scholarly journals Distinct Processing of Sensory Prediction Error and Task Error during Motor Learning

2021 ◽  
Author(s):  
Jonathan Tsay ◽  
Adrian Haith ◽  
Richard B Ivry ◽  
Hyosub E Kim
Keyword(s):  
Prediction Error ◽  
Sensory Feedback ◽  
Displacement Method ◽  
Visuomotor Learning ◽  
Primary Signal ◽  
Implicit Motor ◽  
Feedback Method ◽  
The Difference ◽  
Sensory Prediction ◽  
And Task

While sensory-prediction error (SPE), the difference between predicted and actual sensory feedback, is recognized as the primary signal that drives implicit motor recalibration, recent studies have shown that task error (TE), the difference between sensory feedback and the movement goal, also plays a modulatory role. To systematically examine how SPE and TE collectively shape implicit recalibration, we performed a series of visuomotor learning experiments, introducing perturbations that varied the size of TE using a popular target displacement method and the size of SPE using a clamped visual feedback method. In Experiments 1 & 2, we observed robust sign-dependent changes in hand angle in response to perturbations with both SPE and TE but failed to observe changes in hand angle in response to TE-only perturbations. Yet in Experiments 3 & 4, the magnitude of TE modulated implicit recalibration in the presence of a fixed SPE. Taken together, these results underscore that implicit recalibration is driven by both SPE and TE (Kim, Parvin, & Ivry, 2019), while specifying unappreciated interactions between these two error-based processes. First, TE only impacts implicit calibration when SPE is present. Second, transient changes occurring when the target is displaced to manipulate TE has an attenuating effect on implicit recalibration, perhaps due to attention being directed away from the sensory feedback.

scholarly journals Does somatosensory acuity influence the extent of internal model recalibration in young and older adults?

2020 ◽  
Author(s):  
Koenraad Vandevoorde ◽  
Jean-Jacques Orban de Xivry
Keyword(s):  
Older Adults ◽  
Sensory Integration ◽  
Prediction Error ◽  
Internal Model ◽  
Sensory Feedback ◽  
Motor Adaptation ◽  
Age Related ◽  
Somatosensory Information ◽  
Implicit Motor ◽  
Sensory Prediction

AbstractThe 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, automatic adjustment of movement, or internal model recalibration, remains intact and might even be increased with aging. Since somatosensory information appears to be required for internal model recalibration, it might well be that an age-related decline in somatosensory acuity is linked to the increase of internal model recalibration. One possible explanation for an increased internal model recalibration is that age-related somatosensory deficits could lead to altered sensory integration with an increased weighting of the visual sensory-prediction error. Another possibility is that reduced somatosensory acuity results in an increased reliance on predicted sensory feedback. Both these explanations led to our preregistered hypothesis: we expect a relation between the decline of somatosensation and the increased internal model recalibration with aging. However, we failed to support this hypothesis. Our results question the existence of reliability-based integration of visual and somatosensory signals during motor adaptation.New & NoteworthyIs somatosensory acuity linked to implicit motor adaptation? The latter is larger in old compared to younger people? In light of reliability-based sensory integration, we hypothesized that this larger implicit adaptation was linked to an age-related lower reliability of somatosensation. Over two experiments and 130 participants, we failed to find any evidence for this. We discuss alternative explanations for the increase in implicit adaptation with age and the validity of our somatosensory assessment.

scholarly journals Learning from the path not taken: Sensory prediction errors are sufficient for implicit adaptation of withheld movements

2021 ◽  
Author(s):  
Olivia A Kim ◽  
Alexander D Forrence ◽  
Samuel D McDougle
Keyword(s):  
Sensory Feedback ◽  
Prediction Errors ◽  
Learning Networks ◽  
Model Framework ◽  
Forward Models ◽  
Implicit Motor Learning ◽  
Computer Mouse ◽  
Implicit Motor ◽  
Human Participants ◽  
Sensory Prediction

Current theories of motor control emphasize forward models as a critical component of the brain's motor execution and learning networks. These internal models are thought to predict the consequences of movement before sensory feedback from these movements can reach the brain, allowing for smooth, continuous online motor performance and for the computation of prediction errors that drive implicit motor learning. Taking this framework to its logical extreme, we tested the hypothesis that movements are not necessary for the generation of predictions, the computation of prediction errors, and implicit motor adaptation. Human participants were cued to move a computer mouse to a visually displayed target and were subsequently cued to withhold those movements on a subset of trials. Visual errors displayed on both trials with and without movements to the target induced single-trial learning. Furthermore, learning on trials without movements persisted when accompanying movement trials were never paired with errors and when movement and sensory feedback trajectories were decoupled. These data provide compelling evidence supporting an internal model framework in which forward models generate sensory predictions independent of the generation of movements.

