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      Humans actively sample evidence to support prior beliefs

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          Abstract

          No one likes to be wrong. Previous research has shown that participants may underweight information incompatible with previous choices, a phenomenon called confirmation bias. In this paper, we argue that a similar bias exists in the way information is actively sought. We investigate how choice influences information gathering using a perceptual choice task and find that participants sample more information from a previously chosen alternative. Furthermore, the higher the confidence in the initial choice, the more biased information sampling becomes. As a consequence, when faced with the possibility of revising an earlier decision, participants are more likely to stick with their original choice, even when incorrect. Critically, we show that agency controls this phenomenon. The effect disappears in a fixed sampling condition where presentation of evidence is controlled by the experimenter, suggesting that the way in which confirmatory evidence is acquired critically impacts the decision process. These results suggest active information acquisition plays a critical role in the propagation of strongly held beliefs over time.

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          Most cited references67

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          PsychoPy—Psychophysics software in Python

          The vast majority of studies into visual processing are conducted using computer display technology. The current paper describes a new free suite of software tools designed to make this task easier, using the latest advances in hardware and software. PsychoPy is a platform-independent experimental control system written in the Python interpreted language using entirely free libraries. PsychoPy scripts are designed to be extremely easy to read and write, while retaining complete power for the user to customize the stimuli and environment. Tools are provided within the package to allow everything from stimulus presentation and response collection (from a wide range of devices) to simple data analysis such as psychometric function fitting. Most importantly, PsychoPy is highly extensible and the whole system can evolve via user contributions. If a user wants to add support for a particular stimulus, analysis or hardware device they can look at the code for existing examples, modify them and submit the modifications back into the package so that the whole community benefits.
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            Probabilistic programming in Python using PyMC3

            Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.
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              Confirmation bias: A ubiquitous phenomenon in many guises.

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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                11 April 2022
                2022
                : 11
                : e71768
                Affiliations
                [1 ] Department of Experimental Psychology, University of Oxford ( https://ror.org/052gg0110) Oxford United Kingdom
                [2 ] Wellcome Centre for Integrative Neuroimaging, University of Oxford ( https://ror.org/0172mzb45) Oxford United Kingdom
                [3 ] Institute of Cognitive Neuroscience, University College London ( https://ror.org/02jx3x895) London United Kingdom
                [4 ] Department of Mathematics and Computer Science, Rutgers University ( https://ror.org/05vt9qd57) Newark United States
                [5 ] Centre for Business Research, Cambridge Judge Business School, University of Cambridge ( https://ror.org/013meh722) Cambridge United Kingdom
                [6 ] Department of Economics and Woodrow Wilson School, Princeton University ( https://ror.org/00hx57361) Princeton United States
                [7 ] Wellcome Centre for Human Neuroimaging, University College London ( https://ror.org/02704qw51) London United Kingdom
                École normale supérieure, PSL University, INSERM France
                Brown University ( https://ror.org/05gq02987) United States
                École normale supérieure, PSL University, INSERM France
                École normale supérieure, PSL University, INSERM France
                University of Bristol ( https://ror.org/0524sp257) United Kingdom
                Author information
                https://orcid.org/0000-0002-5068-1946
                https://orcid.org/0000-0003-0159-6777
                https://orcid.org/0000-0002-5943-6621
                https://orcid.org/0000-0002-3555-2732
                Article
                71768
                10.7554/eLife.71768
                9038198
                35404234
                960af6df-3520-4d6d-9edd-ce216817a808
                © 2022, Kaanders et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 29 June 2021
                : 08 April 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: Henry Dale Fellowship
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000288, Royal Society;
                Award ID: Henry Dale Fellowship
                Award Recipient :
                Funded by: Chilean National Agency for Research and Development;
                Award ID: Scholarship
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Neuroscience
                Custom metadata
                In a perceptual choice task, human participants have a preference for sampling more information from a previously chosen alternative, indicating the presence of 'confirmation bias' in perceptual decision-making.

                Life sciences
                decision-making,confirmation bias,information sampling,human
                Life sciences
                decision-making, confirmation bias, information sampling, human

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