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      Changing your mind: a computational mechanism of vacillation

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          Abstract

          A decision is a commitment to a proposition or plan of action based on evidence and expected costs and benefits associated with the outcome. Progress in a variety of fields has led to a quantitative understanding of the mechanisms that evaluate evidence and reach a decision 1- 3. Several formalisms propose that a representation of noisy evidence is evaluated against a criterion to produce a decision 4- 8. Without additional evidence, however, these formalisms fail to explain why a decision-maker would change her mind. Here, we extend a model, developed to account for both the timing and accuracy of the initial decision 9, to explain subsequent changes of mind. Subjects made decisions about a noisy visual stimulus, which they indicated by moving a handle. Although they received no additional information after initiating their movement, their hand trajectories betrayed a change of mind on some trials. We propose that noisy evidence is accumulated over time until it reaches a criterion, or bound which determines the initial decision and that the brain exploits information that is in the processing pipeline when the initial decision is made to subsequently either reverse or reaffirm the initial decision. The model explains both the frequency of changes of mind as well as their dependence on both task difficulty and whether the initial decision was accurate or erroneous. The theoretical and experimental findings advance the understanding of decision making to the highly flexible and cognitive act of vacillation and self-correction.

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          Most cited references25

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          The Jackknife, the Bootstrap and Other Resampling Plans

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            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.
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              Probabilistic population codes for Bayesian decision making.

              When making a decision, one must first accumulate evidence, often over time, and then select the appropriate action. Here, we present a neural model of decision making that can perform both evidence accumulation and action selection optimally. More specifically, we show that, given a Poisson-like distribution of spike counts, biological neural networks can accumulate evidence without loss of information through linear integration of neural activity and can select the most likely action through attractor dynamics. This holds for arbitrary correlations, any tuning curves, continuous and discrete variables, and sensory evidence whose reliability varies over time. Our model predicts that the neurons in the lateral intraparietal cortex involved in evidence accumulation encode, on every trial, a probability distribution which predicts the animal's performance. We present experimental evidence consistent with this prediction and discuss other predictions applicable to more general settings.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                4 August 2009
                19 August 2009
                10 September 2009
                24 May 2010
                : 461
                : 7261
                : 263-266
                Affiliations
                [1 ]Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK
                [2 ]Howard Hughes Medical Institute, Janelia Farm Research Campus, 19700 Helix Drive, Ashburn, VA 20147
                [3 ]Howard Hughes Medical Institute, National Primate Research Center, and Department of Physiology and Biophysics, University of Washington, Seattle, Washington 98195
                Author notes
                Correspondence and requests for materials should be addressed to M.N.S. ( shadlen@ 123456uw.edu ).

                Author Information Reprints and permissions information is available at www.nature.com/reprints.

                Article
                UKMS27425
                10.1038/nature08275
                2875179
                19693010
                3fc050a8-a88e-4417-8527-ca510d575f42

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                History
                Funding
                Funded by: Wellcome Trust :
                Award ID: 077730 || WT
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