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      Dynamics of absolute and relative disparity processing in human visual cortex

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          Abstract

          Cortical processing of binocular disparity is believed to begin in V1 where cells are sensitive to absolute disparity, followed by the extraction of relative disparity in higher visual areas. While much is known about the cortical distribution and spatial tuning of disparity-selective neurons, the relationship between their spatial and temporal properties is less well understood. Here, we use steady-state Visual Evoked Potentials and dynamic random dot stereograms to characterize the temporal dynamics of spatial mechanisms in human visual cortex that are primarily sensitive to either absolute or relative disparity. Stereograms alternated between disparate and non-disparate states at 2 Hz. By varying the disparity-defined spatial frequency content of the stereograms from a planar surface to corrugated ones, we biased responses towards absolute vs. relative disparities. Reliable Components Analysis was used to derive two dominant sources from the 128 channel EEG records. The first component (RC1) was maximal over the occipital pole. In RC1, first harmonic responses were sustained, tuned for corrugation frequency, and sensitive to the presence of disparity references, consistent with prior psychophysical sensitivity measurements. By contrast, the second harmonic, associated with transient processing, was not spatially tuned and was indifferent to references, consistent with it being generated by an absolute disparity mechanism. Thus, our results reveal a duplex coding strategy in the disparity domain, where relative disparities are computed via sustained mechanisms and absolute disparities are computed via transient mechanisms.

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          Most cited references85

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          The Psychophysics Toolbox.

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          The Psychophysics Toolbox is a software package that supports visual psychophysics. Its routines provide an interface between a high-level interpreted language (MATLAB on the Macintosh) and the video display hardware. A set of example programs is included with the Toolbox distribution.
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            On the interpretation of weight vectors of linear models in multivariate neuroimaging.

            The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.
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              Generalized eta and omega squared statistics: measures of effect size for some common research designs.

              The editorial policies of several prominent educational and psychological journals require that researchers report some measure of effect size along with tests for statistical significance. In analysis of variance contexts, this requirement might be met by using eta squared or omega squared statistics. Current procedures for computing these measures of effect often do not consider the effect that design features of the study have on the size of these statistics. Because research-design features can have a large effect on the estimated proportion of explained variance, the use of partial eta or omega squared can be misleading. The present article provides formulas for computing generalized eta and omega squared statistics, which provide estimates of effect size that are comparable across a variety of research designs.
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                Author and article information

                Journal
                9215515
                20498
                Neuroimage
                Neuroimage
                NeuroImage
                1053-8119
                1095-9572
                5 June 2022
                15 July 2022
                07 April 2022
                15 July 2022
                : 255
                : 119186
                Affiliations
                [a ]Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA, USA
                [b ]Department of Psychology, Stanford University, 450 Jane Stanford Way, Stanford, CA, USA
                Author notes
                [* ]Corresponding author at: Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA, USA. milenak@ 123456stanford.edu (M. Kaestner).
                Article
                NIHMS1812225
                10.1016/j.neuroimage.2022.119186
                9205266
                35398280
                16c8e1f2-5ea5-4ae7-bf2b-eb30d1e08e84

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/)

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                Categories
                Article

                Neurosciences
                binocular vision,disparity,ssvep,transient,sustained
                Neurosciences
                binocular vision, disparity, ssvep, transient, sustained

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