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      Serial dependence in the perception of visual variance

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          Abstract

          The recent history of perceptual experience has been shown to influence subsequent perception. Classically, this dependence on perceptual history has been examined in sensory-adaptation paradigms, wherein prolonged exposure to a particular stimulus (e.g., a vertically oriented grating) produces changes in perception of subsequently presented stimuli (e.g., the tilt aftereffect). More recently, several studies have investigated the influence of shorter perceptual exposure with effects, referred to as serial dependence, being described for a variety of low- and high-level perceptual dimensions. In this study, we examined serial dependence in the processing of dispersion statistics, namely variance—a key descriptor of the environment and indicative of the precision and reliability of ensemble representations. We found two opposite serial dependences operating at different timescales, and likely originating at different processing levels: A positive, Bayesian-like bias was driven by the most recent exposures, dependent on feature-specific decision making and appearing only when high confidence was placed in that decision; and a longer lasting negative bias—akin to an adaptation aftereffect—becoming manifest as the positive bias declined. Both effects were independent of spatial presentation location and the similarity of other close traits, such as mean direction of the visual variance stimulus. These findings suggest that visual variance processing occurs in high-level areas but is also subject to a combination of multilevel mechanisms balancing perceptual stability and sensitivity, as with many different perceptual dimensions.

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          Serial dependence in visual perception

          Visual input often arrives in a noisy and discontinuous stream, owing to head and eye movements, occlusion, lighting changes, and many other factors. Yet the physical world is generally stable—objects and physical characteristics rarely change spontaneously. How then does the human visual system capitalize on continuity in the physical environment over time? Here we show that visual perception is serially dependent, using both prior and present input to inform perception at the present moment. Using an orientation judgment task, we found that even when visual input changes randomly over time, perceived orientation is strongly and systematically biased toward recently seen stimuli. Further, the strength of this bias is modulated by attention and tuned to the spatial and temporal proximity of successive stimuli. These results reveal a serial dependence in perception characterized by a spatiotemporally tuned, orientation-selective operator—which we call a continuity field—that may promote visual stability over time.
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            Efficiency and ambiguity in an adaptive neural code.

            We examine the dynamics of a neural code in the context of stimuli whose statistical properties are themselves evolving dynamically. Adaptation to these statistics occurs over a wide range of timescales-from tens of milliseconds to minutes. Rapid components of adaptation serve to optimize the information that action potentials carry about rapid stimulus variations within the local statistical ensemble, while changes in the rate and statistics of action-potential firing encode information about the ensemble itself, thus resolving potential ambiguities. The speed with which information is optimized and ambiguities are resolved approaches the physical limit imposed by statistical sampling and noise.
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              Metamers of the ventral stream

              The human capacity to recognize complex visual patterns emerges in a sequence of brain areas known as the ventral stream, beginning with primary visual cortex (V1). We develop a population model for mid-ventral processing, in which non-linear combinations of V1 responses are averaged within receptive fields that grow with eccentricity. To test the model, we generate novel forms of visual metamers — stimuli that differ physically, but look the same. We develop a behavioral protocol that uses metameric stimuli to estimate the receptive field sizes in which the model features are represented. Because receptive field sizes change along the ventral stream, the behavioral results can identify the visual area corresponding to the representation. Measurements in human observers implicate V2, providing a new functional account of this area. The model explains deficits of peripheral vision known as “crowding”, and provides a quantitative framework for assessing the capabilities of everyday vision.
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                Author and article information

                Contributors
                Journal
                J Vis
                J Vis
                jovi
                J Vis
                JOVI
                Journal of Vision
                The Association for Research in Vision and Ophthalmology
                1534-7362
                2018
                2 July 2018
                : 18
                : 7
                : 4
                Affiliations
                M.Suarez-Pinilla@ 123456sussex.ac.uk
                A.K.Seth@ 123456sussex.ac.uk
                wjroseboom@ 123456gmail.com
                [1]Sackler Center for Consciousness Science and Department of Informatics, University of Sussex, Brighton, UK
                [2]Sackler Center for Consciousness Science and Department of Informatics, University of Sussex, Brighton, UK
                [3]Sackler Center for Consciousness Science and Department of Informatics, University of Sussex, Brighton, UK
                Article
                jovi-18-06-14 JOV-06075-2018
                10.1167/18.7.4
                6028984
                29971350
                cf25078f-d10a-4284-af51-0d103a0355d3
                Copyright 2018 The Authors

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                History
                : 17 January 2018
                : 11 May 2018
                Categories
                Article

                serial dependence,visual variance,ensemble processing,adaptation aftereffects

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