Spatiotemporal integration for tactile localization during arm movements: a probabilistic approach

Maij, Femke; Wing, Alan M.; Medendorp, W. Pieter

doi:10.1152/jn.00971.2012

Cited by 13 publications

(21 citation statements)

References 38 publications

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“…This computation is likely implemented, at least partially, in somatosensory cortex. Future work should address how multilateration can be extended to cases of localization in two (Mancini et al, 2011) or three dimensions (Azañón et al, 2016), as well as when touch occurs under more dynamic contexts (Maij et al, 2013).…”

Section: Resultsmentioning

confidence: 99%

A neural surveyor to map touch on the body

Miller

Fabio

Beers

et al. 2020

Preprint

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Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Though it seems straightforward, this simple representation belies the complex link between an activation in somatosensory Area 3b and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its position within the entire map, though how this is computed by neural networks is unknown.We propose that somatosensory cortex implements multilateration, a common computation used by surveying and GPS systems to localize objects. Specifically, to decode touch location on the body, the somatosensory system estimates the relative distance between the afferent input and the body's joints. We show that a simple feedforward neural network which captures the receptive field properties of somatosensory cortex implements a Bayes-optimal multilateral decoder via a combination of bell-shaped (Area 3b) and sigmoidal (Areas 1/2) tuning curves. Simulations demonstrated that this decoder produced a unique pattern of localization variability between two joints that was not produced by other known neural decoders. Finally, we identify this neural signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate computational mechanism underlying tactile localization. take place in the frontal and parietal cortices (Burnod et al., 1999;Crawford et al., 2004;Medendorp et al., 2005;Pesaran et al., 2006).Equally crucial to localizing objects in the environment is localizing objects on the personal space of the body. Despite over 180 years of research on the sense of touch (Weber, 1834), the computations underlying tactile localization remain largely unknown. Recent accounts have suggested that tactile localization requires two computational steps Medina and Coslett, 2010). First, afferent input must be localized within a topographic map in

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Section: Resultsmentioning

confidence: 99%

A neural surveyor to map touch on the body

Miller

Fabio

Beers

et al. 2020

Preprint

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“…For instance, tactile object recognition critically depends on manipulating the object with the hands, and moving one's fingers across it (20). Furthermore, spatial perception in touch can be anchored to the timing of active movement, as demonstrated by neural coding of touch during whisking in rats (21), as well as systematic localization errors that arise when humans indicate the location of touch that occurred during arm movements (22). In vision, saccades into the periphery often slightly miss their target, and are then corrected by secondary saccades, both in the laboratory (23,24) and naturalistic environments (25).…”

Section: Introductionmentioning

confidence: 99%

Online sensory feedback during active search improves tactile localization

Fuchs

Wulff

Heed

2019

Preprint

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AbstractMany natural behaviors involve closed feedback loops in which ongoing sensory input refines motor behavior. Previous research on tactile localization, however, has implemented localization as open-loop behavior. For instance, participants indicate a touched position on a silhouette shape of the body or on an occluding board mounted above the hand. Such studies have suggested that humans often make large errors when localizing touch on the skin, or that “perceptual body representations” are distorted. However, by artificially preventing tactile feedback from the target body area, the natural action-perception loop is interrupted. Therefore, these localization approaches may underestimate individuals’ localization ability and draw erroneous conclusions about the role and precision of body representations. Here, we tested tactile localization in a natural setting, in which participants first received brief touches on their left forearm and then searched for the target location by moving the right index finger across the skin. Tactile search reduced localization error when the searching finger was far from, but not when it was near the target, resulting in a remaining error of 1-2 cm. Error reduction was absent when participants searched on an acrylic barrier mounted above the arm, suggesting that availability of tactile feedback on the target arm but not proprioceptive and motor signals of the searching arm determined precision, thus confirming the pivotal role of closed-loop sensory feedback for tactile localization. We suggest that actively produced online tactile feedback routinely refines coarse spatial body representations, similar to the refinement of sparse spatial representations in visual memory through consecutive saccades.

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“…ACTION INFLUENCES PERCEPTION. For example, a brief stimulus presented near the time of an eye or arm movement is systematically mislocalized in space, such as a visual flash near the time of a saccade (Richard et al 2009;Ross et al 1997;Schlag and Schlag-Rey 2002) or a haptic stimulus near the time of an arm movement (Dassonville 1995;Maij et al 2013;Watanabe et al 2009). The localization error depends systematically on when the stimulus is presented during the movement; the errors are in the direction of the movement at the beginning of the movement and in the opposite direction at the end of the movement (e.g., Dassonville 1995;O'Regan 1984;Watanabe et al 2009).…”

Section: New and Noteworthymentioning

confidence: 99%

“…Maij and colleagues (Maij et al 2011(Maij et al , 2013 recently modeled this interaction between perception and action using Bayesian inference. In their model, the temporal uncertainty in the processing of the stimulus is converted into a spatial likelihood of where this stimulus was given during the movement, a conversion that involves a Jacobian transformation that depends on the kinematics of the movement.…”

Section: New and Noteworthymentioning

confidence: 99%

“…In their model, the temporal uncertainty in the processing of the stimulus is converted into a spatial likelihood of where this stimulus was given during the movement, a conversion that involves a Jacobian transformation that depends on the kinematics of the movement. The model (Maij et al 2011), derived to explain perisaccadic mislocalization, could also account for tactile mislocalization during arm movements, including the different error patterns between slow and fast arm movements (Maij et al 2013).…”

Section: New and Noteworthymentioning

confidence: 99%

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Afferent motor feedback determines the perceived location of tactile stimuli in the external space presented to the moving arm

Maij

Wing

Medendorp

2017

Journal of Neurophysiology

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People make systematic errors when localizing a brief tactile stimulus in the external space presented on the index finger while moving the arm. Although these errors likely arise in the spatiotemporal integration of the tactile input and information about arm position, the underlying arm position information used in this process is not known. In this study, we tested the contributions of afferent proprioceptive feedback and predictive arm position signals by comparing localization errors during passive vs. active arm movements. In the active trials, participants were instructed to localize a tactile stimulus in the external space that was presented to the index finger near the time of a self-generated arm movement. In the passive trials, each of the active trials was passively replayed in randomized order, using a robotic device. Our results provide evidence that the localization error patterns of the passive trials are similar to the active trials and, moreover, did not lag but rather led the active trials, which suggests that proprioceptive feedback makes an important contribution to tactile localization. To further test which kinematic property of this afferent feedback signal drives the underlying computations, we examined the localization errors with movements that had differently skewed velocity profiles but overall the same displacement. This revealed a difference in the localization patterns, which we explain by a probabilistic model in which temporal uncertainty about the stimulus is converted into a spatial likelihood, depending on the actual velocity of the arm rather than involving an efferent, preprogrammed movement. We show that proprioceptive feedback of arm motion rather than efferent motor signals contributes to tactile localization during an arm movement. Data further show that localization errors depend on arm velocity, not displacement per se, suggesting that instantaneous velocity feedback plays a role in the underlying computations. Model simulation using Bayesian inference suggests that these errors depend not only on spatial but also on temporal uncertainties of sensory and motor signals.

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Spatiotemporal integration for tactile localization during arm movements: a probabilistic approach

Cited by 13 publications

References 38 publications

A neural surveyor to map touch on the body

A neural surveyor to map touch on the body

Online sensory feedback during active search improves tactile localization

Afferent motor feedback determines the perceived location of tactile stimuli in the external space presented to the moving arm

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