A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing
The evidence is now good that different memory systems mediate the learning of different types of category structures. In particular, declarative memory dominates rule-based (RB) category learning and procedural memory dominates information-integration (II) category learning. For example, several st...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-07-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.00643/full |
| _version_ | 1818935340094717952 |
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| author | Vivian V. Valentin W. Todd eMaddox F. Gregory eAshby |
| author_facet | Vivian V. Valentin W. Todd eMaddox F. Gregory eAshby |
| author_sort | Vivian V. Valentin |
| collection | DOAJ |
| description | The evidence is now good that different memory systems mediate the learning of different types of category structures. In particular, declarative memory dominates rule-based (RB) category learning and procedural memory dominates information-integration (II) category learning. For example, several studies have reported that feedback timing is critical for II category learning, but not for RB category learning – results that have broad support within the memory systems literature. Specifically, II category learning has been shown to be best with feedback delays of 500ms compared to delays of 0 and 1000ms, and highly impaired with delays of 2.5 seconds or longer. In contrast, RB learning is unaffected by any feedback delay up to 10 seconds. We propose a neurobiologically detailed theory of procedural learning that is sensitive to different feedback delays. The theory assumes that procedural learning is mediated by plasticity at cortical-striatal synapses that are modified by dopamine-mediated reinforcement learning. The model captures the time-course of the biochemical events in the striatum that cause synaptic plasticity, and thereby accounts for the empirical effects of various feedback delays on II category learning. |
| first_indexed | 2024-12-20T05:18:37Z |
| format | Article |
| id | doaj.art-0a5ed02a83164363b1681d1bb683a50e |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-20T05:18:37Z |
| publishDate | 2014-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-0a5ed02a83164363b1681d1bb683a50e2022-12-21T19:52:05ZengFrontiers Media S.A.Frontiers in Psychology1664-10782014-07-01510.3389/fpsyg.2014.0064388589A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback TimingVivian V. Valentin0W. Todd eMaddox1F. Gregory eAshby2University of California, Santa BarbaraUniversity of Texas, AustinUniversity of California, Santa BarbaraThe evidence is now good that different memory systems mediate the learning of different types of category structures. In particular, declarative memory dominates rule-based (RB) category learning and procedural memory dominates information-integration (II) category learning. For example, several studies have reported that feedback timing is critical for II category learning, but not for RB category learning – results that have broad support within the memory systems literature. Specifically, II category learning has been shown to be best with feedback delays of 500ms compared to delays of 0 and 1000ms, and highly impaired with delays of 2.5 seconds or longer. In contrast, RB learning is unaffected by any feedback delay up to 10 seconds. We propose a neurobiologically detailed theory of procedural learning that is sensitive to different feedback delays. The theory assumes that procedural learning is mediated by plasticity at cortical-striatal synapses that are modified by dopamine-mediated reinforcement learning. The model captures the time-course of the biochemical events in the striatum that cause synaptic plasticity, and thereby accounts for the empirical effects of various feedback delays on II category learning.http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.00643/fullDopaminecomputational modelingStriatumcategory learningsynaptic plastictyprocedural learning |
| spellingShingle | Vivian V. Valentin W. Todd eMaddox F. Gregory eAshby A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing Frontiers in Psychology Dopamine computational modeling Striatum category learning synaptic plasticty procedural learning |
| title | A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing |
| title_full | A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing |
| title_fullStr | A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing |
| title_full_unstemmed | A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing |
| title_short | A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing |
| title_sort | computational model of the temporal dynamics of plasticity in procedural learning sensitivity to feedback timing |
| topic | Dopamine computational modeling Striatum category learning synaptic plasticty procedural learning |
| url | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.00643/full |
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