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Analysing Psychological Data by Evolving Computational Models

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Analysis of Large and Complex Data

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.

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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).

Author information

Authors and Affiliations

  1. School of Computer Science, University of Hertfordshire, College Lane, Hatfield, AL10 9AB, Hertfordshire, UK

    Peter C. R. Lane

  2. Centre for Philosophy of Natural and Social Science, London School of Economics and Political Science, Houghton Street, London, WC2A 2AE, UK

    Peter D. Sozou

  3. Department of Psychological Sciences, University of Liverpool, Bedford Street South, Liverpool, L69 7ZA, UK

    Peter D. Sozou & Fernand Gobet

  4. Faculty of Arts, Design and Media, Birmingham City University, City North Campus, Perry Barr, Birmingham, B42 2SU, UK

    Mark Addis

Authors
  1. Peter C. R. Lane
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  2. Peter D. Sozou
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  3. Fernand Gobet
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  4. Mark Addis
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Corresponding author

Correspondence to Peter C. R. Lane .

Editor information

Editors and Affiliations

  1. Jacobs University Bremen , Bremen, Germany

    Adalbert F.X. Wilhelm

  2. Universität Ulm, Institute of Medical Systems Biology Universität Ulm, Ulm, Baden-Württemberg, Germany

    Hans A. Kestler

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© 2016 Springer International Publishing Switzerland

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Lane, P.C.R., Sozou, P.D., Gobet, F., Addis, M. (2016). Analysing Psychological Data by Evolving Computational Models. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1_50

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