Search arXiv⌕ Search

arXiv · 2306.15279

Does visual experience influence arm proprioception and its lateralization? Evidence from passive matching performance in congenitally-blind and sighted adults

Abstract

In humans, body segments' position and movement can be estimated from multiple senses such as vision and proprioception. It has been suggested that vision and proprioception can influence each other and that upper-limb proprioception is asymmetrical, with proprioception of the non-dominant arm being more accurate and/or precise than proprioception of the dominant arm. However, the mechanisms underlying the lateralization of proprioceptive perception are not yet understood. Here we tested the hypothesis that early visual experience influences the lateralization of arm proprioceptive perception by comparing 8 congenitally-blind and 8 matched, sighted right-handed adults. Their proprioceptive perception was assessed at the elbow and wrist joints of both arms using an ipsilateral passive matching task. Results support and extend the view that proprioceptive precision is better at the non-dominant arm for blindfolded sighted individuals. While this finding was rather systematic across sighted individuals, proprioceptive precision of congenitally-blind individuals was not lateralized as systematically, suggesting that lack of visual experience during ontogenesis influences the lateralization of arm proprioception.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Najib Abi Chebel, Florence Gaunet, Pascale Chavet, Christine Assaiante, Christophe Bourdin, Fabrice Sarlegna. 2023-06-27. Does visual experience influence arm proprioception and its lateralization? Evidence from passive matching performance in congenitally-blind and sighted adults. https://doi.org/10.1016/j.neulet.2023.137335

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

ELiSe: Efficient Learning of Sequences in Structured Recurrent Networks

Behavior can be described as a temporal sequence of actions driven by neural activity. To learn complex sequential patterns in neural networks, memories of past activities need to persist on significantly longer timescales than the relaxation times of single-neuron activity. While recurrent networks can produce such long transients, training these networks is a challenge. Learning via error propagation confers models such as FORCE, RTRL or BPTT a significant functional advantage, but at the expense of biological plausibility. While reservoir computing circumvents this issue by learning only the readout weights, it does not scale well with problem complexity. We propose that two prominent structural features of cortical networks can alleviate these issues: the presence of a certain network scaffold at the onset of learning and the existence of dendritic compartments for enhancing neuronal information storage and computation. Our resulting model for Efficient Learning of Sequences (ELiSe) builds on these features to acquire and replay complex non-Markovian spatio-temporal patterns using only local, always-on and phase-free synaptic plasticity. We showcase the capabilities of ELiSe in a mock-up of birdsong learning, and demonstrate its flexibility with respect to parametrization, as well as its robustness to external disturbances.

q-bio.NC↗

Three Failures of Pain Location: Why Its Diagnostic Utility Is Three Quantities, Not One

Patient-reported pain location is diagnostically decisive for some presentations and nearly uninformative for others. The prevailing account treats this as one gradient of diagnostic utility set by anatomical complexity. That explanation conflates three epistemically distinct failures, each with its own mathematics, its own optimal instrument, and its own public-health consequence. In anatomical multiplexing, many structures share one location: a non-identifiable inverse problem. In delocalized amplification - clinically, central sensitization or nociplastic pain - a centrally driven pain-behaviour pattern replaces the peripheral generator: a change of generative model. In referred and atypical displacement, location is hypothesized to shift in a systematic, person-dependent way: a group-conditional bias whose direct evidence is still open. The three are one Bayesian inference problem failing at different nodes - the likelihood, the model class, and the group-conditional prior - with a fourth node at the report itself. The formal development is in a companion paper; this paper states what each model shows and what follows clinically. Re-examination finds that the published "high-utility" accuracy band leans on overstated specificity (Lipton et al., 2003; Bruyninckx et al., 2008; Devillé et al., 2000), so the gradient is real but flatter than drawn. The well-evidenced finding that better detection alone does not improve outcomes when treatment uptake lags is about the care pathway, not perception - a distinction the three-way split makes visible and a single utility number hides. The paper organizes the failures along a why-location-fails axis, distinct from the nociceptive/neuropathic/nociplastic taxonomy (Kosek et al., 2016), and sets out the study that would test the one prediction still open.

q-bio.NC↗

A Spiking Neural Network Model of Elementary Self-Consciousness via Endogenous Default Mode Network Dynamics

Understanding the neurobiological mechanisms underlying self-referential cognition and baseline self-consciousness remains a fundamental challenge in computational neuroscience. In this work, we propose a large-scale computational model incorporating a 10,000-neuron spiking neural network (SNN) based on Izhikevich dynamics. The network is structured into two interacting subsystems: a sensory processing layer (5,000 regular-spiking cortical neurons) and an endogenous Default Mode Network (DMN) pacemaker subsystem (5,000 intrinsically bursting neurons). The DMN layer is modulated by continuous tonic currents reflecting ascending brainstem neuromodulation, maintaining intrinsic, autonomous bioelectric rhythms independent of external sensory input. To represent top-down cognitive modulation, synaptic weights are hierarchically structured such that DMN-to-network projections exceed sensory-level connections. Through numerical simulations using a modified two-step Euler integration scheme, we demonstrate how endogenous pacemaker activity interacts with transient external sensory perturbations, providing an elementary mathematical framework for the emergence of a persistent, self-sustaining neural representation of "Self".

q-bio.NC↗