Analysing psychological data by evolving computational models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{RN5388,
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author = "Peter C. R. Lane and Peter D. Sozou and
Fernand Gobet and Mark Addis",
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title = "Analysing psychological data by evolving computational
models",
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booktitle = "European Conference on Data Analysis (ECDA 2014) and
Workshop on Classification and Subject Indexing in
Library and Information Science (LIS 2014)",
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year = "2014",
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editor = "Adalbert F. X. Wilhelm and Hans A. Kestler",
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series = "Studies in Classification, Data Analysis, and
Knowledge Organization",
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pages = "587--597",
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address = "Jacobs University, Bremen, Germany",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-25224-7",
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URL = "https://link.springer.com/chapter/10.1007/978-3-319-25226-1_50",
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DOI = "doi:10.1007/978-3-319-25226-1_50",
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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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notes = "Analysis of Large and Complex Data, Studies in
Classification, Data Analysis, and Knowledge
Organization, Proceedings of ECDA 2014.
Published 2016",
- }
Genetic Programming entries for
Peter C R Lane
Peter D Sozou
Fernand Gobet
Mark Addis
Citations