The cerebellum does more than sensory prediction error-based learning in sensorimotor adaptation tasks

Individuals with damage to the cerebellum perform poorly in sensorimotor adaptation paradigms. This deficit has been attributed to impairment in sensory prediction error-based updating of an internal forward model, a form of implicit learning. These individuals can, however, successfully counter a perturbation when instructed with an explicit aiming strategy. This successful use of an instructed aiming strategy presents a paradox: In adaptation tasks, why do individuals with cerebellar damage not come up with an aiming solution on their own to compensate for their implicit learning deficit? To explore this question, we employed a variant of a visuomotor rotation task in which, before executing a movement on each trial, the participants verbally reported their intended aiming location. Compared with healthy control participants, participants with spinocerebellar ataxia displayed impairments in both implicit learning and aiming. This was observed when the visuomotor rotation was introduced abruptly ( experiment 1) or gradually ( experiment 2). This dual deficit does not appear to be related to the increased movement variance associated with ataxia: Healthy undergraduates showed little change in implicit learning or aiming when their movement feedback was artificially manipulated to produce similar levels of variability ( experiment 3). Taken together the results indicate that a consequence of cerebellar dysfunction is not only impaired sensory prediction error-based learning but also a difficulty in developing and/or maintaining an aiming solution in response to a visuomotor perturbation. We suggest that this dual deficit can be explained by the cerebellum forming part of a network that learns and maintains action-outcome associations across trials. NEW & NOTEWORTHY Individuals with cerebellar pathology are impaired in sensorimotor adaptation. This deficit has been attributed to an impairment in error-based learning, specifically, from a deficit in using sensory prediction errors to update an internal model. Here we show that these individuals also have difficulty in discovering an aiming solution to overcome their adaptation deficit, suggesting a new role for the cerebellum in sensorimotor adaptation tasks.

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The cerebellum does more than sensory-prediction-error-based learning in sensorimotor adaptation tasks

10.1101/139337 ◽

2017 ◽

Cited By ~ 1

Author(s):

Peter A. Butcher ◽

Richard B. Ivry ◽

Sheng-Han Kuo ◽

David Rydz ◽

John W. Krakauer ◽

...

Keyword(s):

Implicit Learning ◽

Prediction Error ◽

Internal Model ◽

Sensorimotor Adaptation ◽

Prediction Errors ◽

Cerebellar Dysfunction ◽

Visuomotor Rotation ◽

Cerebellar Damage ◽

Internal Forward Model ◽

Sensory Prediction

AbstractIndividuals with damage to the cerebellum perform poorly in sensorimotor adaptation paradigms. This deficit has been attributed to impairment in sensory-prediction-error-based updating of an internal forward model, a form of implicit learning. These individuals can, however, successfully counter a perturbation when instructed with an explicit aiming strategy. This successful use of an instructed aiming strategy presents a paradox: In adaptation tasks, why don’t individuals with cerebellar damage come up with an aiming solution on their own to compensate for their implicit learning deficit? To explore this question, we employed a variant of a visuomotor rotation task in which, prior to executing a movement on each trial, the participants verbally reported their intended aiming location. Compared to healthy controls, participants with spinocerebellar ataxia (SCA) displayed impairments in both implicit learning and aiming. This was observed when the visuomotor rotation was introduced abruptly (Exp. 1) or gradually (Exp. 2). This dual deficit does not appear to be related to the increased movement variance associated with ataxia: Healthy undergraduates showed little change in implicit learning or aiming when their movement feedback was artificially manipulated to produce similar levels of variability (Exp. 3). Taken together the results indicate that a consequence of cerebellar dysfunction is not only impaired sensory-prediction-error-based learning, but also a difficulty in developing and/or maintaining an aiming solution in response to a visuomotor perturbation. We suggest that this dual deficit can be explained by the cerebellum forming part of a network that learns and maintains action-outcome associations across trials.New and noteworthyIndividuals with cerebellar pathology are impaired in sensorimotor adaptation. This deficit has been attributed to an impairment in error-based learning, specifically, from a deficit in using sensory prediction errors to update an internal model. Here, we show that these individuals also have difficulty in discovering an aiming solution to overcome their adaptation deficit, suggesting a new role for the cerebellum in sensorimotor adaptation tasks.