Kinematic contribution and synchronization of the trunk, hip, and knee during free-dynamic lifting

2003 ◽  
Vol 3 (2) ◽  
pp. 99-108
Author(s):  
Kermit G. Davis ◽  
Riley E. Splittstoesser ◽  
William S. Marras
Keyword(s):  
Whole Body ◽  
Knee Height ◽  
Squat Lifting ◽  
The Difference ◽  
Free Dynamic ◽  
Body Lifting ◽  
And Task ◽  
Kinematic Contribution ◽  
Stoop Lifting

Although there have been numerous studies evaluating the difference between stooped and squat lifting styles, there remains a lack of understanding of whole body kinematics during unrestricted lifting. The current study evaluated nine males and nine females while lifting two box weights (9.1 kg, 18.2 kg) from five origins below the waist (0, 19, 38, 57, and 76 cm above the floor) and from three task asymmetries (sagittally symmetric, 45° clockwise, 45° counter-clockwise). While the lifting style was significantly influenced by the height of lift origin and to a lesser extent gender, box weight, and task asymmetry, none of the conditions resulted in pure squat or stoop lifting style. However, for lifts above knee height, the lifting style resembled more of a stoop lift while lifts originating below knee height were more of a squat lift. As the origin moved closer to the floor, participants relied more on their hips to accomplish the sagittal flexion but overall adopted a more coordinated whole-body lifting style. All together, as more sagittal flexion is required, more joints are relied upon in a more coordinated effort. The current study indicates that caution needs to be exercised when applying results of pure squat or pure stoop lifting studies to free-style (realistic) lifting.

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

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.

scholarly journals A Computational Role for Top–Down Modulation from Frontal Cortex in Infancy

2020 ◽  
Vol 32 (3) ◽  
pp. 508-514 ◽  
Author(s):  
Sagi Jaffe-Dax ◽  
Alex M. Boldin ◽  
Nathaniel D. Daw ◽  
Lauren L. Emberson
Keyword(s):  
Associative Learning ◽  
Preterm Infants ◽  
Prediction Error ◽  
Frontal Lobes ◽  
Full Term ◽  
Computational Learning ◽  
Top Down ◽  
Term Infants ◽  
Young Infants ◽  
Sensory Prediction

Recent findings have shown that full-term infants engage in top–down sensory prediction, and these predictions are impaired as a result of premature birth. Here, we use an associative learning model to uncover the neuroanatomical origins and computational nature of this top–down signal. Infants were exposed to a probabilistic audiovisual association. We find that both groups (full term, preterm) have a comparable stimulus-related response in sensory and frontal lobes and track prediction error in their frontal lobes. However, preterm infants differ from their full-term peers in weaker tracking of prediction error in sensory regions. We infer that top–down signals from the frontal lobe to the sensory regions carry information about prediction error. Using computational learning models and comparing neuroimaging results from full-term and preterm infants, we have uncovered the computational content of top–down signals in young infants when they are engaged in a probabilistic associative learning.

scholarly journals Time-dependent prediction degredation assessment of neural-networks-based TEC forecasting models

2003 ◽  
Vol 10 (6) ◽  
pp. 585-587 ◽  
Author(s):  
Th. D. Xenos ◽  
S. S. Kouris ◽  
A. Casimiro
Keyword(s):  
Neural Networks ◽  
Faraday Rotation ◽  
Prediction Error ◽  
Prediction Accuracy ◽  
Time Span ◽  
Time Dependent ◽  
Forecasting Models ◽  
Error Margin ◽  
The Difference ◽  
Tec Variability

Abstract. An estimation of the difference in TEC prediction accuracy achieved when the prediction varies from 1 h to 7 days in advance is described using classical neural networks. Hourly-daily Faraday-rotation derived TEC measurements from Florence are used. It is shown that the prediction accuracy for the examined dataset, though degrading when time span increases, is always high. In fact, when a relative prediction error margin of ± 10% is considered, the population percentage included therein is almost always well above the 55%. It is found that the results are highly dependent on season and the dataset wealth, whereas they highly depend on the foF2 - TEC variability difference and on hysteresis-like effect between these two ionospheric characteristics.

scholarly journals M40. Sensory Prediction Error Deficits in Schizophrenia: A Multi Behavioral Approach

2017 ◽  
Vol 43 (suppl_1) ◽  
pp. S226-S226
Author(s):  
Sonia Bansal ◽  
Barbara Schwartz ◽  
Wilsaan Joiner
Keyword(s):  
Prediction Error ◽  
Behavioral Approach ◽  
Sensory Prediction

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

Brain ◽  
2019 ◽  
Vol 142 (3) ◽  
pp. 662-673 ◽  
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.

Recall of “Right” and “Wrong” Responses as a Function of Instructions for Intentional Learning and Task Familiarization

1976 ◽  
Vol 38 (2) ◽  
pp. 619-624 ◽  
Author(s):  
Géry D'ydewalle
Keyword(s):  
Law School ◽  
Intentional Learning ◽  
First Year ◽  
Real Word ◽  
First Year Students ◽  
The Law ◽  
The Difference ◽  
Correct Translation ◽  
And Task

Subjects (100 first-year students of both sexes from the Law School at Leuven, Belgium) selected one of several nonsense words as a “translation” of a real word and were told whether their choice was “right” or “wrong.” Subjects who expected to have the whole series of items a number of times until the correct translation for each word was completely learned (multiple-trial condition) recalled more “right” responses than subjects who expected a single second trial (two-trial condition). A preliminary exposure to a similar but shorter task had a different influence according to the two conditions: there was a decrease in recall of response in the multiple-trial condition but an increase in the two-trial condition. The data suggest that conflicting conclusions about the difference in recall between “right” and “wrong” responses are related to differences in the instructions for intentional learning.

Sign in / Sign up

Export Citation Format

Share Document