Testing the limits of optimal integration of visual and proprioceptive information of path trajectory

Reuschel, Johanna; Rösler, Frank; Henriques, Denise Y. P.; Fiehler, Katja

doi:10.1007/s00221-011-2596-0

Cited by 8 publications

(4 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…van Beers et al (1999) asked their participants to move their target-hand to a position as close to the final target location as possible and then touch the underside of the experimental set up until they found a tactile marker. Previous work in our laboratory , a reaching task with gaze deviated, using a robot manipulandum for proprioceptive target placement also did not find support for Bayesian integration of remembered visual information with online or remembered proprioceptive information, nor did previous studies that have employed precise placement of the target-hand using a mechanical apparatus (Monaco et al, 2010;Reuschel, Rosler, Henriques, & Fiehler, 2011). Last, in our study, the last target site was the start of the path to the next target site.…”

Section: Combining Vision and Proprioceptioncontrasting

confidence: 83%

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

Jones

Byrne

Fiehler

et al. 2012

Journal of Neurophysiology

Self Cite

View full text Add to dashboard Cite

Previous research has shown that reach endpoints vary with the starting position of the reaching hand and the location of the reach target in space. We examined the effect of movement direction of a proprioceptive target-hand, immediately preceding a reach, on reach endpoints to that target. Participants reached to visual, proprioceptive (left target-hand), or visual-proprioceptive targets (left target-hand illuminated for 1 s prior to reach onset) with their right hand. Six sites served as starting and final target locations (35 target movement directions in total). Reach endpoints do not vary with the movement direction of the proprioceptive target, but instead appear to be anchored to some other reference (e.g., body). We also compared reach endpoints across the single and dual modality conditions. Overall, the pattern of reaches for visual-proprioceptive targets resembled those for proprioceptive targets, while reach precision resembled those for the visual targets. We did not, however, find evidence for integration of vision and proprioception based on a maximum-likelihood estimator in these tasks.

show abstract

Section: Combining Vision and Proprioceptioncontrasting

confidence: 83%

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

Jones

Byrne

Fiehler

et al. 2012

Journal of Neurophysiology

Self Cite

View full text Add to dashboard Cite

show abstract

“…This distribution does not support a dichotomous error attribution [14], [15] to either internal or, alternatively, external causes, but rather suggests a continuous attribution mechanism on the level of single trials. Such partial attribution of prediction errors to internal causes would be consistent with the notion that the perception of one’s actions builds on the integration of internal and external action-related cues [26], [27], [28], [29].…”

Section: Discussionsupporting

confidence: 78%

Sensorimotor Recalibration Depends on Attribution of Sensory Prediction Errors to Internal Causes

2013

View full text Add to dashboard Cite

Sensorimotor learning critically depends on error signals. Learning usually tries to minimise these error signals to guarantee optimal performance. Errors can, however, have both internal causes, resulting from one’s sensorimotor system, and external causes, resulting from external disturbances. Does learning take into account the perceived cause of error information? Here, we investigated the recalibration of internal predictions about the sensory consequences of one’s actions. Since these predictions underlie the distinction of self- and externally produced sensory events, we assumed them to be recalibrated only by prediction errors attributed to internal causes. When subjects were confronted with experimentally induced visual prediction errors about their pointing movements in virtual reality, they recalibrated the predicted visual consequences of their movements. Recalibration was not proportional to the externally generated prediction error, but correlated with the error component which subjects attributed to internal causes. We also revealed adaptation in subjects’ motor performance which reflected their recalibrated sensory predictions. Thus, causal attribution of error information is essential for sensorimotor learning.

show abstract

“…Studies on shape perception (Helbig and Ernst 2007), hand position sense (van Beers et al 1996), and path trajectory integration (Reuschel et al 2010) showed results consistent with a maximum-likelihood estimation (MLE) model. However, recent work on reaching and multimodal asynchronous curvature detection was not in agreement with a MLE model of visual-proprioceptive integration (Jones and Henriques 2010;Reuschel et al 2011;Winges et al 2010).…”

mentioning

confidence: 82%

Two hands, one perception: how bimanual haptic information is combined by the brain

Squeri

Sciutti

Gori

et al. 2012

Journal of Neurophysiology

View full text Add to dashboard Cite

Humans routinely use both of their hands to gather information about shape and texture of objects. Yet, the mechanisms of how the brain combines haptic information from the two hands to achieve a unified percept are unclear. This study systematically measured the haptic precision of humans exploring a virtual curved object contour with one or both hands to understand if the brain integrates haptic information from the two hemispheres. Bayesian perception theory predicts that redundant information from both hands should improve haptic estimates. Thus exploring an object with two hands should yield haptic precision that is superior to unimanual exploration. A bimanual robotic manipulandum passively moved the hands of 20 blindfolded, right-handed adult participants along virtual curved contours. Subjects indicated which contour was more "curved" (forced choice) between two stimuli of different curvature. Contours were explored uni-or bimanually at two orientations (toward or away from the body midline). Respective psychophysical discrimination thresholds were computed. First, subjects showed a tendency for one hand to be more sensitive than the other with most of the subjects exhibiting a left-hand bias. Second, bimanual thresholds were mostly within the range of the corresponding unimanual thresholds and were not predicted by a maximum-likelihood estimation (MLE) model. Third, bimanual curvature perception tended to be biased toward the motorically dominant hand, not toward the haptically more sensitive left hand. Two-handed exploration did not necessarily improve haptic sensitivity. We found no evidence that haptic information from both hands is integrated using a MLE mechanism. Rather, results are indicative of a process of "sensory selection", where information from the dominant right hand is used, although the left, nondominant hand may yield more precise haptic estimates.handedness; human; sensorimotor; sensory integration HUMANS ROUTINELY USE THEIR hands to haptically explore objects in the environment. In many cases, both hands are used to gain information about the properties of the object. We know that haptic sensing requires the integration of spatially disparate sensory signals from cutaneous afferents in the digits with proprioceptive signals of the arm (a process of intersensory integration). However, we know very little about how the brain combines the haptic information of our two hands to achieve a single percept of an object and about the underlying mechanism of how the nervous system integrates or fuses information from two haptic systems.To investigate issues of haptic sensing in a controlled experimental setting, virtual force environments have been used to present haptic stimuli (Chib et al. 2006;Fasse et al. 2000;Henriques and Soechting 2005;Hogan et al. 1990). In these studies, subjects usually grasped the handle or stylus of a robotically controlled manipulandum, which generated appropriate boundary forces resembling the surfaces of virtual objects. The advantage of this technique is that o...

show abstract

Testing the limits of optimal integration of visual and proprioceptive information of path trajectory

Cited by 8 publications

References 58 publications

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

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

Sensorimotor Recalibration Depends on Attribution of Sensory Prediction Errors to Internal Causes

Two hands, one perception: how bimanual haptic information is combined by the brain

Contact Info

Product

Resources

About