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      Humans can efficiently look for but not select multiple visual objects

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          Abstract

          The human brain recurrently prioritizes task-relevant over task-irrelevant visual information. A central question is whether multiple objects can be prioritized simultaneously. To answer this, we let observers search for two colored targets among distractors. Crucially, we independently varied the number of target colors that observers anticipated, and the number of target colors actually used to distinguish the targets in the display. This enabled us to dissociate the preparation of selection mechanisms from the actual engagement of such mechanisms. Multivariate classification of electroencephalographic activity allowed us to track selection of each target separately across time. The results revealed only small neural and behavioral costs associated with preparing for selecting two objects, but substantial costs when engaging in selection. Further analyses suggest this cost is the consequence of neural competition resulting in limited parallel processing, rather than a serial bottleneck. The findings bridge diverging theoretical perspectives on capacity limitations of feature-based attention.

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          Most cited references59

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          Guided Search 2.0 A revised model of visual search.

          An important component of routine visual behavior is the ability to find one item in a visual world filled with other, distracting items. This ability to performvisual search has been the subject of a large body of research in the past 15 years. This paper reviews the visual search literature and presents a model of human search behavior. Built upon the work of Neisser, Treisman, Julesz, and others, the model distinguishes between a preattentive, massively parallel stage that processes information about basic visual features (color, motion, various depth cues, etc.) across large portions of the visual field and a subsequent limited-capacity stage that performs other, more complex operations (e.g., face recognition, reading, object identification) over a limited portion of the visual field. The spatial deployment of the limited-capacity process is under attentional control. The heart of the guided search model is the idea that attentional deployment of limited resources isguided by the output of the earlier parallel processes. Guided Search 2.0 (GS2) is a revision of the model in which virtually all aspects of the model have been made more explicit and/or revised in light of new data. The paper is organized into four parts: Part 1 presents the model and the details of its computer simulation. Part 2 reviews the visual search literature on preattentive processing of basic features and shows how the GS2 simulation reproduces those results. Part 3 reviews the literature on the attentional deployment of limited-capacity processes in conjunction and serial searches and shows how the simulation handles those conditions. Finally, Part 4 deals with shortcomings of the model and unresolved issues.
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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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              Neural activity predicts individual differences in visual working memory capacity.

              Contrary to our rich phenomenological visual experience, our visual short-term memory system can maintain representations of only three to four objects at any given moment. For over a century, the capacity of visual memory has been shown to vary substantially across individuals, ranging from 1.5 to about 5 objects. Although numerous studies have recently begun to characterize the neural substrates of visual memory processes, a neurophysiological index of storage capacity limitations has not yet been established. Here, we provide electrophysiological evidence for lateralized activity in humans that reflects the encoding and maintenance of items in visual memory. The amplitude of this activity is strongly modulated by the number of objects being held in the memory at the time, but approaches a limit asymptotically for arrays that meet or exceed storage capacity. Indeed, the precise limit is determined by each individual's memory capacity, such that the activity from low-capacity individuals reaches this plateau much sooner than that from high-capacity individuals. Consequently, this measure provides a strong neurophysiological predictor of an individual's capacity, allowing the demonstration of a direct relationship between neural activity and memory capacity.
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                Author and article information

                Contributors
                Role: Senior Editor
                Role: Reviewing Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                27 August 2019
                2019
                : 8
                : e49130
                Affiliations
                [1 ]deptDepartment of Experimental and Applied Psychology Vrije Universiteit Amsterdam AmsterdamNetherlands
                [2 ]Institute for Brain and Behavior Amsterdam AmsterdamNetherlands
                [3 ]deptDepartment of Brain and Cognition University of Amsterdam AmsterdamNetherlands
                [4 ]deptExperimental Psychology Utrecht University UtrechtNetherlands
                [5 ]deptDepartment of Psychological Sciences Birkbeck College, University of London LondonUnited Kingdom
                University of Pennsylvania, Philadelphia United States
                Peking University China
                Peking University China
                Chinese University of Hong Kong Hong Kong
                Author information
                https://orcid.org/0000-0001-5546-3561
                https://orcid.org/0000-0002-9025-3436
                https://orcid.org/0000-0001-7470-5378
                Article
                49130
                10.7554/eLife.49130
                6733593
                31453807
                bfb24ad2-19fc-41c4-9677-67b67b199ad9
                © 2019, Ort et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 07 June 2019
                : 26 August 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek;
                Award ID: 464-13-003
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: ERC-2013-CoG-615423
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Neuroscience
                Custom metadata
                During search for multiple targets, individuals can efficiently prepare to select multiple target objects simultaneously, but the actual selection of those objects from the sensory input is limited.

                Life sciences
                feature-based attention,visual search,attentional template,multiple targets,eeg,mvpa,human
                Life sciences
                feature-based attention, visual search, attentional template, multiple targets, eeg, mvpa, human

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