Computational theory-driven studies of reinforcement learning and decision-making in addiction: what have we learned?
| Autor: | Anna B. Konova, Maëlle C M Gueguen, Emma M Schweitzer |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Value (ethics)
Computer science Cognitive Neuroscience Addiction media_common.quotation_subject 05 social sciences Psychological intervention Cognition Article 050105 experimental psychology 03 medical and health sciences Behavioral Neuroscience Psychiatry and Mental health 0302 clinical medicine Theory of computation Reinforcement learning 0501 psychology and cognitive sciences 030217 neurology & neurosurgery media_common Cognitive psychology |
| Zdroj: | Curr Opin Behav Sci |
| ISSN: | 2352-1546 |
| DOI: | 10.1016/j.cobeha.2020.08.007 |
| Popis: | Computational psychiatry provides a powerful new approach for linking the behavioral manifestations of addiction to their precise cognitive and neurobiological substrates. However, this emerging area of research is still limited in important ways. While research has identified features of reinforcement learning and decision-making in substance users that differ from health, less emphasis has been placed on capturing addiction cycles/states dynamically, within-person. In addition, the focus on few behavioral variables at a time has precluded more detailed consideration of related processes and heterogeneous clinical profiles. We propose that a longitudinal and multidimensional examination of value-based processes, a type of dynamic "computational fingerprint", will provide a more complete understanding of addiction as well as aid in developing better tailored and timed interventions. |
| Databáze: | OpenAIRE |
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