The influence of task outcome on implicit motor learning

Kim, Hyosub; Parvin, Darius E.; Ivry, Richard B

doi:10.7554/elife.39882

Cited by 108 publications

(151 citation statements)

References 64 publications

(126 reference statements)

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“…Critically, when presented with the deviated trajectory repeatedly over trials, the participant’s reach angle gradually shifts in the direction opposite the clamp (Fig. 1A), and their behavior shows the cardinal signatures of implicit motor adaptation (Morehead et al, 2017; Kim et al, 2018, 2019; Parvin et al, 2018; Avraham et al, 2020; Tsay et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…However, the Rescorla-Wagner model can, in principle, compute any dependent variable (e.g., reaching angle). For all simulations, we set λ to 20° (similar to the observed implicit adaptation asymptotes in other studies, Bond and Taylor, 2015; Morehead et al, 2017; Kim et al, 2018, 2019), β to 0.2, and the three salience parameters ( α ’s) for the movement plan CS, tone CS, and light CS, to, respectively, 0.6, 0.0009, and 0.0009. These divergent salience values reflect our assumption that most of the associative strength of the US would be absorbed by the movement plan CS, given its central relevance to the task goal of reaching to a target.…”

Section: Methodsmentioning

confidence: 99%

“…Adaptation via sensory prediction error is considered to be an implicit form of learning. When properly isolated from other processes that contribute to learning in typical adaptation tasks, such as explicit cognitive strategies (McDougle et al, 2016), implicit adaptation appears to be curiously limited: It does not exhibit savings (Avraham et al, 2020; Haith et al, 2015; Morehead et al, 2015), is generally insensitive to both the magnitude and consistency of errors (Bond and Taylor, 2015; Hutter and Taylor, 2018; Avraham et al, 2019), and appears to be impervious to information relevant to task performance (Mazzoni and Krakauer, 2006; Morehead et al, 2017; but see Leow et al, 2018 and Kim et al, 2019). Moreover, as shown in several recent studies, implicit adaptation fails, at least initially, in producing appropriate changes in behavior in response to other simple perturbations like mirror reversals (i.e., when visual feedback is mirrored across an unseen axis with respect to the veridical limb position), a task that should be natural for a canonical forward model to solve (Hadjiosif et al, 2020; Telgen et al, 2014; Wilterson and Taylor, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Contextual effects in sensorimotor adaptation adhere to associative learning rules

Avraham

Taylor

Breska

et al. 2020

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Two influential paradigms, sensorimotor adaptation and eyeblink conditioning, have deepened our understanding of the theoretical and neural foundations of motor learning, and in particular, the role of the cerebellum. Although there has been some cross-pollination between these two lines of research, they typically operate within distinct theoretical frameworks, with the incremental updating of an internal forward model explaining adaptation, and associative learning processes explaining eyeblink conditioning. Here we ask if a unified framework might be parsimonious, directly linking sensorimotor adaptation to associative learning. Using a task that isolates implicit sensorimotor adaptation, we paired movement-related feedback with neutral auditory or visual cues that served as conditioning stimuli (CSs) to test two key signatures of associative learning-differential conditioning and compound conditioning. We observed clear Pavlovian effects in both cases: Implicit trial-by-trial changes in movement kinematics were reliably modulated by the CSs in the predicted directions. Moreover, after compound conditioning, we observed a robust negative correlation between the responses of individuals to the two elemental CSs of the compound, consistent with the additivity principle posited by the Rescorla-Wagner model of classical conditioning. Computational modelling demonstrates that these results cannot be captured by the conventional algorithm used to explain the operation of a forward model. We believe that associative learning effects in implicit sensorimotor adaptation provide a proof-of-concept for linking multiple motor learning paradigms within a similar theoretical framework.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Contextual effects in sensorimotor adaptation adhere to associative learning rules

Avraham

Taylor

Breska

et al. 2020

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“…Laboratory-tasks are usually designed to look or characterise a specific learning mechanism (which is being studied) for all subjects, using different types of feedback and perturbation manipulations (Huang et al, 2011; e.g. Galea et al, 2015; Kim et al, 2019). In contrast, the way we started to study real-world motor learning here, which mechanisms are used and to what extent is unknown a priori.…”

