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      Triple Dissociation of Attention and Decision Computations across Prefrontal Cortex

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          Abstract

          Naturalistic decision-making typically involves sequential deployment of attention to choice alternatives to gather information before a decision is made. Attention filters how information enters decision circuits, implying attentional control may shape how decision computations unfold. We recorded neuronal activity from three subregions of prefrontal cortex (PFC) while monkeys performed an attention-guided decision-making task. From the first saccade to decision-relevant information, a triple dissociation of decision- and attention-related computations emerged in parallel across PFC subregions. During subsequent saccades, orbitofrontal cortex activity reflected value comparison between currently and previously attended information. By contrast, anterior cingulate cortex carried several signals reflecting belief updating in light of newly attended information, integration of evidence to a decision bound, and an emerging plan for what action to choose. Our findings show how anatomically dissociable PFC representations evolve during attention-guided information search, supporting computations critical for value-guided choice.

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          Most cited references43

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          How does the brain solve visual object recognition?

          Mounting evidence suggests that 'core object recognition,' the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. However, the algorithm that produces this solution remains poorly understood. Here we review evidence ranging from individual neurons and neuronal populations to behavior and computational models. We propose that understanding this algorithm will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical subnetworks with a common functional goal. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Constructive Consumer Choice Processes

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              Visual fixations and the computation and comparison of value in simple choice.

              Most organisms facing a choice between multiple stimuli will look repeatedly at them, presumably implementing a comparison process between the items' values. Little is known about the nature of the comparison process in value-based decision-making or about the role of visual fixations in this process. We created a computational model of value-based binary choice in which fixations guide the comparison process and tested it on humans using eye-tracking. We found that the model can quantitatively explain complex relationships between fixation patterns and choices, as well as several fixation-driven decision biases.
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                Author and article information

                Journal
                9809671
                21092
                Nat Neurosci
                Nat. Neurosci.
                Nature neuroscience
                1097-6256
                1546-1726
                17 December 2018
                26 September 2018
                October 2018
                26 March 2019
                : 21
                : 10
                : 1471-1481
                Affiliations
                [1 ]Sobell Department of Motor Neuroscience, University College London
                [2 ]Wellcome Centre for Human Neuroimaging, University College London
                [3 ]Wellcome Centre for Integrative Neuroimaging, University of Oxford
                [4 ]Oxford Health NHS Foundation Trust
                [5 ]International Neuroscience Doctoral Programme, Champalimaud Foundation
                [6 ]Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa
                Author notes
                Article
                EMS79186
                10.1038/s41593-018-0239-5
                6331040
                30258238
                2375afc7-fd7f-408f-bc07-9bd5b75d3b62

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                Neurosciences

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