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      Evidence accumulation is biased by motivation: A computational account

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      1 , * , 1 , 2 , 1 , *
      PLoS Computational Biology
      Public Library of Science

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          Abstract

          To make good judgments people gather information. An important problem an agent needs to solve is when to continue sampling data and when to stop gathering evidence. We examine whether and how the desire to hold a certain belief influences the amount of information participants require to form that belief. Participants completed a sequential sampling task in which they were incentivized to accurately judge whether they were in a desirable state, which was associated with greater rewards than losses, or an undesirable state, which was associated with greater losses than rewards. While one state was better than the other, participants had no control over which they were in, and to maximize rewards they had to maximize accuracy. Results show that participants’ judgments were biased towards believing they were in the desirable state. They required a smaller proportion of supporting evidence to reach that conclusion and ceased gathering samples earlier when reaching the desirable conclusion. The findings were replicated in an additional sample of participants. To examine how this behavior was generated we modeled the data using a drift-diffusion model. This enabled us to assess two potential mechanisms which could be underlying the behavior: (i) a valence-dependent response bias and/or (ii) a valence-dependent process bias. We found that a valence-dependent model, with both a response bias and a process bias, fit the data better than a range of other alternatives, including valence-independent models and models with only a response or process bias. Moreover, the valence-dependent model provided better out-of-sample prediction accuracy than the valence-independent model. Our results provide an account for how the motivation to hold a certain belief decreases the need for supporting evidence. The findings also highlight the advantage of incorporating valence into evidence accumulation models to better explain and predict behavior.

          Author summary

          People tend to gather information before making judgments. As information is often unlimited a decision has to be made as to when the data is sufficient to reach a conclusion. Here, we show that the decision to stop gathering data is influenced by whether the data points towards the desired conclusion. Importantly, we characterize the factors that generate this behaviour using a valence-dependent evidence accumulation model. In a sequential sampling task participants sampled less evidence before reaching a desirable than undesirable conclusion. Despite being incentivized for accuracy, participants’judgments were biased towards believing they were in a desirable state. Fitting the data to an evidence accumulation model revealed this behavior was due both to the starting point and rate of evidence accumulation being biased towards desirable beliefs. Our results show that evidence accumulation is altered by what people want to believe and provide an account for how this modulation is generated.

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          Most cited references30

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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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            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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              Neural correlates of decision variables in parietal cortex.

              Decision theory proposes that humans and animals decide what to do in a given situation by assessing the relative value of each possible response. This assessment can be computed, in part, from the probability that each action will result in a gain and the magnitude of the gain expected. Here we show that the gain (or reward) a monkey can expect to realize from an eye-movement response modulates the activity of neurons in the lateral intraparietal area, an area of primate cortex that is thought to transform visual signals into eye-movement commands. We also show that the activity of these neurons is sensitive to the probability that a particular response will result in a gain. When animals can choose freely between two alternative responses, the choices subjects make and neuronal activation in this area are both correlated with the relative amount of gain that the animal can expect from each response. Our data indicate that a decision-theoretic model may provide a powerful new framework for studying the neural processes that intervene between sensation and action.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                27 June 2019
                June 2019
                : 15
                : 6
                : e1007089
                Affiliations
                [1 ] Affective Brain Lab, Department of Experimental Psychology, University College London, London, United Kingdom
                [2 ] Google, Mountain View, California, United States of America
                McGill, CANADA
                Author notes

                Authors FG and TS have no conflict of interests. DC declares to have been employed by Google LLC, Mountain View, at the time of preparing the article for publication.

                Author information
                http://orcid.org/0000-0002-3355-8049
                http://orcid.org/0000-0002-8384-6292
                Article
                PCOMPBIOL-D-18-01257
                10.1371/journal.pcbi.1007089
                6597032
                31246955
                6475d4ee-1b33-4c01-8171-f8206600577f
                © 2019 Gesiarz et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 25 July 2018
                : 10 May 2019
                Page count
                Figures: 4, Tables: 2, Pages: 15
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 214268/Z/18/Z
                Award Recipient :
                Funded by a Wellcome Trust Fellowship 214268/Z/18/Z to TS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                All files are available from the Github https://github.com/affective-brain-lab/Gesiarz_Evidence_Motivation.

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