Visualizing sounds: training-induced plasticity with a visual-to-auditory conversion device

Author:

Lerousseau Jacques PesnotORCID,Arnold GabrielORCID,Auvray MalikaORCID

Abstract

AbstractSensory substitution devices aim at restoring visual functions by converting visual information into auditory or tactile stimuli. Although these devices show promises in the range of behavioral abilities they allow, the processes underlying their use remains underspecified. In particular, while an initial debate focused on the visual versus auditory or tactile nature of sensory substitution, since over a decade, the idea that it reflects a mixture of both has emerged. In order to investigate behaviorally the extent to which visual and auditory processes are involved, participants completed a Stroop-like crossmodal interference paradigm before and after being trained with a conversion device which translates visual images into sounds. In addition, participants’ auditory abilities and their phenomenologies were measured. Our study revealed that, after training, when asked to identify sounds, processes shared with vision were involved, as participants’ performance in sound identification was influenced by the simultaneously presented visual distractors. In addition, participants’ performance during training and their associated phenomenology depended on their auditory abilities, revealing that processing finds its roots in the input sensory modality. Our results pave the way for improving the design and learning of these devices by taking into account inter-individual differences in auditory and visual perceptual strategies.HighlightsTrained people spontaneously use processes shared with vision when hearing sounds from the deviceProcesses with conversion devices find roots both in vision and auditionTraining with a visual-to-auditory conversion device induces perceptual plasticity

Publisher

Cold Spring Harbor Laboratory

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Vision Processing for Assistive Vision: A Deep Reinforcement Learning Approach;IEEE Transactions on Human-Machine Systems;2022-02

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