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

Dawid Grzegorczyk; Marek Nieznański; Jan J. Mulawka

doi:10.1117/12.905429

6 October 2011 Simulation of concept acquisition according to Posner's theory using artificial neural networks

Dawid Grzegorczyk, Marek Nieznański, Jan J. Mulawka

Proceedings Volume 8008, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2011; 80080S (2011) https://doi.org/10.1117/12.905429
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2011, 2011, Wilga, Poland

Abstract

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.

Citation Download Citation

Dawid Grzegorczyk, Marek Nieznański, and Jan J. Mulawka "Simulation of concept acquisition according to Posner's theory using artificial neural networks", Proc. SPIE 8008, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2011, 80080S (6 October 2011); https://doi.org/10.1117/12.905429

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