People adopt optimal policies in simple decision-making, after practice and guidance

Evans, Nathan J.; Brown, Scott

doi:10.3758/s13423-016-1135-1

Cited by 76 publications

(142 citation statements)

References 26 publications

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“…However, it has not been established whether humans attempt to maximize their reward rate by adopting collapsing thresholds in such circumstances, and our Experiment 1 suggests that they do not. This result is consistent with previous work on reward rate optimality which has demonstrated that humans fail to adopt optimal decision-making policies in many ways (Evans & Brown, 2017;Starns & Ratcli↵, 2012, though see for a task-design explanation). Almost every participant (55 out of 57) in our Experiment 1 had a reward rate that was lower than the best possible reward rate under a single fixed threshold (see the supplementary materials for more details).…”

Section: Discussionsupporting

confidence: 91%

“…Task and procedure. Participants made decisions about apparent motion in random dot kinematograms (Roitman & Shadlen, 2002;Evans & Brown, 2017), which is a standard task in decision-making studies. Our stimuli used the white-noise algorithm (Pilly & Seitz, 2009) with 40 white dots on a black background.…”

Section: Methodsmentioning

confidence: 99%

“…The analyses reported in the supplementary materials are based on methods developed by several groups in last five years: (Evans & Brown, 2017;Evans, 2019b;Evans, Brown, Mewhort, & Heathcote, 2018;Hawkins, Forstmann, et al, 2015;. Data level :…”

Section: Methodsmentioning

confidence: 99%

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The role of passing time in decision-making.

Evans¹,

Hawkins²,

Brown

2020

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Theories of perceptual decision-making have been dominated by the idea that evidence accumulates in favor of di↵erent alternatives until some fixed threshold amount is reached, which triggers a decision. Recent theories have suggested that these thresholds may not be fixed during each decision, but change as time passes. These collapsing thresholds can improve performance in particular decision environments, but reviews of data from typical decision-making paradigms have failed to support collapsing thresholds. We designed three experiments to test collapsing threshold assumptions in decision environments specifically tailored to make them optimal. An emphasis on decision speed encouraged the adoption of collapsing thresholds-most strongly through the use of response deadlines, but also through instruction to a lesser extent-but setting an explicit goal of reward rate optimality through both instructions and task design did not.

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Section: Discussionsupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

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The role of passing time in decision-making.

Evans¹,

Hawkins²,

Brown

2020

Journal of Experimental Psychology: Learning, Memory, and Cogni

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“…The most commonly applied cognitive models for disentangling the effects of caution, processing speed, and motor speed are evidence accumulation models of simple decision‐making (Evans & Brown, ; Evans, Hawkins, Boehm, Wagenmakers, & Brown, ; Evans et al., ; Ratcliff, Thapar, & McKoon, ). These models posit that decision‐making is the result of evidence accumulating in favor of each response alternative until a threshold amount is reached for one of the alternatives, at which time a response is triggered.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Covariance Structure of Complex Datasets Using Cognitive Models: An Application to Individual Differences and the Heritability of Cognitive Ability

Evans¹,

Steyvers

Brown³

2018

Cognitive Science

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Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process-such as cognitive processing speed, response caution, and motor execution speed-in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other areas of cognition. Importantly, a limitation of previous work on cognitive heritability is the underlying assumption that variability in response times solely reflects variability in the speed of cognitive processing. This assumption has been problematic in other domains, due to the confounding effects of caution and motor execution speed on observed response times. We extend a cognitive model of decision-making to account for relatedness structure in a twin study paradigm. This approach can separately quantify different contributions to the heritability of response time. Using data from the Human Connectome Project, we find strong evidence for the heritability of response caution, and more ambiguous evidence for the heritability of cognitive processing speed and motor execution speed. Our study suggests that the assumption made in previous studies-that the heritability of cognitive ability is based on cognitive processing speed-may be incorrect. More generally, our methodology provides a useful avenue for future research in complex data that aims to analyze cognitive traits across different sources of related data, whether the relation is between people, tasks, experimental phases, or methods of measurement.

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“…For the main analyses, we excluded the first 21 blocks of trials from Experiment 1, which we based on previous studies (Evans & Brown, 2017;Evans, Bennett, & Brown, 2018). In those studies, participants took approximately 15 blocks to stabilize on a single strategy when given reward rate feedback.…”

Section: Stability Of Performance Over Blocksmentioning

confidence: 99%

Supplemental Material for The Role of Passing Time in Decision-Making

2019

Journal of Experimental Psychology: Learning, Memory, and Cogni

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In the main text we compared a "full" fixed thresholds diffusion model to a "simple" collapsing thresholds diffusion model -which we believe is both the most theoretically interesting and practical comparison for the reasons outlined in the main text. The "full" fixed thresholds model includes between-trial variability in drift rate, starting point, and non-decision time, while the "simple" collapsing thresholds model did not contain betweentrial variability in any parameters. However, in the cases where the collapsing thresholds model was found to be superior, our comparison leaves two potential explanations for why the collapsing thresholds model provided the better DIC value: either 1) the collapsing thresholds were able to capture certain aspects of the data, or 2) the best model was actually a fixed thresholds "simple" diffusion model, and the fixed thresholds model lost in the complexity-corrected DIC calculation because of the additional flexibility provided by the between-trial variability parameters that was not actually required to adequately account for the data.In order to address this potential limitation, we also tested a "simple" variant of the fixed thresholds diffusion model to the data of each experiment, in the same manner as the models compared in the main text. The comparison of all three models (collapsing thresholds, "full" fixed thresholds, "simple" fixed thresholds) on DIC weights for each experiment can be seen in Figure S1. For Experiment 2 (the deadline experiment), no participant had any appreciable weight in favor of the simple fixed thresholds diffusion model, meaning that its inclusion had no impact on the results. For Experiment 1 (reward rate emphasis), most participants had no weight in favor of the simple fixed thresholds diffusion model, and the minority of participants that had non-zero weight did not change the trends, again meaning that its inclusion again had very little impact on the results. However, for Experiment 3 (speed emphasis), a considerable number of participants showed PASSING TIME IN DECISION-MAKING

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People adopt optimal policies in simple decision-making, after practice and guidance

Cited by 76 publications

References 26 publications

The role of passing time in decision-making.

The role of passing time in decision-making.

Modeling the Covariance Structure of Complex Datasets Using Cognitive Models: An Application to Individual Differences and the Heritability of Cognitive Ability

Supplemental Material for The Role of Passing Time in Decision-Making

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