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      Feeling fooled: Texture contaminates the neural code for tactile speed

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          Abstract

          Motion is an essential component of everyday tactile experience: most manual interactions involve relative movement between the skin and objects. Much of the research on the neural basis of tactile motion perception has focused on how direction is encoded, but less is known about how speed is. Perceived speed has been shown to be dependent on surface texture, but previous studies used only coarse textures, which span a restricted range of tangible spatial scales and provide a limited window into tactile coding. To fill this gap, we measured the ability of human observers to report the speed of natural textures—which span the range of tactile experience and engage all the known mechanisms of texture coding—scanned across the skin. In parallel experiments, we recorded the responses of single units in the nerve and in the somatosensory cortex of primates to the same textures scanned at different speeds. We found that the perception of speed is heavily influenced by texture: some textures are systematically perceived as moving faster than are others, and some textures provide a more informative signal about speed than do others. Similarly, the responses of neurons in the nerve and in cortex are strongly dependent on texture. In the nerve, although all fibers exhibit speed-dependent responses, the responses of Pacinian corpuscle–associated (PC) fibers are most strongly modulated by speed and can best account for human judgments. In cortex, approximately half of the neurons exhibit speed-dependent responses, and this subpopulation receives strong input from PC fibers. However, speed judgments seem to reflect an integration of speed-dependent and speed-independent responses such that the latter help to partially compensate for the strong texture dependence of the former.

          Abstract

          Our ability to sense the speed at which a surface moves across our skin is highly unreliable and depends on the texture of the surface. This study shows that speed illusions can be predicted from the responses of a specific population of nerve fibers and of their downstream targets; because the skin is too sparsely innervated to compute tactile speed accurately, the nervous system relies on a heuristic to estimate it.

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          Most cited references62

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          Spatial and temporal codes mediate the tactile perception of natural textures.

          When we run our fingers over the surface of an object, we acquire information about its microgeometry and material properties. Texture information is widely believed to be conveyed in spatial patterns of activation evoked across one of three populations of cutaneous mechanoreceptive afferents that innervate the fingertips. Here, we record the responses evoked in individual cutaneous afferents in Rhesus macaques as we scan a diverse set of natural textures across their fingertips using a custom-made rotating drum stimulator. We show that a spatial mechanism can only account for the processing of coarse textures. Information about most natural textures, however, is conveyed through precise temporal spiking patterns in afferent responses, driven by high-frequency skin vibrations elicited during scanning. Furthermore, these texture-specific spiking patterns predictably dilate or contract in time with changes in scanning speed; the systematic effect of speed on neuronal activity suggests that it can be reversed to achieve perceptual constancy across speeds. The proposed temporal coding mechanism involves converting the fine spatial structure of the surface into a temporal spiking pattern, shaped in part by the mechanical properties of the skin, and ascribes an additional function to vibration-sensitive mechanoreceptive afferents. This temporal mechanism complements the spatial one and greatly extends the range of tangible textures. We show that a combination of spatial and temporal mechanisms, mediated by all three populations of afferents, accounts for perceptual judgments of texture.
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            The sense of flutter-vibration: comparison of the human capacity with response patterns of mechanoreceptive afferents from the monkey hand.

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              Fly motion vision.

              Fly motion vision and resultant compensatory optomotor responses are a classic example for neural computation. Here we review our current understanding of processing of optic flow as generated by an animal's self-motion. Optic flow processing is accomplished in a series of steps: First, the time-varying photoreceptor signals are fed into a two-dimensional array of Reichardt-type elementary motion detectors (EMDs). EMDs compute, in parallel, local motion vectors at each sampling point in space. Second, the output signals of many EMDs are spatially integrated on the dendrites of large-field tangential cells in the lobula plate. In the third step, tangential cells form extensive interactions with each other, giving rise to their large and complex receptive fields. Thus, tangential cells can act as matched filters tuned to optic flow during particular flight maneuvers. They finally distribute their information onto postsynaptic descending neurons, which either instruct the motor centers of the thoracic ganglion for flight and locomotion control or act themselves as motor neurons that control neck muscles for head movements.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Investigation
                Role: InvestigationRole: Writing – review & editing
                Role: Data curationRole: Investigation
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                27 August 2019
                August 2019
                27 August 2019
                : 17
                : 8
                : e3000431
                Affiliations
                [1 ] Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois, United States of America
                [2 ] Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
                [3 ] Committee on Computational Neuroscience, University of Chicago, Illinois, United States of America
                Yeshiva University Albert Einstein College of Medicine, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-3974-7921
                http://orcid.org/0000-0002-9665-8901
                http://orcid.org/0000-0001-8221-7008
                Article
                PBIOLOGY-D-19-00218
                10.1371/journal.pbio.3000431
                6711498
                31454360
                407c697b-8e96-4935-a4e9-52b52680ff46
                © 2019 Delhaye et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 24 January 2019
                : 24 June 2019
                Page count
                Figures: 8, Tables: 0, Pages: 21
                Funding
                Funded by: NINDS
                Award ID: NS101325
                Award Recipient :
                Funded by: Fonds specials de Recherche
                Award Recipient :
                This work was supported by the National Institute of Neurological Disorders and Stroke NS101325 (SJB) and Fonds Specials de Recherche (BPD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Neurons
                Biology and Life Sciences
                Neuroscience
                Cellular Neuroscience
                Neurons
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Neurons
                Nerve Fibers
                Biology and Life Sciences
                Neuroscience
                Cellular Neuroscience
                Neurons
                Nerve Fibers
                Biology and Life Sciences
                Neuroscience
                Sensory Perception
                Biology and Life Sciences
                Psychology
                Sensory Perception
                Social Sciences
                Psychology
                Sensory Perception
                Biology and Life Sciences
                Anatomy
                Brain
                Somatosensory Cortex
                Medicine and Health Sciences
                Anatomy
                Brain
                Somatosensory Cortex
                Biology and Life Sciences
                Neuroscience
                Neuronal Tuning
                Biology and Life Sciences
                Psychology
                Psychophysics
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                Neuroscience
                Sensory Perception
                Psychophysics
                Biology and Life Sciences
                Psychology
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                Psychophysics
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                Sensory Perception
                Psychophysics
                Physical Sciences
                Physics
                Classical Mechanics
                Vibration
                Biology and Life Sciences
                Computational Biology
                Computational Neuroscience
                Coding Mechanisms
                Biology and Life Sciences
                Neuroscience
                Computational Neuroscience
                Coding Mechanisms
                Custom metadata
                Data available in S1 Data and at https://web.gin.g-node.org/JohnDowney/Feeling_Fooled.

                Life sciences
                Life sciences

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