University of Hertfordshire

By the same authors

Evolving Understandable Cognitive Models

Research output: Contribution to conferencePaperpeer-review

Documents

  • iccm-2022

    Accepted author manuscript, 422 KB, PDF document

  • Peter Lane
  • Laura Bartlett
  • Noman Javed
  • Angelo Pirrone
  • Fernand Gobet
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Original languageEnglish
Number of pages7
Publication statusPublished - 2022
EventInternational Conference on Cognitive Modelling -
Duration: 11 Jul 2022 → …
Conference number: 20
https://mathpsych.org/conference/9/

Conference

ConferenceInternational Conference on Cognitive Modelling
Period11/07/22 → …
Internet address

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.

ID: 27771947