A neural surveyor to map touch on the body

Miller, Luke; Fabio, Cécile; Azaroual, Malika; Muret, Dollyane; Beers, Robert J. van; Farnè, Alessandro; Medendorp, W. Pieter

doi:10.1073/pnas.2102233118

Cited by 17 publications

(21 citation statements)

References 83 publications

(141 reference statements)

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“…Instead, the present results are more in line with a functional account of embodiment, where sensorimotor computations are merely re-used during tool use. Localizing touch on a rods may involve similar sensorimotor transformations (Yamamoto & Kitazawa, 2001), somatosensory computations (Miller et al, 2022;Miller et al, 2021), and neural processes (Miller et al, 2019) as localizing touch on the body. Given the high localization accuracy observed in the present study, we propose that this is also the case when localizing touch on rods of extreme length.…”

Section: Methodsmentioning

confidence: 99%

A horizon for haptic perception

Miller¹,

Jarto²,

Medendorp³

2022

Preprint

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The spatial limits of sensory acquisition (its sensory horizon) is a fundamental property of any sensorimotor system. In the present study, we sought to determine the sensory horizon for the human haptic modality. At first blush, it seems obvious that the haptic system is bounded by the space where the body can interact with the environment (e.g., the arm span). However, the human somatosensory system is exquisitely tuned to sensing with tools; blind-cane navigation being a classic example of this. The horizon of haptic perception therefore extends beyond body space, but to what extent is unknown. We first used neuromechanical modelling to determine the theoretical horizon, which we pinpointed as six meters. We then used a psychophysical localization paradigm to behaviorally confirm that humans can haptically localize objects using a six-meter rod. This finding underscores the incredibly flexibility of the brain's sensorimotor representations, as they can be adapted to sense with an object many times longer than the user's own body.

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Section: Methodsmentioning

confidence: 99%

A horizon for haptic perception

Miller¹,

Jarto²,

Medendorp³

2022

Preprint

Self Cite

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“…The brain does not represent the angles, lengths, and locations as individual point estimates but rather as probability distributions. 15 This is because the activity within the nervous system is intrinsically noisy, from transduction of signals to network interactions. 16 To deal with this uncertainty, the brain must rely on probabilistic processing, as formalized by Bayesian decision theory.…”

Section: A Bayesian Model Of Finger Perceptionmentioning

confidence: 99%

Biases in hand perception are driven by somatosensory computations, not a distorted hand model

Peviani,

Miller,

Medendorp

2023

Preprint

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SummaryTo sense and interact with objects in the environment, we effortlessly configure our fingertips at desired locations. It is therefore reasonable to assume the underlying control mechanisms rely on accurate knowledge about the structure and spatial dimensions of our hand and fingers. This intuition, however, is challenged by years of research showing drastic biases in the perception of finger geometry, e.g.,1–5. This perceptual bias has been taken as evidence that the brain’s internal representation of the body’s geometry is distorted,6leading to an apparent paradox with the skillfulness of our actions.7Here, we propose an alternative explanation of the biases in hand perception—They are the result of the Bayesian integration of noisy, but unbiased somatosensory signals about finger geometry and posture. To address this hypothesis, we combined Bayesian reverse-engineering with behavioral experimentation on joint and fingertip localization of the index finger. We modelled the Bayesian integration either in sensory or in space-based coordinates, showing that the latter model variant led to biases in finger perception despiteaccuraterepresentations of finger length. Behavioral measures of joint and fingertip localization responses showed similar biases, which were well-fitted by the space-based but not the sensory-based model variant. Our results suggest that perceptual distortions of finger geometry do not reflect a distorted hand model but originate from near-optimal Bayesian inference on somatosensory signals. These findings demand a reinterpretation of previous work on hand model distortions.

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“…Because it is essential to identify the position of a tactile object in order to interact with it, more than a century of research has characterized the perceptual processes underlying tactile localization at a behavioral level (Miller et al, 2022;Liu & Medina, 2021;Fuchs, Wulff, & Heed, 2020;Sadibolova, Tamè, & Longo, 2018;Badde, Röder, & Heed, 2015;Mancini, Longo, Iannetti, & Haggard, 2011;Harrar & Harris, 2009;Ho & Spence, 2007;Cholewiak & Collins, 2003;Stevens, 1992;Parrish, 1897;Weber, 1846; for a review, see the work of . These studies have identified different spatial codes that are utilized to localize tactile stimuli.…”

Section: Introductionmentioning

confidence: 99%

Alpha Oscillations Are Involved in Localizing Touch on Handheld Tools

Fabio

Salemme

Koun

et al. 2022

Journal of Cognitive Neuroscience

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The sense of touch is not restricted to the body but can also extend to external objects. When we use a handheld tool to contact an object, we feel the touch on the tool and not in the hand holding the tool. The ability to perceive touch on a tool actually extends along its entire surface, allowing the user to accurately localize where it is touched similarly as they would on their body. Although the neural mechanisms underlying the ability to localize touch on the body have been largely investigated, those allowing to localize touch on a tool are still unknown. We aimed to fill this gap by recording the electroencephalography signal of participants while they localized tactile stimuli on a handheld rod. We focused on oscillatory activity in the alpha (7–14 Hz) and beta (15–30 Hz) ranges, as they have been previously linked to distinct spatial codes used to localize touch on the body. Beta activity reflects the mapping of touch in skin-based coordinates, whereas alpha activity reflects the mapping of touch in external space. We found that alpha activity was solely modulated by the location of tactile stimuli applied on a handheld rod. Source reconstruction suggested that this alpha power modulation was localized in a network of fronto-parietal regions previously implicated in higher-order tactile and spatial processing. These findings are the first to implicate alpha oscillations in tool-extended sensing and suggest an important role for processing touch in external space when localizing touch on a tool.

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A neural surveyor to map touch on the body

Cited by 17 publications

References 83 publications

A horizon for haptic perception

A horizon for haptic perception

Biases in hand perception are driven by somatosensory computations, not a distorted hand model

Alpha Oscillations Are Involved in Localizing Touch on Handheld Tools

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