1117/12.905429

Published in SPIE Proceedings Vol. 8008:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2011
Ryszard S. Romaniuk, Editor(s)