Model-based prioritization for acquiring protection.
Protection often involves the capacity to prospectively plan the actions needed to mitigate harm. The computational architecture of decisions involving protection remains unclear, as well as whether these decisions differ from other beneficial prospective actions such as reward acquisition. Here we...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010805 |
| _version_ | 1811167247472787456 |
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| author | Sarah M Tashjian Toby Wise Dean Mobbs |
| author_facet | Sarah M Tashjian Toby Wise Dean Mobbs |
| author_sort | Sarah M Tashjian |
| collection | DOAJ |
| description | Protection often involves the capacity to prospectively plan the actions needed to mitigate harm. The computational architecture of decisions involving protection remains unclear, as well as whether these decisions differ from other beneficial prospective actions such as reward acquisition. Here we compare protection acquisition to reward acquisition and punishment avoidance to examine overlapping and distinct features across the three action types. Protection acquisition is positively valenced similar to reward. For both protection and reward, the more the actor gains, the more benefit. However, reward and protection occur in different contexts, with protection existing in aversive contexts. Punishment avoidance also occurs in aversive contexts, but differs from protection because punishment is negatively valenced and motivates avoidance. Across three independent studies (Total N = 600) we applied computational modeling to examine model-based reinforcement learning for protection, reward, and punishment in humans. Decisions motivated by acquiring protection evoked a higher degree of model-based control than acquiring reward or avoiding punishment, with no significant differences in learning rate. The context-valence asymmetry characteristic of protection increased deployment of flexible decision strategies, suggesting model-based control depends on the context in which outcomes are encountered as well as the valence of the outcome. |
| first_indexed | 2024-04-10T16:06:05Z |
| format | Article |
| id | doaj.art-82e68fec3e2f4f838d268acb34235222 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-10T16:06:05Z |
| publishDate | 2022-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-82e68fec3e2f4f838d268acb342352222023-02-10T05:30:48ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-12-011812e101080510.1371/journal.pcbi.1010805Model-based prioritization for acquiring protection.Sarah M TashjianToby WiseDean MobbsProtection often involves the capacity to prospectively plan the actions needed to mitigate harm. The computational architecture of decisions involving protection remains unclear, as well as whether these decisions differ from other beneficial prospective actions such as reward acquisition. Here we compare protection acquisition to reward acquisition and punishment avoidance to examine overlapping and distinct features across the three action types. Protection acquisition is positively valenced similar to reward. For both protection and reward, the more the actor gains, the more benefit. However, reward and protection occur in different contexts, with protection existing in aversive contexts. Punishment avoidance also occurs in aversive contexts, but differs from protection because punishment is negatively valenced and motivates avoidance. Across three independent studies (Total N = 600) we applied computational modeling to examine model-based reinforcement learning for protection, reward, and punishment in humans. Decisions motivated by acquiring protection evoked a higher degree of model-based control than acquiring reward or avoiding punishment, with no significant differences in learning rate. The context-valence asymmetry characteristic of protection increased deployment of flexible decision strategies, suggesting model-based control depends on the context in which outcomes are encountered as well as the valence of the outcome.https://doi.org/10.1371/journal.pcbi.1010805 |
| spellingShingle | Sarah M Tashjian Toby Wise Dean Mobbs Model-based prioritization for acquiring protection. PLoS Computational Biology |
| title | Model-based prioritization for acquiring protection. |
| title_full | Model-based prioritization for acquiring protection. |
| title_fullStr | Model-based prioritization for acquiring protection. |
| title_full_unstemmed | Model-based prioritization for acquiring protection. |
| title_short | Model-based prioritization for acquiring protection. |
| title_sort | model based prioritization for acquiring protection |
| url | https://doi.org/10.1371/journal.pcbi.1010805 |
| work_keys_str_mv | AT sarahmtashjian modelbasedprioritizationforacquiringprotection AT tobywise modelbasedprioritizationforacquiringprotection AT deanmobbs modelbasedprioritizationforacquiringprotection |