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      Object Vision in a Structured World

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          Abstract

          In natural vision, objects appear at typical locations, both with respect to visual space (e.g., an airplane in the upper part of a scene) and other objects (e.g., a lamp above a table). Recent studies have shown that object vision is strongly adapted to such positional regularities. In this review we synthesize these developments, highlighting that adaptations to positional regularities facilitate object detection and recognition, and sharpen the representations of objects in visual cortex. These effects are pervasive across various types of high-level content. We posit that adaptations to real-world structure collectively support optimal usage of limited cortical processing resources. Taking positional regularities into account will thus be essential for understanding efficient object vision in the real world.

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Speed of processing in the human visual system.

            How long does it take for the human visual system to process a complex natural image? Subjectively, recognition of familiar objects and scenes appears to be virtually instantaneous, but measuring this processing time experimentally has proved difficult. Behavioural measures such as reaction times can be used, but these include not only visual processing but also the time required for response execution. However, event-related potentials (ERPs) can sometimes reveal signs of neural processing well before the motor output. Here we use a go/no-go categorization task in which subjects have to decide whether a previously unseen photograph, flashed on for just 20 ms, contains an animal. ERP analysis revealed a frontal negativity specific to no-go trials that develops roughly 150 ms after stimulus onset. We conclude that the visual processing needed to perform this highly demanding task can be achieved in under 150 ms.
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              Neuronal synchrony: a versatile code for the definition of relations?

              W. Singer (1999)
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                Author and article information

                Journal
                9708669
                Trends Cogn Sci
                Trends Cogn Sci
                Trends in cognitive sciences
                1364-6613
                1879-307X
                17 November 2021
                01 August 2019
                27 May 2019
                23 November 2021
                : 23
                : 8
                : 672-685
                Affiliations
                [1 ]Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
                [2 ]Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
                [3 ]Berlin School of Mind and Brain, Humboldt-Universität Berlin, Berlin, Germany
                [4 ]Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
                Author notes
                [* ]Correspondence: danielkaiser.net@ 123456gmail.com (D. Kaiser) and m.peelen@ 123456donders.ru.nl (M.V. Peelen).
                Article
                EMS138945
                10.1016/j.tics.2019.04.013
                7612023
                31147151
                b787ce8e-2d95-4d7e-b1b1-2b053043d08b

                This is an open access article underthe CC BY license ( https://creativecommons.org/licenses/by/4.0/).

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                Neurosciences
                Neurosciences

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