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Can patients with cerebellar disease switch learning mechanisms to reduce their adaptation deficits?

Brain ◽

10.1093/brain/awy334 ◽

2019 ◽

Vol 142 (3) ◽

pp. 662-673 ◽

Cited By ~ 17

Author(s):

Aaron L Wong ◽

Cherie L Marvel ◽

Jordan A Taylor ◽

John W Krakauer

Keyword(s):

Prediction Error ◽

Poor Performance ◽

Visuomotor Adaptation ◽

Cerebellar Degeneration ◽

Prediction Errors ◽

Cerebellar Disease ◽

Learning Mechanisms ◽

Visuomotor Rotation ◽

Age Related ◽

Sensory Prediction

Abstract Systematic perturbations in motor adaptation tasks are primarily countered by learning from sensory-prediction errors, with secondary contributions from other learning processes. Despite the availability of these additional processes, particularly the use of explicit re-aiming to counteract observed target errors, patients with cerebellar degeneration are surprisingly unable to compensate for their sensory-prediction error deficits by spontaneously switching to another learning mechanism. We hypothesized that if the nature of the task was changed—by allowing vision of the hand, which eliminates sensory-prediction errors—patients could be induced to preferentially adopt aiming strategies to solve visuomotor rotations. To test this, we first developed a novel visuomotor rotation paradigm that provides participants with vision of their hand in addition to the cursor, effectively setting the sensory-prediction error signal to zero. We demonstrated in younger healthy control subjects that this promotes a switch to strategic re-aiming based on target errors. We then showed that with vision of the hand, patients with cerebellar degeneration could also switch to an aiming strategy in response to visuomotor rotations, performing similarly to age-matched participants (older controls). Moreover, patients could retrieve their learned aiming solution after vision of the hand was removed (although they could not improve beyond what they retrieved), and retain it for at least 1 year. Both patients and older controls, however, exhibited impaired overall adaptation performance compared to younger healthy controls (age 18–33 years), likely due to age-related reductions in spatial and working memory. Patients also failed to generalize, i.e. they were unable to adopt analogous aiming strategies in response to novel rotations. Hence, there appears to be an inescapable obligatory dependence on sensory-prediction error-based learning—even when this system is impaired in patients with cerebellar disease. The persistence of sensory-prediction error-based learning effectively suppresses a switch to target error-based learning, which perhaps explains the unexpectedly poor performance by patients with cerebellar degeneration in visuomotor adaptation tasks.

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Can patients with cerebellar disease switch learning mechanisms to reduce their adaptation deficits?

10.1101/386466 ◽

2018 ◽

Cited By ~ 1

Author(s):

Aaron L. Wong ◽

Cherie L. Marvel ◽

Jordan A. Taylor ◽

John W. Krakauer

Keyword(s):

