دورية أكاديمية
Computational Mechanisms Underlying Motivation to Earn Symbolic Reinforcers.
| العنوان: | Computational Mechanisms Underlying Motivation to Earn Symbolic Reinforcers. |
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| المؤلفون: | Burk DC; Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892-4415., Taswell C; Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892-4415., Tang H; Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892-4415., Averbeck BB; Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892-4415 averbeckbb@mail.nih.gov. |
| المصدر: | The Journal of neuroscience : the official journal of the Society for Neuroscience [J Neurosci] 2024 Jun 12; Vol. 44 (24). Date of Electronic Publication: 2024 Jun 12. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Society for Neuroscience Country of Publication: United States NLM ID: 8102140 Publication Model: Electronic Cited Medium: Internet ISSN: 1529-2401 (Electronic) Linking ISSN: 02706474 NLM ISO Abbreviation: J Neurosci Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Washington, DC : Society for Neuroscience Original Publication: [Baltimore, Md.] : The Society, c1981- |
| مواضيع طبية MeSH: | Motivation*/physiology , Reinforcement, Psychology* , Macaca mulatta* , Choice Behavior*/physiology , Reward*, Animals ; Male ; Reaction Time/physiology ; Markov Chains ; Female |
| مستخلص: | Reinforcement learning is a theoretical framework that describes how agents learn to select options that maximize rewards and minimize punishments over time. We often make choices, however, to obtain symbolic reinforcers (e.g., money, points) that are later exchanged for primary reinforcers (e.g., food, drink). Although symbolic reinforcers are ubiquitous in our daily lives, widely used in laboratory tasks because they can be motivating, mechanisms by which they become motivating are less understood. In the present study, we examined how monkeys learn to make choices that maximize fluid rewards through reinforcement with tokens. The question addressed here is how the value of a state, which is a function of multiple task features (e.g., the current number of accumulated tokens, choice options, task epoch, trials since the last delivery of primary reinforcer, etc.), drives value and affects motivation. We constructed a Markov decision process model that computes the value of task states given task features to then correlate with the motivational state of the animal. Fixation times, choice reaction times, and abort frequency were all significantly related to values of task states during the tokens task ( n = 5 monkeys, three males and two females). Furthermore, the model makes predictions for how neural responses could change on a moment-by-moment basis relative to changes in the state value. Together, this task and model allow us to capture learning and behavior related to symbolic reinforcement. Competing Interests: The authors declare no competing financial interests. (Copyright © 2024 Burk et al.) |
| التعليقات: | Update of: bioRxiv. 2023 Oct 11:2023.10.11.561900. doi: 10.1101/2023.10.11.561900. (PMID: 37873311) |
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| معلومات مُعتمدة: | ZIA MH002928 United States ImNIH Intramural NIH HHS |
| فهرسة مساهمة: | Keywords: Markov decision process; computational modeling; learning; motivation; reinforcement learning; reward |
| تواريخ الأحداث: | Date Created: 20240426 Date Completed: 20240612 Latest Revision: 20240615 |
| رمز التحديث: | 20240615 |
| مُعرف محوري في PubMed: | PMC11170943 |
| DOI: | 10.1523/JNEUROSCI.1873-23.2024 |
| PMID: | 38670805 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1529-2401 |
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| DOI: | 10.1523/JNEUROSCI.1873-23.2024 |