Literature DB >> 30220502

Confirmation Bias through Selective Overweighting of Choice-Consistent Evidence.

Bharath Chandra Talluri1, Anne E Urai2, Konstantinos Tsetsos3, Marius Usher4, Tobias H Donner5.   

Abstract

People's assessments of the state of the world often deviate systematically from the information available to them [1]. Such biases can originate from people's own decisions: committing to a categorical proposition, or a course of action, biases subsequent judgment and decision-making. This phenomenon, called confirmation bias [2], has been explained as suppression of post-decisional dissonance [3, 4]. Here, we provide insights into the underlying mechanism. It is commonly held that decisions result from the accumulation of samples of evidence informing about the state of the world [5-8]. We hypothesized that choices bias the accumulation process by selectively altering the weighting (gain) of subsequent evidence, akin to selective attention. We developed a novel psychophysical task to test this idea. Participants viewed two successive random dot motion stimuli and made two motion-direction judgments: a categorical discrimination after the first stimulus and a continuous estimation of the overall direction across both stimuli after the second stimulus. Participants' sensitivity for the second stimulus was selectively enhanced when that stimulus was consistent with the initial choice (compared to both, first stimuli and choice-inconsistent second stimuli). A model entailing choice-dependent selective gain modulation explained this effect better than several alternative mechanisms. Choice-dependent gain modulation was also established in another task entailing averaging of numerical values instead of motion directions. We conclude that intermittent choices direct selective attention during the evaluation of subsequent evidence, possibly due to decision-related feedback in the brain [9]. Our results point to a recurrent interplay between decision-making and selective attention.
Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  attention; computational model; decision-making; human; numerical cognition; perception; psychophysics

Mesh:

Year:  2018        PMID: 30220502     DOI: 10.1016/j.cub.2018.07.052

Source DB:  PubMed          Journal:  Curr Biol        ISSN: 0960-9822            Impact factor:   10.834


  32 in total

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2.  Decision Making through Integration of Sensory Evidence at Prolonged Timescales.

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Review 3.  Optimal models of decision-making in dynamic environments.

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5.  Efficient sampling and noisy decisions.

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6.  Boosts in brain signal variability track liberal shifts in decision bias.

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7.  Dynamic Representation of the Subjective Value of Information.

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8.  Real-world evaluation of a computed tomography-first triage strategy for suspected Coronavirus disease 2019 in outpatients in Japan: An observational cohort study.

Authors:  Shigeta Miyake; Takuma Higurashi; Takashi Jono; Taisuke Akimoto; Fumihiro Ogawa; Yasufumi Oi; Katsushi Tanaka; Yu Hara; Nobuaki Kobayashi; Hideaki Kato; Tsuneo Yamashiro; Daisuke Utsunomiya; Atsushi Nakajima; Tetsuya Yamamoto; Shin Maeda; Takeshi Kaneko; Ichiro Takeuchi
Journal:  Medicine (Baltimore)       Date:  2021-06-04       Impact factor: 1.817

9.  The Flat Earth Theory: is Evidence-Based Physiotherapy a Sphere?

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Journal:  J Man Manip Ther       Date:  2021-04

10.  Increased influence of prior choices on perceptual decisions in autism.

Authors:  Helen Feigin; Shir Shalom-Sperber; Ditza A Zachor; Adam Zaidel
Journal:  Elife       Date:  2021-07-07       Impact factor: 8.140

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