Abstract
Most theories explaining how animals form preferences for their actions agree upon a basic outline: animals discover what is preferable through interactions with the world, store this information in memory, and recall it to help them decide what to do in a new situation. However, no single theory currently explains both how preferences are learned, and how they are recalled in a way that is compatible with empirical data. We advance precisely such a proposal in the form of a stochastic choice model where the decision agent learns what to do based on scale-free comparisons between options it observes in the world and at each decision instance recalls a subset of these comparison experiences in a manner that minimizes the metabolic costs of memory recall. In simulation, this model makes qualitatively accurate predictions connecting agent choices with various dynamic choice correlates documented in the literature on choice process models.
Original language | English (US) |
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Title of host publication | Proceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014 |
Publisher | The Cognitive Science Society |
Pages | 1509-1514 |
Number of pages | 6 |
ISBN (Electronic) | 9780991196708 |
State | Published - 2014 |
Event | 36th Annual Meeting of the Cognitive Science Society, CogSci 2014 - Quebec City, Canada Duration: Jul 23 2014 → Jul 26 2014 |
Publication series
Name | Proceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014 |
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Conference
Conference | 36th Annual Meeting of the Cognitive Science Society, CogSci 2014 |
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Country/Territory | Canada |
City | Quebec City |
Period | 7/23/14 → 7/26/14 |
Bibliographical note
Publisher Copyright:© 2014 Proceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014. All rights reserved.
Keywords
- artificial intelligence
- Bayesian modeling
- cognitive science
- computer simulation
- Decision-making
- learning
- mathematical modeling
- memory