Simulation of Concept Acquisition According to Posner's Theory Using Artificial Neural Networks
Dawid Grzegorczyk , Nieznański Marek , Jan Mulawka
AbstractThe 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.
|Book||Romaniuk Ryszard (eds.): Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2011 (Proceedings Volume), vol. 8008, 2011, SPIE, ISBN 9780819485823, 614 pages; 71 papers; N/A|
|project||Research on measurment, circuit and signal theory and electronic circuits and systems. Project leader: Romaniuk Ryszard,
, Phone: +48 22 234 7986, +48 22 234 5360, start date 05-04-2012, planned end date 31-12-2012, end date 30-11-2013, ISE/2012/DS, Completed
|Citation count*||0 (2014-12-28)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.