Evolving process-based models from psychological data using genetic programming

Abstract

The development of computational models to provide explanations of psychological data can be achieved using semi-automated search techniques, such as genetic programming. One challenge with these techniques is to control the type of model that is evolved to be cognitively plausible – a typical problem is that of “bloating”, where continued evolution generates models of increasing size without improving overall fitness. In this paper we describe a system for representing psychological data, a class of process-based models, and algorithms for evolving models. We apply this system to the delayed match-to-sample task. We show how the challenge of bloating may be addressed by extending the fitness function to include measures of cognitive performance

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 76,419

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

  • Only published works are available at libraries.

Similar books and articles

Data models and the acquisition and manipulation of data.Todd Harris - 2003 - Philosophy of Science 70 (5):1508-1517.
Minimal models vs. logic programming: the case of counterfactual conditionals.Katrin Schulz - 2014 - Journal of Applied Non-Classical Logics 24 (1-2):153-168.

Analytics

Added to PP
2016-04-23

Downloads
6 (#1,105,473)

6 months
1 (#452,962)

Historical graph of downloads
How can I increase my downloads?