Journal article
Models of affective decision making: how do feelings predict choice?
- Abstract:
- Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 521.5KB, Terms of use)
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- Publisher copy:
- 10.1177/0956797616634654
Authors
- Publisher:
- Association for Psychological Science
- Journal:
- Psychological Science More from this journal
- Volume:
- 27
- Issue:
- 6
- Pages:
- 763-775
- Publication date:
- 2016-04-12
- Acceptance date:
- 2016-02-02
- DOI:
- EISSN:
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1467-9280
- ISSN:
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0956-7976
- Language:
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English
- Keywords:
- Pubs id:
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pubs:598456
- UUID:
-
uuid:ee527156-f97f-4f51-a416-4258b799e254
- Local pid:
-
pubs:598456
- Source identifiers:
-
598456
- Deposit date:
-
2016-02-01
- ARK identifier:
Terms of use
- Copyright holder:
- Charpentier et al
- Copyright date:
- 2016
- Rights statement:
- © The Authors 2016. This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
- Licence:
- CC Attribution (CC BY)
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