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      Inversion of pop-out for a distracting feature dimension in monkey visual cortex

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          Goal-directed behaviors like visual search involve both the selection of behaviorally relevant targets and the suppression of task-irrelevant distractors. This is especially important if distractors are salient and capture attention. Here we demonstrate that nonhuman primates suppress a salient color distractor while searching for a target that is defined by shape, i.e., another feature dimension. The neuronal activity of V4 neurons revealed the temporal evolution of target selection and distractor suppression. The neuronal responses elicited by the pop-out target stimuli were enhanced, whereas responses elicited by salient pop-out color distractors were suppressed, after an initial brief phase of response enhancement. Our results reveal a “pop-in” mechanism by which the visual cortex inverts an attentional capture signal into suppression to facilitate visual search.

          Abstract

          During visual search, it is important to reduce the interference of distracting objects in the scene. The neuronal responses elicited by the search target stimulus are typically enhanced. However, it is equally important to suppress the representations of distracting stimuli, especially if they are salient and capture attention. We trained monkeys to make an eye movement to a unique “pop-out” shape stimulus among an array of distracting stimuli. One of these distractors had a salient color that varied across trials and differed from the color of the other stimuli, causing it to also pop-out. The monkeys were able to select the pop-out shape target with high accuracy and actively avoided the pop-out color distractor. This behavioral pattern was reflected in the activity of neurons in area V4. Responses to the shape targets were enhanced, while the activity evoked by the pop-out color distractor was only briefly enhanced, directly followed by a sustained period of pronounced suppression. These behavioral and neuronal results demonstrate a cortical selection mechanism that rapidly inverts a pop-out signal to “pop-in” for an entire feature dimension thereby facilitating goal-directed visual search in the presence of salient distractors.

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          Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.

          We describe a model of visual processing in which feedback connections from a higher- to a lower-order visual cortical area carry predictions of lower-level neural activities, whereas the feedforward connections carry the residual errors between the predictions and the actual lower-level activities. When exposed to natural images, a hierarchical network of model neurons implementing such a model developed simple-cell-like receptive fields. A subset of neurons responsible for carrying the residual errors showed endstopping and other extra-classical receptive-field effects. These results suggest that rather than being exclusively feedforward phenomena, nonclassical surround effects in the visual cortex may also result from cortico-cortical feedback as a consequence of the visual system using an efficient hierarchical strategy for encoding natural images.
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            A theory of cortical responses.

            This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modern-day statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts.It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain's free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain's attempt to minimize the free energy induced by a stimulus and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organization and responses. The aim of this article is to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective. In terms of cortical architectures, the theoretical treatment predicts that sensory cortex should be arranged hierarchically, that connections should be reciprocal and that forward and backward connections should show a functional asymmetry (forward connections are driving, whereas backward connections are both driving and modulatory). In terms of synaptic physiology, it predicts associative plasticity and, for dynamic models, spike-timing-dependent plasticity. In terms of electrophysiology, it accounts for classical and extra classical receptive field effects and long-latency or endogenous components of evoked cortical responses. It predicts the attenuation of responses encoding prediction error with perceptual learning and explains many phenomena such as repetition suppression, mismatch negativity (MMN) and the P300 in electroencephalography. In psychophysical terms, it accounts for the behavioural correlates of these physiological phenomena, for example, priming and global precedence. The final focus of this article is on perceptual learning as measured with the MMN and the implications for empirical studies of coupling among cortical areas using evoked sensory responses.
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              The distinct modes of vision offered by feedforward and recurrent processing.

              An analysis of response latencies shows that when an image is presented to the visual system, neuronal activity is rapidly routed to a large number of visual areas. However, the activity of cortical neurons is not determined by this feedforward sweep alone. Horizontal connections within areas, and higher areas providing feedback, result in dynamic changes in tuning. The differences between feedforward and recurrent processing could prove pivotal in understanding the distinctions between attentive and pre-attentive vision as well as between conscious and unconscious vision. The feedforward sweep rapidly groups feature constellations that are hardwired in the visual brain, yet is probably incapable of yielding visual awareness; in many cases, recurrent processing is necessary before the features of an object are attentively grouped and the stimulus can enter consciousness.
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                Author and article information

                Contributors
                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                22 February 2023
                28 February 2023
                22 August 2023
                : 120
                : 9
                : e2210839120
                Affiliations
                [1] aDepartment of Vision and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences , 1105 BA, Amsterdam, The Netherlands
                [2] bExperimental Psychology, Helmholtz Institute, Utrecht University , 3584 CS, Utrecht, The Netherlands
                [3] cLaboratory of Visual Brain Therapy, Sorbonne Université, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut de la Vision , Paris F-75012, France
                [4] dDepartment of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, Vrije Universiteit , 1081 HV, Amsterdam, The Netherlands
                [5] eDepartment of Psychiatry, Amsterdam UMC, University of Amsterdam , 1105 AZ, Amsterdam, The Netherlands
                Author notes
                2To whom correspondence may be addressed. Email: c.klink@ 123456nin.knaw.nl or p.roelfsema@ 123456nin.knaw.nl .

                Edited by Charles E. Connor, Johns Hopkins University, Baltimore, MD; received June 27, 2022; accepted January 25, 2023 by Editorial Board Member Charles D. Gilbert

                1P.C.K. and R.R.M.T. contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-6784-7842
                https://orcid.org/0000-0003-0800-0278
                https://orcid.org/0000-0002-1625-0034
                Article
                202210839
                10.1073/pnas.2210839120
                9992771
                36812207
                5d144104-c760-46b6-8d2e-6c56b8f57dc4
                Copyright © 2023 the Author(s). Published by PNAS.

                This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                : 27 June 2022
                : 25 January 2023
                Page count
                Pages: 9, Words: 7054
                Funding
                Funded by: Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), FundRef 501100003246;
                Award ID: Crossover Program 17619 "INTENSE"
                Award Recipient : P. Christiaan Klink Award Recipient : Pieter R. Roelfsema
                Funded by: Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), FundRef 501100003246;
                Award ID: VENI 451.13.023
                Award Recipient : P. Christiaan Klink Award Recipient : Pieter R. Roelfsema
                Funded by: European Union FP7;
                Award ID: ERC 339490 "Cortic_al_gorithms"
                Award Recipient : P. Christiaan Klink Award Recipient : Rob R.M. Teeuwen Award Recipient : Pieter R. Roelfsema
                Funded by: The Human Brain Project;
                Award ID: Agreement No. 945539 "Human Brain Project SGA3"
                Award Recipient : P. Christiaan Klink Award Recipient : Rob R.M. Teeuwen Award Recipient : Pieter R. Roelfsema
                Funded by: Friends Foundation of the Netherlands Institute for Neuroscience;
                Award ID: n/a
                Award Recipient : P. Christiaan Klink Award Recipient : Rob R.M. Teeuwen Award Recipient : Pieter R. Roelfsema
                Categories
                research-article, Research Article
                neuro, Neuroscience
                424
                Biological Sciences
                Neuroscience

                visual search,v4,monkey,suppression,enhancement
                visual search, v4, monkey, suppression, enhancement

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