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Cognitive modeling with context sensitive reinforcement learning

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Author

Editor

  • J. Malek

Summary, in English

We describe how a standard reinforcement learning algorithm can be changed to include a second contextual input that is used to modulate the learning in the original algorithm. The new algorithm takes the context into account during relearning when the previously learned actions are no longer valid. The algorithm was tested on a number of cognitive experiment and shown to reproduce the learning in both a task switching test and in the Wisconsin Card Sorting Test. In addition, the algorithm was able to learn a context sensitive categorization of objects in the Labov experiment.

Department/s

Publishing year

2004

Language

English

Pages

10-19

Publication/Series

Proceedings of AILS 04 ( Report / Lund Institute of Technology, Lund University ; 151)

Document type

Conference paper

Publisher

Department of Computer Science, Lund University

Topic

  • Computer Vision and Robotics (Autonomous Systems)

Status

Published

Project

  • Ikaros: An infrastructure for system level modelling of the brain

ISBN/ISSN/Other

  • ISSN: 1650-1276