2021
DOI: 10.7554/elife.63436
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Optimal policy for attention-modulated decisions explains human fixation behavior

Abstract: Traditional accumulation-to-bound decision-making models assume that all choice options are processed with equal attention. In real life decisions, however, humans alternate their visual fixation between individual items to efficiently gather relevant information (Yang et al., 2016). These fixations also causally affect one's choices, biasing them toward the longer-fixated item (Krajbich et al., 2010). We derive a normative decision-making model in which attention enhances the reliability of information, consi… Show more

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Cited by 65 publications
(59 citation statements)
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References 61 publications
(123 reference statements)
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“…For example, the efficacy of allocating attention may depend upon which option is considered. In turn, the brain may dynamically refocus its attention on maximally uncertain options when prospective information gains exceed switch costs ( Callaway et al, 2021 ; Jang et al, 2021 ). Such optimal adjustment of divided attention might eventually explain systematic decision biases and shortened response times for ‘default’ choices ( Lopez-Persem et al, 2016 ).…”
Section: Discussionmentioning
confidence: 99%
“…For example, the efficacy of allocating attention may depend upon which option is considered. In turn, the brain may dynamically refocus its attention on maximally uncertain options when prospective information gains exceed switch costs ( Callaway et al, 2021 ; Jang et al, 2021 ). Such optimal adjustment of divided attention might eventually explain systematic decision biases and shortened response times for ‘default’ choices ( Lopez-Persem et al, 2016 ).…”
Section: Discussionmentioning
confidence: 99%
“…Finally, we briefly address recent studies that also apply a value inference framework to understand attention-modulated decision-making [ 11 , 42 , 43 ]. These studies interpret the switching of attention as an active sampling process and derive the switching strategy from a optimal policy.…”
Section: Discussionmentioning
confidence: 99%
“…This view not only bridges decision-making with active informationsampling (Boldt, Blundell, & De Martino, 2019;Cohen, McClure, & Yu, 2007;Gottlieb, 2018;Gottlieb, Cohanpour, Li, Singletary, & Zabeh, 2020;Gottlieb & Oudeyer, 2018;Hunt et al, 2018;Hunt, Rutledge, Malalasekera, Kennerley, & Dolan, 2016;Kaanders, Nili, O'Reilly, & Hunt, 2020), extended behaviors (Callaway, van Opheusden, et al, 2021;Holroyd & Yeung, 2012), and learning (Behrens, Woolrich, Walton, & Rushworth, 2007;Frömer et al, 2020;Nassar et al, 2012;O'Reilly, 2013), it also renders decision-making fundamentally a control problem. A group of recently proposed process models puts information search -rather than value comparison -at the core of the decision process (Callaway, Rangel, & Griffiths, 2021;Jang, Sharma, & Drugowitsch, 2021). These authors address the question how information should be sampled through gaze based on what the decision-maker knows, and doesn't know, at a given point in time.…”
Section: Controlling the Flow Of Informationmentioning
confidence: 99%
“…G. Lee & Daunizeau, 2021). Like estimates of value (Callaway, Rangel, et al, 2021;Gluth et al, 2020;Jang et al, 2021;Li & Ma, 2020), local expressions of confidence likely evolve dynamically over time (Desender et al, 2021;van den Berg et al, 2016). In perceptual decision-making, choice demands and confidence can be dissociated through manipulations of post-decision evidence (Charles & Yeung, 2019).…”
Section: Decisions and Control Over Future Research Directionsmentioning
confidence: 99%
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