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Analysing Psychological Data by Evolving Computational Models

  • Peter C. R. LaneEmail author
  • Peter D. Sozou
  • Fernand Gobet
  • Mark Addis
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

We present a system to represent and discover computational models to capture data in psychology. The system uses a Theory Representation Language to define the space of possible models. This space is then searched using genetic programming (GP), to discover models which best fit the experimental data. The aim of our semi-automated system is to analyse psychological data and develop explanations of underlying processes. Some of the challenges include: capturing the psychological experiment and data in a way suitable for modelling, controlling the kinds of models that the GP system may develop, and interpreting the final results. We discuss our current approach to all three challenges, and provide results from two different examples, including delayed-match-to-sample and visual attention.

Notes

Acknowledgements

This research was supported by ESRC Grant ES/L003090/1.

The implementation was written for the Java 7 platform in the Fantom language, and used the ECJ evolutionary computing library (Luke 2013).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Peter C. R. Lane
    • 1
    Email author
  • Peter D. Sozou
    • 2
    • 3
  • Fernand Gobet
    • 3
  • Mark Addis
    • 4
  1. 1.School of Computer Science, University of HertfordshireHertfordshireUK
  2. 2.Centre for Philosophy of Natural and Social ScienceLondonUK
  3. 3.Department of Psychological Sciences, University of LiverpoolLiverpoolUK
  4. 4.Faculty of Arts, Design and Media, Birmingham City UniversityBirminghamUK

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