Contextual inference underlies the learning of sensorimotor repertoires

Heald, James B.; Lengyel, Máté; Wolpert, Daniel M.

doi:10.1038/s41586-021-04129-3

Cited by 126 publications

(151 citation statements)

References 40 publications

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“…Here, implicit and explicit systems are treated as parallel states that adapt to the same error. A recent inference-based model of motor adaptation ( Heald et al, 2021 ) suggests the possibility that implicit and explicit systems participate in a credit assignment problem: with the explicit state estimating the external perturbation, and implicit state estimating the mismatch between vision and proprioception. This inference model will also produce a competition because both states attempt to sum to total state feedback.…”

Section: Discussionmentioning

confidence: 99%

Competition between parallel sensorimotor learning systems

Albert

Jang

Modchalingam

et al. 2022

eLife

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Sensorimotor learning is supported by at least two parallel systems: a strategic process that benefits from explicit knowledge, and an implicit process that adapts subconsciously. How do these systems interact? Does one system's contributions suppress the other, or do they operate independently? Here we illustrate that during reaching, implicit and explicit systems both learn from visual target errors. This shared error leads to competition such that an increase in the explicit system's response siphons away resources that are needed for implicit adaptation, thus reducing its learning. As a result, steady-state implicit learning can vary across experimental conditions, due to changes in strategy. Furthermore, strategies can mask changes in implicit learning properties, such as its error sensitivity. These ideas, however, become more complex in conditions where subjects adapt using multiple visual landmarks, a situation which introduces learning from sensory prediction errors in addition to target errors. These two types of implicit errors can oppose each other, leading to another type of competition. Thus, during sensorimotor adaptation, implicit and explicit learning systems compete for a common resource: error.

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Section: Discussionmentioning

confidence: 99%

Competition between parallel sensorimotor learning systems

Albert

Jang

Modchalingam

et al. 2022

eLife

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“…For example, associative mechanisms might be accompanied by Bayesian inference processes that carve the world into distinct contexts (Collins and Frank, 2013; Gershman, 2015). Indeed, Heald and colleagues (Heald et al, 2021) recently used this approach to demonstrate how contextual representations in sensorimotor adaptation can allow for the establishment of multiple motor memories; their model suggests that spontaneous recovery and consistency effects emerge due to contextual inferences that likely interact with deliberate changes in explicit strategies (Collins and McDougle, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…For instance, distinct motor memories should be retrieved when a skilled tennis player prepares to hit a service return on a clay court versus a grass court. A topic of considerable discussion in the adaptation literature centers on the constraints underlying effective contextual cues (Heald et al, 2021; Howard et al, 2013). Contextual cues are highly effective when the cue is directly relevant to properties of the movement.…”

Section: Introductionmentioning

confidence: 99%

Contextual effects in sensorimotor adaptation adhere to associative learning rules

Avraham

Taylor

Breska

et al. 2020

Preprint

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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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“…Numerous studies have shown that the rate at which a perturbation is introduced can strongly influence motor memory formation [ 29 – 32 ]. According to a recent model, a new motor memory is formed in response to a rapidly introduced perturbation whereas an existing motor memory is adapted for a gradually introduced perturbation [ 33 ]. Not only did we observe a decrease in generalization away from the training direction, we also observed an ‘uptick’ in generalization in the polar opposite direction to the training direction.…”

Section: Discussionmentioning

confidence: 99%

Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning

2022

Self Cite

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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.

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Contextual inference underlies the learning of sensorimotor repertoires

Cited by 126 publications

References 40 publications

Competition between parallel sensorimotor learning systems

Competition between parallel sensorimotor learning systems

Contextual effects in sensorimotor adaptation adhere to associative learning rules

Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning

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