Spinocerebellar Ataxia ◽

Prediction Error ◽

Visuomotor Adaptation ◽

Cerebellar Degeneration ◽

Prediction Errors ◽

Healthy Controls ◽

Learning Mechanisms ◽

Visuomotor Rotation ◽

Age Related ◽

Sensory Prediction

ABSTRACTSystematic perturbations in motor adaptation tasks are primarily countered by learning from sensory-prediction errors, with secondary contributions from other learning processes. Despite the availability of these additional processes, particularly the use of explicit re-aiming to counteract observed target errors, patients with cerebellar degeneration are surprisingly unable to compensate for their sensory-prediction-error deficits by spontaneously switching to another learning mechanism. We hypothesized that if the nature of the task was changed – by allowing vision of the hand, which eliminates sensory-prediction errors – patients could be induced to preferentially adopt aiming strategies to solve visuomotor rotations. To test this, we first developed a novel visuomotor rotation paradigm that provides participants with vision of their hand in addition to the cursor, effectively setting the sensory-prediction-error signal to zero. We demonstrated in younger healthy controls that this promotes a switch to strategic re-aiming based on target errors. We then showed that with vision of the hand, patients with spinocerebellar ataxia could also switch to an aiming strategy in response to visuomotor rotations, performing similarly to age-matched participants (older controls). Moreover, patients could retrieve their learned aiming solution after vision of the hand was removed, and retain it for at least one year. Both patients and older controls, however, exhibited impaired overall adaptation performance compared to younger healthy controls (age, 18-33), likely due to age-related reductions in spatial and working memory. Moreover, patients failed to generalize, i.e., they were unable to adopt analogous aiming strategies in response to novel rotations, nor could they further improve their performance without vision of the hand. Hence, there appears to be an inescapable obligatory dependence on sensory-prediction-error-based learning – even when this system is impaired in patients with cerebellar degeneration. The persistence of sensory-prediction-error-based learning effectively suppresses a switch to target-error-based learning, which perhaps explains the unexpectedly poor performance by patients with spinocerebellar ataxia in visuomotor adaptation tasks.

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Task Errors Do Not Induce Implicit Sensorimotor Learning

10.1101/2020.11.13.381285 ◽

2020 ◽

Author(s):

Anushka Oza ◽

Adarsh Kumar ◽

Pratik K. Mutha

Keyword(s):

Implicit Learning ◽

Prediction Error ◽

Cognitive Process ◽

Target Location ◽

Prediction Errors ◽

Verbal Instruction ◽

Reaching Task ◽

Task Success ◽

Task Goals ◽

Sensory Prediction

ABSTRACTHumans implicitly adjust their movements when challenged with perturbations that induce sensory prediction errors. Recent work suggests that failure to accomplish task goals could function as a gain on this prediction-error-driven adaptation or could independently trigger additional implicit mechanisms to bring about greater net learning. We aimed to distinguish between these possibilities using a reaching task wherein prediction errors were fixed at zero, but task success was modulated via changes in target location and size. We first observed that task failure caused changes in hand angle that showed classic signatures of implicit learning. Surprisingly however, these adjustments were eliminated when participants were explicitly instructed to ignore task errors. These results fail to support the idea that task errors independently induce implicit learning, and instead endorse the view that they provide a distinct signal to an intentional cognitive process that is responsive to verbal instruction.

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Does proprioceptive acuity influence the extent of implicit sensorimotor adaptation in young and older adults?

Journal of Neurophysiology ◽

10.1152/jn.00636.2020 ◽

2021 ◽

Author(s):

Koenraad Vandevoorde ◽

Jean-Jacques Orban de Xivry

Keyword(s):

Older Adults ◽

Sensory Integration ◽

Prediction Error ◽

Motor Adaptation ◽

Sensorimotor Adaptation ◽

Proprioceptive Information ◽

Age Related ◽

Sensory Prediction

The ability to adjust movements to changes in the environment declines with aging. This age-related decline is caused by the decline of explicit adjustments. However, implicit adaptation remains intact and might even be increased with aging. Since proprioceptive information has been linked to implicit adaptation, it might well be that an age-related decline in proprioceptive acuity might be linked to the performance of older adults in implicit adaptation tasks. Indeed, age-related proprioceptive deficits could lead to altered sensory integration with an increased weighting of the visual sensory-prediction error. Another possibility is that reduced proprioceptive acuity results in an increased reliance on predicted sensory consequences of the movement. Both these explanations led to our preregistered hypothesis: we expected a relation between the decline of proprioception and the amount of implicit adaptation across ages. However, we failed to support this hypothesis. Our results question the existence of reliability-based integration of visual and proprioceptive signals during motor adaptation.

