66
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Mechanisms underlying cortical activity during value-guided choice

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          When choosing between two options, correlates of their value are represented in neural activity throughout the brain. Whether these representations reflect activity fundamental to the computational process of value comparison, as opposed to other computations covarying with value, is unknown. Here, we investigated activity in a biophysically plausible network model that transforms inputs relating to value into categorical choices. A set of characteristic time-varying signals emerged that reflect value comparison. We tested these model predictions in magnetoencephalography data recorded from human subjects performing value-guided decisions. Parietal and prefrontal signals matched closely with model predictions. These results provide a mechanistic explanation of neural signals recorded during value-guided choice, and a means of distinguishing computational roles of different cortical regions whose activity covaries with value.

          Related collections

          Most cited references42

          • Record: found
          • Abstract: found
          • Article: not found

          Advances in prospect theory: Cumulative representation of uncertainty

          Journal of Risk and Uncertainty, 5(4), 297-323
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The time course of perceptual choice: the leaky, competing accumulator model.

            The time course of perceptual choice is discussed in a model of gradual, leaky, stochastic, and competitive information accumulation in nonlinear decision units. Special cases of the model match a classical diffusion process, but leakage and competition work together to address several challenges to existing diffusion, random walk, and accumulator models. The model accounts for data from choice tasks using both time-controlled (e.g., response signal) and standard reaction time paradigms and its adequacy compares favorably with other approaches. A new paradigm that controls the time of arrival of information supporting different choice alternatives provides further support. The model captures choice behavior regardless of the number of alternatives, accounting for the log-linear relation between reaction time and number of alternatives (Hick's law) and explains a complex pattern of visual and contextual priming in visual word identification.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Probabilistic decision making by slow reverberation in cortical circuits.

              Recent physiological studies of alert primates have revealed cortical neural correlates of key steps in a perceptual decision-making process. To elucidate synaptic mechanisms of decision making, I investigated a biophysically realistic cortical network model for a visual discrimination experiment. In the model, slow recurrent excitation and feedback inhibition produce attractor dynamics that amplify the difference between conflicting inputs and generates a binary choice. The model is shown to account for salient characteristics of the observed decision-correlated neural activity, as well as the animal's psychometric function and reaction times. These results suggest that recurrent excitation mediated by NMDA receptors provides a candidate cellular mechanism for the slow time integration of sensory stimuli and the formation of categorical choices in a decision-making neocortical network.
                Bookmark

                Author and article information

                Journal
                9809671
                21092
                Nat Neurosci
                Nat. Neurosci.
                Nature neuroscience
                1097-6256
                1546-1726
                6 December 2011
                08 January 2012
                01 September 2012
                : 15
                : 3
                : 470-S3
                Affiliations
                [1 ]Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford OX1 3UD
                [2 ]FMRIB Centre, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU
                [3 ]Howard Hughes Medical Institute and Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305
                [4 ]Oxford Centre for Human Brain Activity (OHBA), University Department of Psychiatry, Warneford Hospital, Oxford OX3 7JX
                [5 ]Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG
                Author notes
                Correspondence and requests for materials should be addressed to T.E.J.B. ( behrens@ 123456fmrib.ox.ac.uk ).
                [* ]Corresponding author. Phone: (+44) 1865 222738; lhunt@ 123456fmrib.ox.ac.uk
                Article
                UKMS40135
                10.1038/nn.3017
                3378494
                22231429
                c7ad8ca0-3873-4609-8443-86018afb7eb5

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Funding
                Funded by: Wellcome Trust :
                Award ID: 088312 || WT
                Funded by: Wellcome Trust :
                Award ID: 080540 || WT
                Categories
                Article

                Neurosciences
                Neurosciences

                Comments

                Comment on this article