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Analysing psychological data by evolving computational models

Lane, Peter C. R., Sozou, Peter D., Gobet, Fernand and Addis, Mark (2016) Analysing psychological data by evolving computational models. In: Wilhelm, Adalbert F.X. and Kestler, Hans A., (eds.) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer International (Firm), Switzerland, pp. 587-597. ISBN 9783319252247

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Identification Number: 10.1007/978-3-319-25226-1_50


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.

Item Type: Book Section
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Additional Information: © 2016 Springer International Publishing Switzerland
Divisions: Philosophy, Logic and Scientific Method
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Date Deposited: 29 Sep 2016 10:14
Last Modified: 16 May 2024 05:44

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