Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning
Motor adaptation can be achieved through error-based learning, driven by sensory prediction errors, or reinforcement learning, driven by reward prediction errors. Recent work on visuomotor adaptation has shown that reinforcement learning leads to more persistent adaptation when visual feedback is re...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159621/?tool=EBI |
| _version_ | 1818232566526771200 |
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| author | Tsuyoshi Ikegami J. Randall Flanagan Daniel M. Wolpert |
| author_facet | Tsuyoshi Ikegami J. Randall Flanagan Daniel M. Wolpert |
| author_sort | Tsuyoshi Ikegami |
| collection | DOAJ |
| description | Motor adaptation can be achieved through error-based learning, driven by sensory prediction errors, or reinforcement learning, driven by reward prediction errors. Recent work on visuomotor adaptation has shown that reinforcement learning leads to more persistent adaptation when visual feedback is removed, compared to error-based learning in which continuous visual feedback of the movement is provided. However, there is evidence that error-based learning with terminal visual feedback of the movement (provided at the end of movement) may be driven by both sensory and reward prediction errors. Here we examined the influence of feedback on learning using a visuomotor adaptation task in which participants moved a cursor to a single target while the gain between hand and cursor movement displacement was gradually altered. Different groups received either continuous error feedback (EC), terminal error feedback (ET), or binary reinforcement feedback (success/fail) at the end of the movement (R). Following adaptation we tested generalization to targets located in different directions and found that generalization in the ET group was intermediate between the EC and R groups. We then examined the persistence of adaptation in the EC and ET groups when the cursor was extinguished and only binary reward feedback was provided. Whereas performance was maintained in the ET group, it quickly deteriorated in the EC group. These results suggest that terminal error feedback leads to a more robust form of learning than continuous error feedback. In addition our findings are consistent with the view that error-based learning with terminal feedback involves both error-based and reinforcement learning. |
| first_indexed | 2024-12-12T11:08:19Z |
| format | Article |
| id | doaj.art-5b3f6b3a24894ab387d8ff733ea11389 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-12T11:08:19Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-5b3f6b3a24894ab387d8ff733ea113892022-12-22T00:26:22ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01176Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learningTsuyoshi IkegamiJ. Randall FlanaganDaniel M. WolpertMotor adaptation can be achieved through error-based learning, driven by sensory prediction errors, or reinforcement learning, driven by reward prediction errors. Recent work on visuomotor adaptation has shown that reinforcement learning leads to more persistent adaptation when visual feedback is removed, compared to error-based learning in which continuous visual feedback of the movement is provided. However, there is evidence that error-based learning with terminal visual feedback of the movement (provided at the end of movement) may be driven by both sensory and reward prediction errors. Here we examined the influence of feedback on learning using a visuomotor adaptation task in which participants moved a cursor to a single target while the gain between hand and cursor movement displacement was gradually altered. Different groups received either continuous error feedback (EC), terminal error feedback (ET), or binary reinforcement feedback (success/fail) at the end of the movement (R). Following adaptation we tested generalization to targets located in different directions and found that generalization in the ET group was intermediate between the EC and R groups. We then examined the persistence of adaptation in the EC and ET groups when the cursor was extinguished and only binary reward feedback was provided. Whereas performance was maintained in the ET group, it quickly deteriorated in the EC group. These results suggest that terminal error feedback leads to a more robust form of learning than continuous error feedback. In addition our findings are consistent with the view that error-based learning with terminal feedback involves both error-based and reinforcement learning.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159621/?tool=EBI |
| spellingShingle | Tsuyoshi Ikegami J. Randall Flanagan Daniel M. Wolpert Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning PLoS ONE |
| title | Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning |
| title_full | Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning |
| title_fullStr | Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning |
| title_full_unstemmed | Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning |
| title_short | Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning |
| title_sort | reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159621/?tool=EBI |
| work_keys_str_mv | AT tsuyoshiikegami reachadaptiontoavisuomotorgainwithterminalerrorfeedbackinvolvesreinforcementlearning AT jrandallflanagan reachadaptiontoavisuomotorgainwithterminalerrorfeedbackinvolvesreinforcementlearning AT danielmwolpert reachadaptiontoavisuomotorgainwithterminalerrorfeedbackinvolvesreinforcementlearning |