Reach endpoint errors do not vary with movement path of the proprioceptive target

Jones, Stephanie; Byrne, Patrick; Fiehler, Katja; Henriques, Denise Y. P.

doi:10.1152/jn.00901.2011

Cited by 7 publications

(5 citation statements)

References 46 publications

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“…The similar sensory weights we found across hands suggest that they might be determined through learning the contingencies of the task rather than being specific to each effector. Our findings are in line with previous research showing that in certain cases the brain does not seem to compute a maximum-likelihood estimate of hand position (Jones et al 2012). Likewise, when performing a bimanual task, proprioceptive signals from left and right arms are not assigned integration weights that are related to their respective proprioceptive reliabilities (Wong et al 2014).…”

Section: Interpretation Of Main Findingssupporting

confidence: 91%

Learned rather than online relative weighting of visual-proprioceptive sensory cues

Mikula

Gaveau

Pisella

et al. 2018

Journal of Neurophysiology

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When reaching to an object, information about the target location as well as the initial hand position is required to program the motor plan for the arm. The initial hand position can be determined by proprioceptive information as well as visual information, if available. Bayes-optimal integration posits that we utilize all information available, with greater weighting on the sense that is more reliable, thus generally weighting visual information more than the usually less reliable proprioceptive information. The criterion by which information is weighted has not been explicitly investigated; it has been assumed that the weights are based on task- and effector-dependent sensory reliability requiring an explicit neuronal representation of variability. However, the weights could also be determined implicitly through learned modality-specific integration weights and not on effector-dependent reliability. While the former hypothesis predicts different proprioceptive weights for left and right hands, e.g., due to different reliabilities of dominant vs. nondominant hand proprioception, we would expect the same integration weights if the latter hypothesis was true. We found that the proprioceptive weights for the left and right hands were extremely consistent regardless of differences in sensory variability for the two hands as measured in two separate complementary tasks. Thus we propose that proprioceptive weights during reaching are learned across both hands, with high interindividual range but independent of each hand's specific proprioceptive variability. NEW & NOTEWORTHY How visual and proprioceptive information about the hand are integrated to plan a reaching movement is still debated. The goal of this study was to clarify how the weights assigned to vision and proprioception during multisensory integration are determined. We found evidence that the integration weights are modality specific rather than based on the sensory reliabilities of the effectors.

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Section: Interpretation Of Main Findingssupporting

confidence: 91%

Learned rather than online relative weighting of visual-proprioceptive sensory cues

Mikula

Gaveau

Pisella

et al. 2018

Journal of Neurophysiology

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show abstract

“…However, if proprioception and predicted sensory consequences were integrated in a Bayesian manner, then the contribution of proprioception to the overall state estimate depended on it’s accuracy relative to the accuracy of predicted sensory consequences. This accuracy can be expressed as the inverse of the variance [ 25 ], and such an approach has been used to model the integration of proprioception with actual visual consequences [ 26 , 27 , 28 ]. If efference-based predictions of hand location were much more accurate than proprioception-based localization and the two signals were integrated in a Bayesian manner, then the variance in the active localization conditions should be lower than the variance in the passive localization conditions for all participants.…”

Section: Methodsmentioning

confidence: 99%

Separating Predicted and Perceived Sensory Consequences of Motor Learning

Hart

Henriques

2016

PLoS ONE

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During motor adaptation the discrepancy between predicted and actually perceived sensory feedback is thought to be minimized, but it can be difficult to measure predictions of the sensory consequences of actions. Studies attempting to do so have found that self-directed, unseen hand position is mislocalized in the direction of altered visual feedback. However, our lab has shown that motor adaptation also leads to changes in perceptual estimates of hand position, even when the target hand is passively displaced. We attribute these changes to a recalibration of hand proprioception, since in the absence of a volitional movement, efferent or predictive signals are likely not involved. The goal here is to quantify the extent to which changes in hand localization reflect a change in the predicted sensory (visual) consequences or a change in the perceived (proprioceptive) consequences. We did this by comparing changes in localization produced when the hand movement was self-generated (‘active localization’) versus robot-generated (‘passive localization’) to the same locations following visuomotor adaptation to a rotated cursor. In this passive version, there should be no predicted consequences of these robot-generated hand movements. We found that although changes in localization were somewhat larger in active localization, the passive localization task also elicited substantial changes. Our results suggest that the change in hand localization following visuomotor adaptation may not be based entirely on updating predicted sensory consequences, but may largely reflect changes in our proprioceptive state estimate.

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“…This may be due to differences in processing visual and somatosensory targets during reaching. For instance, reaching to remembered proprioceptive targets is substantially less accurate and precise than reaching to visual targets [ 35 – 37 ]. Therefore, somatosensory signals from the unseen target hand may also need more processing resources than visual signals about the target position.…”

Section: Discussionmentioning

confidence: 99%

The role of visual processing on tactile suppression

2018

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It has been suggested that tactile signals are suppressed on a moving limb to free capacities for processing other relevant sensory signals. In line with this notion, we recently showed that tactile suppression is indeed stronger in the presence of reach-relevant somatosensory signals. Here we examined whether this effect also generalizes to the processing of additional visual signals during reaching. Brief vibrotactile stimuli were presented on the participants’ right index finger either during right-hand reaching to a previously illuminated target LED, or during rest. Participants had to indicate whether they detected the vibrotactile stimulus or not. The target LED remained off (tactile), or was briefly illuminated (tactile & vis) during reaching, providing additional reach-relevant visual information about the target position. If tactile suppression frees capacities for reach-relevant visual information, suppression should be stronger in the tactile & vis compared to the tactile condition. In an additional visual-discrimination condition (tactile & visDis), the target LED flashed once or twice during reaching and participants had to also report the number of flashes. If tactile suppression occurs to free additional capacities for perception-relevant visual signals, tactile suppression should be even stronger in the tactile & visDis compared to the tactile & vis condition. We found that additional visual signals improved reach endpoint accuracy and precision. In all conditions, reaching led to tactile suppression as indicated by higher detection thresholds compared to rest, confirming previous findings. However, tactile suppression was comparable between conditions arguing against the hypothesis that it frees capacities for processing other relevant visual signals.

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Reach endpoint errors do not vary with movement path of the proprioceptive target

Cited by 7 publications

References 46 publications

Learned rather than online relative weighting of visual-proprioceptive sensory cues

Learned rather than online relative weighting of visual-proprioceptive sensory cues

Separating Predicted and Perceived Sensory Consequences of Motor Learning

The role of visual processing on tactile suppression

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