The (Neural) Dynamics of Stochastic Choice

Webb, Ryan

doi:10.1287/mnsc.2017.2931

Cited by 93 publications

(65 citation statements)

References 128 publications

(177 reference statements)

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“…For instance, when choosing what movie to watch or what food to order off a menu, we must often search through a large number of alternatives. While much effort has been devoted to understanding the mechanisms underlying two-alternative forced choice (2AFC) in value-based decisionmaking (Alós-Ferrer, 2018;Bhatia, 2013;Boorman, Rushworth & Behrens, 2013;Clithero, 2018;De Martino, Kumaran, Seymour, & Dolan, 2006;Hare, Camerer & Rangel, 2009;Hunt, Malalasekera, de Berker, Miranda, Farmer, et al, 2018;Hutcherson, Bushong & Rangel, 2015;Krajbich, Armel & Rangel, 2010;Mormann, Malmaud, Huth, et al, 2010;Philiastides & Ratcliff, 2013;Polonia, Woodford & Ruff, 2019;Rodriquez, Turner & McClure, 2014;Webb. 2019) and choices involving three to four alternatives (Berkowitsch, Scheibehenne & Rieskamp, 2014;Diederich, 2003;Gluth, Spektor & Rieskamp, 2018;Gluth, Kern, Kortmann & Vitali, 2020;Noguchi & Stewart, 2014;Roe, Busemeyer & Townsend, 2001;Towal, Mormann, & Koch, 2013;Trueblood, Brown & Heathcote, 2014;Usher & McClelland, 2004), comparably little has been done to investigate many-alternative forced choices (MAFC, more than four alternatives) (Ashby, Jekel, Dickert & Glöckner, 2016;Payne, 1976;Reutskaja, Nagel, Camerer, & Rangel, 2011).…”

Section: Introductionmentioning

confidence: 99%

Uncovering the Computational Mechanisms Underlying Many-Alternative Choice

Thomas

Molter²,

Krajbich³

2020

Preprint

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How do we choose when confronted with many alternatives (e.g., choosing which soda to buy at the supermarket)? While studies of the decision mechanisms underlying two- to three-alternative choice are common in the literature, there is comparably little decision modeling work with larger choice sets, despite their prevalence in everyday life. Even further, there is an apparent disconnect between research in small choice sets, supporting a process of gaze-driven evidence accumulation, and research in larger choice sets, arguing for models of optimal choice, satisficing, and hybrids of the two. Here, we bridge this divide by comparing these models in a many-alternative value-based choice experiment with 9, 16, 25 or 36 choice alternatives. We find that human subjects' choice behaviour does not match the assumptions of satisficing or optimal choice, while a hybrid of the two captures response times and choices well. Yet, only a process of gaze-driven evidence accumulation is able to also accurately capture the observed association between gaze allocation and choices.

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Section: Introductionmentioning

confidence: 99%

Uncovering the Computational Mechanisms Underlying Many-Alternative Choice

Thomas

Molter²,

Krajbich³

2020

Preprint

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“…economics has followed suit (Wilcox, 1993;Rubinstein, 2007;Chabris, Morris, Taubinsky, Laibson, & Schuldt, 2009;Rand, Greene, & Nowak, 2012;Spiliopoulos & Ortmann, 2018;Clithero, 2016Clithero, , 2018. To rigorously apply these insights, researchers have developed computational models that jointly incorporate stochastic choice and time expenditure (e.g., Ratcliff, 1978;Busemeyer & Townsend, 1993;Woodford, 2014;Fudenberg, Strack, & Strzalecki, 2018;Webb, 2018). Most of these models are motivated by principles of optimality.…”

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confidence: 99%

A statistical test for the optimality of deliberative time allocation

Bhui

2019

Psychon Bull Rev

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Whenever we make a choice, we must also decide how much time to spend making it. Many theories of decision-making crucially assume that this deliberation perfectly balances the costs of time expenditure and the benefits of better decisions. However, might we "overthink" or "underthink" decisions? Here, I propose and implement a method to precisely determine whether people are optimally spending their time on deliberation, accounting for individual preferences. This test evaluates the consistency of underlying preferences for time when incentives change, which is a necessary condition for optimality. This enables a more comprehensive analysis of rationality in a variety of contexts. I demonstrate how the test can reveal departures from optimality using two motion discrimination experiments in which I vary task difficulty and monetary incentives.When we decide whether to leap left or right to return a tennis serve, we expend time evaluating the options. The purpose of this deliberation is to reduce uncertainty as to which option is better, and thereby improve decision quality. Accordingly, choice often involves an inverse relationship between speed and accuracy; we can make judgments that are fast but error-prone, or slow but high quality. Theories based on utility maximization predict that people optimally balance the costs of spending time with the benefits of better decisions. I propose a new test of this prediction that more closely targets the theoretical core of optimality compared to past attempts, and I conduct two experiments to show how suboptimality can be accordingly investigated in deliberative time allocation.A long-standing body of work in psychology involves measuring response time in order to improve inferences about cognitive processing and predictions about behav-

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“…Guiding much of the recent research on RT in economic choice is the literature on sequential sampling models (SSM), such as the drift-diffusion model (Clithero 2018;Dickhaut et al 2009;Fehr and Rangel 2011;Fudenberg et al 2018;Krajbich et al 2014;Webb 2018;Woodford 2014). These models conceptualize the decision process as noisy information gathering, akin to a Bayesian framework.…”

Section: Response Timesmentioning

confidence: 99%