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- Contribution Type:
- Abstract
- Author(s):
- M. Forano and D.W. Franklin
- Title:
- The influence of reward on dual-adaptation
- Abstract:
- Introduction Motor learning occurs through a variety of learning mechanisms, including reinforcement learning in which rewards affect participant’s behaviour. A reward is defined as a stimulus administered to an organism following a correct or desired response that increases the probability of occurrence of the response (Montague et al., 2004). Studies that have investigated the effect of reward on motor adaptation show that reward leads to an overall better adaptation, although the specific processes by which reward influences adaptation are unclear. For example, it has been shown that reward occurs through both an increased learning rate and retention rate (Quattrocchi et al., 2017), only through an increased learning rate (Nikooyan et al., 2014), or only through an increased retention rate (Galea et al., 2015). Here, we studied the effect of reward on motor adaptation using our previous design (Forano and Franklin, 2020) by introducing a performance-based reward. Participants adapted to two opposing force fields in an adaptation-deadaptation-error clamp paradigm, while being exposed to performance scaled rewards, where the reward was enhanced as participants reduced their lateral error. Specifically, we test whether reward influences the learning rate or retention rate of slow or fast processes, as well as distinguishing between implicit and explicit learning. Methods Twenty participants performed reaching movements to a target while simultaneously adapting to two opposing force fields (adaptation phase), each associated with a contextual cue (workspace visual location). A de-adaptation phase directly followed where the association between the contextual cues and the force fields were reversed to wash out the previous adaptation. Finally, a consecutive error clamp phase (channel trials) was used to assess the retention. In addition, visual rewards of both points and the presentation of digital faces (emoji) were scaled to participants’ trial by trial performance in an experimental ‘reward’ condition (n=10 participants) and compared to a control group (n=10) where no reward was presented. As participants reduced their horizontal error (induced by the external force fields), the reward showed a happier face and the participants received higher numbers of points. Additional instructed channel trials, where participants were informed of a temporary force field removal, drove participants to not produce any explicit force compensation. In these trials, any measured force compensation allowed us to quantify implicit adaptation component, and therefore dissociate implicit and explicit components from the overall adaptation (Forano et al., 2021). Results In both experimental and control conditions, participants simultaneously adapted to both opposing force fields, reducing the motor error by learning the appropriate compensation to the perturbation. The reward group showed both faster initial adaptation and better retention in the error-clamp phase. However, the major difference in results between the reward and control groups appears to come exclusively from an explicit learning component. Model fitting suggests a higher learning rate and slightly increased retention rate for the reward condition, particularly for the fast process which may be related to explicit learning. Discussion Our experimental and model fitting results bring together previous studies on the increase of learning and retention rate of adaptation, in a dual-adaptation task. However, we additionally show that reward may act primarily through its effect on the explicit component of learning, suggesting that reward might activate higher areas of the brain. Literature Forano, M., & Franklin, D. W. (2020). Timescales of motor memory formation in dual-adaptation. PLoS Computational Biology, 16(10), e1008373. Forano, M., Schween, R., Taylor, J. A., Hegele, M., & Franklin, D. W. (2021). Direct and indirect cues can enable dual adaptation, but through different learning processes. Journal of Neurophysiology, 126(5), 1490-1506. Galea, J. M., Mallia, E., Rothwell, J., & Diedrichsen, J. (2015). The dissociable effects of punishment and reward on motor learning. Nature Neuroscience, 18(4), 597-602. Montague, P. R., Hyman, S. E., & Cohen, J. D. (2004). Computational roles for dopamine in behavioural control. Nature, 431(7010), 760-767. Nikooyan, A. A., & Ahmed, A. A. (2015). Reward feedback accelerates motor learning. Journal of Neurophysiology, 113(2), 633-646. Quattrocchi, G., Greenwood, R., Rothwell, J. C., Galea, J. M., & Bestmann, S. (2017). Reward and punishment enhance motor adaptation in stroke. Journal of Neurology, Neurosurgery & Psychiatry, 88(9), 730-736.
- Month:
- Sep
- Year:
- 2022
- URL:
- https://www.sport.kit.edu/dvs-Jahrestagung-Sportmotorik-2022/Wissenschaftliches-Programm.php
- Language:
- en
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