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Task errors contribute to implicit remapping in sensorimotor adaptation

10.1101/263988 ◽

2018 ◽

Cited By ~ 1

Author(s):

Li-Ann Leow ◽

Welber Marinovic ◽

Aymar de Rugy ◽

Timothy J Carroll

Keyword(s):

Sensory Feedback ◽

Hand Position ◽

Sensorimotor Adaptation ◽

Prediction Errors ◽

Reward Prediction ◽

Strategic Processes ◽

The Relationship ◽

Sensory Prediction ◽

Or Task

AbstractPerturbations of sensory feedback evoke sensory prediction errors (discrepancies between predicted and actual sensory outcomes of movements), and reward prediction errors (discrepancies between predicted rewards and actual rewards). Sensory prediction errors result in obligatory remapping of the relationship between motor commands and predicted sensory outcomes. The role of reward prediction errors in sensorimotor adaptation is less clear. When moving towards a target, we expect to obtain the reward of hitting the target, and so we experience a reward prediction error if the perturbation causes us to miss it. These discrepancies between desired task outcomes and actual task outcomes, or “task errors”, are thought to drive the use of strategic processes to restore success, although their role is not fully understood. Here, we investigated the role of task errors in sensorimotor adaptation: during target-reaching, we either removed task errors by moving the target mid-movement to align with cursor feedback of hand position, or enforced task error by moving the target away from the cursor feedback of hand position. Removing task errors not only reduced the rate and extent of adaptation during exposure to the perturbation, but also reduced the amount of post-adaptation implicit remapping. Hence, task errors contribute to implicit remapping resulting from sensory prediction errors. This suggests that the system which implicitly acquires new sensorimotor maps via exposure to sensory prediction errors is also sensitive to reward prediction errors.

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Acoustic stimulation increases implicit adaptation in sensorimotor adaptation

10.1101/2020.10.25.354340 ◽

2020 ◽

Author(s):

Li-Ann Leow ◽

James R. Tresilian ◽

Aya Uchida ◽

Dirk Koester ◽

Tamara Spingler ◽

...

Keyword(s):

Motor Skills ◽

Brain Stimulation ◽

Acoustic Stimulation ◽

Individual Variability ◽

Healthy Adults ◽

Sensorimotor Adaptation ◽

Prediction Errors ◽

Motor Tasks ◽

Non Invasive ◽

Sensory Prediction

AbstractSensorimotor adaptation is an important part of our ability to perform novel motor tasks (i.e., learning of motor skills). Efforts to improve adaptation in healthy and clinical patients using non-invasive brain stimulation methods have been hindered by interindividual and intra-individual variability in brain susceptibility to stimulation. Here, we explore unpredictable loud acoustic stimulation as an alternative method of modulating brain excitability to improve sensorimotor adaptation. In two experiments, participants moved a cursor towards targets, and adapted to a 30° rotation of cursor feedback, either with or without unpredictable acoustic stimulation. Acoustic stimulation improved initial adaptation to sensory prediction errors in Study 1, and improved overnight retention of adaptation in Study 2. Unpredictable loud acoustic stimulation might thus be a potent method of modulating sensorimotor adaptation in healthy adults.

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Both fast and slow learning processes contribute to savings following sensorimotor adaptation

Journal of Neurophysiology ◽

10.1152/jn.00794.2018 ◽

2019 ◽

Vol 121 (4) ◽

pp. 1575-1583 ◽

Cited By ~ 11

Author(s):

Susan K. Coltman ◽

Joshua G. A. Cashaback ◽

Paul L. Gribble

Keyword(s):

