Evolving understandable cognitive models

Created by W.Langdon from gp-bibliography.bib Revision:1.7644

@InProceedings{lane:2022:ICCM,

• author = "Peter C. R. Lane and Laura K. Bartlett and Noman Javed and Angelo Pirrone and Fernand Gobet",
• title = "Evolving understandable cognitive models",
• booktitle = "Proceedings of the 20th International Conference on Cognitive Modelling",
• year = "2022",
• editor = "T. C. Stewart",
• pages = "176--182",
• month = "11-15 " # jul,
• publisher = "University Park, PA: Applied Cognitive Science Lab, Penn State",
• keywords = "genetic algorithms, genetic programming, cognitive modeling, model visualisation",
• URL = "https://mathpsych.org/session/209",
• URL = "https://researchprofiles.herts.ac.uk/files/46343553/iccm_2022.pdf",
• size = "7 pages",
• 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.",
• 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