Where does value come from?
The computational framework of reinforcement learning (RL) has allowed us to both understand biological brains and build successful artificial agents. However, in this opinion, we highlight open challenges for RL as a model of animal behaviour in natural environments. We ask how the external reward...
| Main Authors: | , |
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| 格式: | Journal article |
| 語言: | English |
| 出版: |
Cell Press
2019
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| _version_ | 1826256975239839744 |
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| author | Juechems, K Summerfield, C |
| author_facet | Juechems, K Summerfield, C |
| author_sort | Juechems, K |
| collection | OXFORD |
| description | The computational framework of reinforcement learning (RL) has allowed us to both understand biological brains and build successful artificial agents. However, in this opinion, we highlight open challenges for RL as a model of animal behaviour in natural environments. We ask how the external reward function is designed for biological systems, and how we can account for the context sensitivity of valuation. We summarise both old and new theories proposing that animals track current and desired internal states and seek to minimise the distance to a goal across multiple value dimensions. We suggest that this framework readily accounts for canonical phenomena observed in the fields of psychology, behavioural ecology, and economics, and recent findings from brain-imaging studies of value-guided decision-making. |
| first_indexed | 2024-03-06T18:10:48Z |
| format | Journal article |
| id | oxford-uuid:02f3af5e-bc2d-49f5-bf1d-f2e3d7e2f545 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:10:48Z |
| publishDate | 2019 |
| publisher | Cell Press |
| record_format | dspace |
| spelling | oxford-uuid:02f3af5e-bc2d-49f5-bf1d-f2e3d7e2f5452022-03-26T08:43:33ZWhere does value come from?Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:02f3af5e-bc2d-49f5-bf1d-f2e3d7e2f545EnglishSymplectic Elements at OxfordCell Press2019Juechems, KSummerfield, CThe computational framework of reinforcement learning (RL) has allowed us to both understand biological brains and build successful artificial agents. However, in this opinion, we highlight open challenges for RL as a model of animal behaviour in natural environments. We ask how the external reward function is designed for biological systems, and how we can account for the context sensitivity of valuation. We summarise both old and new theories proposing that animals track current and desired internal states and seek to minimise the distance to a goal across multiple value dimensions. We suggest that this framework readily accounts for canonical phenomena observed in the fields of psychology, behavioural ecology, and economics, and recent findings from brain-imaging studies of value-guided decision-making. |
| spellingShingle | Juechems, K Summerfield, C Where does value come from? |
| title | Where does value come from? |
| title_full | Where does value come from? |
| title_fullStr | Where does value come from? |
| title_full_unstemmed | Where does value come from? |
| title_short | Where does value come from? |
| title_sort | where does value come from |
| work_keys_str_mv | AT juechemsk wheredoesvaluecomefrom AT summerfieldc wheredoesvaluecomefrom |