Hierarchical processing underpins competition in tactile perceptual bistability.

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Computational Neuroscience Pub Date : 2022-08-01 Epub Date: 2023-05-19 DOI:10.1007/s10827-023-00852-0
Farzaneh Darki, Andrea Ferrario, James Rankin
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Abstract

Ambiguous sensory information can lead to spontaneous alternations between perceptual states, recently shown to extend to tactile perception. The authors recently proposed a simplified form of tactile rivalry which evokes two competing percepts for a fixed difference in input amplitudes across antiphase, pulsatile stimulation of the left and right fingers. This study addresses the need for a tactile rivalry model that captures the dynamics of perceptual alternations and that incorporates the structure of the somatosensory system. The model features hierarchical processing with two stages. The first and the second stages of model could be located at the secondary somatosensory cortex (area S2), or in higher areas driven by S2. The model captures dynamical features specific to the tactile rivalry percepts and produces general characteristics of perceptual rivalry: input strength dependence of dominance times (Levelt's proposition II), short-tailed skewness of dominance time distributions and the ratio of distribution moments. The presented modelling work leads to experimentally testable predictions. The same hierarchical model could generalise to account for percept formation, competition and alternations for bistable stimuli that involve pulsatile inputs from the visual and auditory domains.

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分级处理是触觉-知觉双稳态竞争的基础。
模糊的感官信息会导致感知状态之间的自发变化,最近显示这种变化延伸到触觉。作者最近提出了一种简化形式的触觉对抗,它唤起了两种相互竞争的感知,即在左手和右手手指的反相脉动刺激中输入振幅的固定差异。这项研究解决了对触觉竞争模型的需求,该模型捕捉感知变化的动态,并结合了体感系统的结构。该模型的特点是分两个阶段进行分层处理。模型的第一和第二阶段可以位于次级体感皮层(S2区域),或者位于S2驱动的更高区域。该模型捕捉了触觉竞争感知特有的动态特征,并产生了感知竞争的一般特征:支配时间的输入强度依赖性(Levelt命题II)、支配时间分布的短尾偏斜度和分布矩的比率。所提出的建模工作导致了可通过实验测试的预测。同样的层次模型可以广义地解释双稳态刺激的感知形成、竞争和交替,双稳态刺激涉及来自视觉和听觉领域的脉动输入。
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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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