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Abstract
We apply an algorithmic learning model of inquiry to model reasoning carried by experimental subjects in Wason's _Selection Task_ that represents reasoning in the task as computation of a decision tree that supervenes on (partial) semantic representations. We argue that the resulting model improves on previous probabilistic (Bayesian) and pragmatic (Relevance theory) models of the task. In particular, it suggests that subjects' selection could in fact be guided by sophisticated patterns of argumentative reasoning.
| Original language | English |
|---|---|
| Publication status | Accepted/In press - 2013 |
| Event | Second Conference on Games, Interactive Rationality and Learning - Lund, Sweden Duration: 2013 Apr 23 → 2013 Apr 26 |
Conference
| Conference | Second Conference on Games, Interactive Rationality and Learning |
|---|---|
| Country/Territory | Sweden |
| Period | 2013/04/23 → 2013/04/26 |
Subject classification (UKÄ)
- Philosophy
Keywords
- Algorithmic learning
- Interrogative model of inquiry
- Selection task
- Problem-solving
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Fast and Loose: Interpreting Wason's Selection Task (Part I: The Descriptive Case)
Genot, E. & Pärnamets, P., 2013, (Unpublished).Research output: Contribution to conference › Paper, not in proceeding › peer-review
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Information Processing and Constraint Satisfaction in Wason’s Selection Task
Genot, E., 2012, Cognition, reasoning, emotion, Action. CogSc-12. Proceedings of the ILCLI International Workshop on Cognitive Science.. Larrazabal, J. M. (ed.). University of the Basque Country Press, p. 153-162Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review
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