Force Field ◽

Motor Adaptation ◽

Retention Rates ◽

Sensorimotor Adaptation ◽

Prediction Errors ◽

Fast Process ◽

Learning Rates ◽

History Of ◽

Underlying Mechanisms ◽

Sensory Prediction

Recent work suggests that the rate of learning in sensorimotor adaptation is likely not fixed, but rather can change based on previous experience. One example is savings, a commonly observed phenomenon whereby the relearning of a motor skill is faster than the initial learning. Sensorimotor adaptation is thought to be driven by sensory prediction errors, which are the result of a mismatch between predicted and actual sensory consequences. It has been proposed that during motor adaptation the generation of sensory prediction errors engages two processes (fast and slow) that differ in learning and retention rates. We tested the idea that a history of errors would influence both the fast and slow processes during savings. Participants were asked to perform the same force field adaptation task twice in succession. We found that adaptation to the force field a second time led to increases in estimated learning rates for both fast and slow processes. While it has been proposed that savings is explained by an increase in learning rate for the fast process, here we observed that the slow process also contributes to savings. Our work suggests that fast and slow adaptation processes are both responsive to a history of error and both contribute to savings. NEW & NOTEWORTHY We studied the underlying mechanisms of savings during motor adaptation. Using a two-state model to represent fast and slow processes that contribute to motor adaptation, we found that a history of error modulates performance in both processes. While previous research has attributed savings to only changes in the fast process, we demonstrated that an increase in both processes is needed to account for the measured behavioral data.

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Flexible explicit but rigid implicit learning in a visuomotor adaptation task

Journal of Neurophysiology ◽

10.1152/jn.00009.2015 ◽

2015 ◽

Vol 113 (10) ◽

pp. 3836-3849 ◽

Cited By ~ 97

Author(s):

Krista M. Bond ◽

Jordan A. Taylor

Keyword(s):

Implicit Learning ◽

Computational Models ◽

Visuomotor Adaptation ◽

Task Demands ◽

Similar Degree ◽

Explicit Learning ◽

Prediction Errors ◽

Visuomotor Rotation ◽

Indirect Measures ◽

Task Conditions

There is mounting evidence for the idea that performance in a visuomotor rotation task can be supported by both implicit and explicit forms of learning. The implicit component of learning has been well characterized in previous experiments and is thought to arise from the adaptation of an internal model driven by sensorimotor prediction errors. However, the role of explicit learning is less clear, and previous investigations aimed at characterizing the explicit component have relied on indirect measures such as dual-task manipulations, posttests, and descriptive computational models. To address this problem, we developed a new method for directly assaying explicit learning by having participants verbally report their intended aiming direction on each trial. While our previous research employing this method has demonstrated the possibility of measuring explicit learning over the course of training, it was only tested over a limited scope of manipulations common to visuomotor rotation tasks. In the present study, we sought to better characterize explicit and implicit learning over a wider range of task conditions. We tested how explicit and implicit learning change as a function of the specific visual landmarks used to probe explicit learning, the number of training targets, and the size of the rotation. We found that explicit learning was remarkably flexible, responding appropriately to task demands. In contrast, implicit learning was strikingly rigid, with each task condition producing a similar degree of implicit learning. These results suggest that explicit learning is a fundamental component of motor learning and has been overlooked or conflated in previous visuomotor tasks.

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Dopamine neuron ensembles signal the content of sensory prediction errors

10.1101/723908 ◽

2019 ◽

Cited By ~ 1

Author(s):

Thomas A. Stalnaker ◽

James D. Howard ◽

Yuji K. Takahashi ◽

Samuel J. Gershman ◽

Thorsten Kahnt ◽

...

Keyword(s):

Neural Activity ◽

Prediction Error ◽

Sensory Information ◽

Dopamine Neurons ◽

Prediction Errors ◽

Bold Response ◽

Dopamine System ◽

Sensory Event ◽

Specific Teaching ◽

Sensory Prediction

AbstractDopamine neurons respond to errors in predicting value-neutral sensory information. These data, combined with causal evidence that dopamine transients support sensory-based associative learning, suggest that the dopamine system signals a multidimensional prediction error. Yet such complexity is not evident in individual neuron or average neural activity. How then do downstream areas know what to learn in response to these signals? One possibility is that information about content is contained in the pattern of firing across many dopamine neurons. Consistent with this, here we show that the pattern of firing across a small group of dopamine neurons recorded in rats signals the identity of a mis-predicted sensory event. Further, this same information is reflected in the BOLD response elicited by sensory prediction errors in human midbrain. These data provide evidence that ensembles of dopamine neurons provide highly specific teaching signals, opening new possibilities for how this system might contribute to learning.

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