Section: Discussionmentioning

confidence: 99%

Brain activity reveals multiple motor-learning mechanisms in a real-world task

Haar

Faisal

2020

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21Many recent studies found signatures of motor learning in neural beta oscillations (13)(14)(15)(16)(17)(18)(19)(20)(21)(22) 30Hz), and specifically in the post-movement beta rebound (PMBR). All these studies were in 23 simplified laboratory-tasks in which learning was either error-based or reward-based. Interestingly, 24 these studies reported opposing dynamics of the PMBR magnitude over learning for the error-based 25 and reward-based tasks (increase verses decrease, respectively). Here we explored the PMBR 26 dynamics during real-world motor-skill-learning in a billiards task using mobile-brain-imaging. 27Our EEG recordings highlight opposing dynamics of PMBR magnitudes between different subjects 28 performing the same task. The groups of subjects, defined by their neural-dynamics, also showed 29 behavioral differences expected for error-based verses reward-based learning. Our results suggest 30 that when faced with the complexity of the real-world different subjects might use different learning 31 mechanisms for the same complex task. We speculate that all subjects combine multi-modal 32 mechanisms of learning, but different subjects have different predominant learning mechanisms. 33 recorded with the DSI-24 there were 6 PMBR Increasers and 4 PMBR Decreasers. 126Correspondingly, there was no correlation between the system and the PMBR-Error correlation 127 (Spearman rank correlation r=0.01, p=0.97). 128Based on the EEG data, which suggests two groups of subjects with different PMBR 129 dynamics, we looked for behavioral signatures in the task performance of different learning 130 between these groups. The lag-1 autocorrelation (ACF(1)) of the performance measure (in our case, 131 the directional error of the target-ball relative to the pocket) was suggested as an index of 132 performance in motor-skill learning, where close to zero values corresponds to high skill (van Beers 133 et al., 2013). We calculated for each subject the ACF(1) in the first and the second half of the 134 training session (blocks 1-6 and 7-12, respectively). The ACF(1) values of both groups were 135 significantly greater than zero during both halves of the session (t test p<0.01), as expected for 136 naïve participance ( Figure 3A). But, the decay in the ACF(1) from the first half of the training 137 session to the second was significantly higher for the PMBR Decreasers (t test p<0.01, Figure 3B). 138Additionally, the decay in the intertrial variability (measured from the first block (trials 1-25) to 139 the learning plateau (trials 201-300)) was also significantly larger in the PMBR Decreasers (t test

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“…Explicit reward feedback has been shown to enhance the retention of error-based learning, while punishment feedback accelerates acquisition (Shmuelof et al 2012;Galea et al 2015;Kuling et al 2019). Sensory error-based learning can also be modulated by implicit reward or task errors related to whether feedback hits or misses visual targets (Kim, Parvin, and Ivry 2019;Leow et al 2018). Reinforcement learning and sensory error-based learning can also contribute to adaptation as separable processes.…”

Section: Introductionmentioning

confidence: 99%

Null effects of levodopa on reward- and error-based motor adaptation, savings, and anterograde interference

et al. 2020

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Dopamine signaling is thought to mediate reward-based learning. We tested for a role of dopamine in motor adaptation by administering the dopamine precursor levodopa to healthy participants in two experiments involving reaching movements. Levodopa has been shown to impair reward-based learning in cognitive tasks. Thus, we hypothesized that levodopa would selectively impair aspects of motor adaptation that depend on reinforcement of rewarding actions.In the first experiment, participants performed two separate tasks in which adaptation was driven either by visual feedback of the hand position or binary reward feedback. We used EEG to measure event-related potentials evoked by task feedback. We hypothesized that levodopa would specifically diminish adaptation and the neural responses to feedback in the reward learning task. However, levodopa did not affect motor adaptation in either task nor did it diminish event-related potentials elicited by reward outcomes.In the second experiment, participants learned to compensate for mechanical force field perturbations applied to the hand during reaching. Previous exposure to a particular force field can result in savings during subsequent adaptation to the same force field and interference during adaptation to an opposite force field. We hypothesized that levodopa would diminish savings and anterograde interference, as previous work suggests that these phenomena result from a reinforcement learning process. However, we found no reliable effects of levodopa.These results suggest that reward-based motor adaptation, savings, and interference may not depend on the same dopaminergic mechanisms which have been shown to be disrupted by levodopa during various cognitive tasks.New and NoteworthyMotor adaptation relies on multiple processes including reinforcement of successful actions. Cognitive reinforcement learning is impaired by levodopa-induced disruption of dopamine function. We administered levodopa to healthy adults who participated in multiple motor adaptation tasks. We found no effects of levodopa on any component of motor adaptation. This suggests that motor adaptation may not depend on the same dopaminergic mechanisms as cognitive forms or reinforcement learning which have been shown to be impaired by levodopa.

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The influence of task outcome on implicit motor learning

Cited by 108 publications

References 64 publications

Contextual effects in sensorimotor adaptation adhere to associative learning rules

Contextual effects in sensorimotor adaptation adhere to associative learning rules

Brain activity reveals multiple motor-learning mechanisms in a real-world task

Null effects of levodopa on reward- and error-based motor adaptation, savings, and anterograde interference

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