Evolving understandable cognitive models
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{lane:2022:ICCM,
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author = "Peter C. R. Lane and Laura K. Bartlett and
Noman Javed and Angelo Pirrone and Fernand Gobet",
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title = "Evolving understandable cognitive models",
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booktitle = "Proceedings of the 20th International Conference on
Cognitive Modelling",
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year = "2022",
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editor = "T. C. Stewart",
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pages = "176--182",
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month = "11-15 " # jul,
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publisher = "University Park, PA: Applied Cognitive Science Lab,
Penn State",
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keywords = "genetic algorithms, genetic programming, cognitive
modeling, model visualisation",
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URL = "https://mathpsych.org/session/209",
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URL = "https://researchprofiles.herts.ac.uk/files/46343553/iccm_2022.pdf",
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size = "7 pages",
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abstract = "Cognitive models for explaining and predicting human
performance in experimental settings are often
challenging to develop and verify. We describe a
process to automatically generate the programs for
cognitive models from a user-supplied specification,
using genetic programming (GP). We first construct a
suitable fitness function, taking into account observed
error and reaction times. Then we introduce
post-processing techniques to transform the large
number of candidate models produced by GP into a
smaller set of models, whose diversity can be depicted
graphically and can be individually studied through
pseudo-code. These techniques are demonstrated on a
typical neuro-scientific task, the Delayed Match to
Sample Task, with the final set of symbolic models
separated into two types, each employing a different
attentional strategy.",
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notes = "Centre for Philosophy of Natural and Social Science,
London School of Economics, Houghton Street, London
WC2A 2AE, UK",
- }
Genetic Programming entries for
Peter C R Lane
Laura Bartlett
Noman Javed
Angelo Pirrone
Fernand Gobet
Citations