Testing the drift-diffusion model

Fudenberg, Drew; Newey, Whitney K.; Strack, Philipp; Strzalecki, Tomasz

doi:10.1073/pnas.2011446117

Cited by 39 publications

(16 citation statements)

References 46 publications

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“…Our empirical results are also consistent with the predictions of recent work on driftdiffusion models that has considered settings in which the relative evaluations of decision alternatives are uncertain ex-ante and that has shown that the positive association between decision speed and decision quality dominates in such settings (see Fudenberg et al, 2018Fudenberg et al, , 2020. So far, drift-diffusion models have been mainly used to model decisions in simple decision problems and existing evidence is restricted to lab experiments with non-complex decisions (see, e.g., Ratcliff and Rouder, 1998;Krajbich and Rangel, 2011;Ratcliff et al, 2015;Bhui, 2019;Schotter and Trevino, 2021;Huseynov and Palma, 2021).…”

Section: Introductionsupporting

confidence: 90%

“…Hence, the larger the perceived difference in evaluations the more informative is the signal (the larger is the drift towards the boundaries). It has been shown that the decision boundaries vary with decision time in potentially non-monotone ways, but the decision quality decreases with decision time when aggregating many decisions of a decision maker who on average has correct priors about the difference in the evaluation of the respective choice alternatives (see Fudenberg et al, 2018Fudenberg et al, , 2020. In this setting, the optimal choice can be characterized as the maximization of the posterior belief about the best possible move, subject to the cost associated with cogitation per instant of time.…”

Section: Discussionmentioning

confidence: 99%

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Speed, Quality, and the Optimal Timing of Complex Decisions: Field Evidence

Sunde¹,

Zēgners²,

Strittmatter³

2022

Preprint

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This paper presents an empirical investigation of the relation between decision speed and decision quality for a real-world setting of cognitivelydemanding decisions in which the timing of decisions is endogenous: professional chess. Move-by-move data provide exceptionally detailed and precise information about decision times and decision quality, based on a comparison of actual decisions to a computational benchmark of best moves constructed using the artificial intelligence of a chess engine. The results reveal that faster decisions are associated with better performance. The findings are consistent with the predictions of procedural decision models like drift-diffusion-models in which decision makers sequentially acquire information about decision alternatives with uncertain valuations.

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Section: Introductionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Speed, Quality, and the Optimal Timing of Complex Decisions: Field Evidence

Sunde¹,

Zēgners²,

Strittmatter³

2022

Preprint

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“…In evidence accumulation models, the distribution of RTs of a decision process (e.g., an ongoing task response) is modeled by several parameters (Ratcliff & McKoon, 2008), of which drift rate, response threshold, and non-decision time are the most important in the majority of studies. Previous research has used changes in these parameters under different conditions to infer cognitive processes (e.g., Fudenberg et al, 2020;Johnson et al, 2017;Pedersen et al, 2017;Ratcliff et al, 2016). In studies using evidence accumulation models, drift rate is usually associated with information processing speed, while response threshold is related to the response criterion, in other words, how much information needs to be gathered before a decision is made.…”

Section: Costs To the Ongoing Taskmentioning

confidence: 99%

Dissociating sub-processes of aftereffects of completed intentions and costs to the ongoing task in prospective memory: A mouse-tracking approach

et al. 2022

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In the present study, we used mouse tracking to investigate two processes underlying prospective memory (PM) retrieval: First, we aimed to explore to what extent spontaneous retrieval of already completed PM intentions is supported by reflexive-associative and discrepancy-plus-search processes. Second, we aimed to disentangle whether costs to an ongoing task during the pursuit of a PM intention are associated with presumably resource-demanding monitoring processes or with a presumably resource-sparing strategic delay of ongoing-task responses. Our third aim was to explore the interaction of processes underlying costs to the ongoing task and processes of spontaneous retrieval. Our analyses replicated response-time patterns from previous studies indicating aftereffects of completed intentions and costs to ongoing-task performance, as well as increased aftereffects while pursuing a PM intention. Notably, based on our mouse-tracking analyses, we argue that aftereffects of completed intentions are best explained by a reflexive initiation of an already completed intention. If the completed intention is not performed in its entirety (i.e., no commission error), the reflexive initiation of the completed intention is followed by a subsequent movement correction that most likely represents a time-consuming response-verification process. Regarding performance costs in the ongoing task, our analyses suggest that actively pursuing a PM intention most likely leads to a strategic delay of ongoing activities. Lastly, we found that pursuing a novel PM task after intention completion exacerbated orienting responses to all deviant stimuli, exacerbated the readiness to initiate the completed intention reflexively, and substantially prolonged the response-verification process following this reflexive intention retrieval.

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“…Decision making is a central cognitive function for animals, and the ability to integrate information in a swift and accurate manner is of great importance. The decision behavior is typically measured by speed and accuracy of the subject, and several computational or mathematical models have been proposed to account for these two quantities (Wang, 2012(Wang, , 2008Rao, 2010;Beck et al, 2008;Machens et al, 2005;Lo and Wang, 2006;Bogacz et al, 2006;Fudenberg et al, 2020). In addition to the psychometric measures, the neurological basis of decision making has always been a highly sought-out topic of interest.…”

Section: Introductionmentioning

confidence: 99%

Choose carefully, act quickly: Efficient decision making with selective inhibition in attractor neural networks

Liu

2021

Preprint

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The ability to decide swiftly and accurately in an urgent scenario is crucial for an organism's survival. The neural mechanisms underlying the perceptual decision and trade-off between speed and accuracy have been extensively studied in the past few decades. Among several theoretical models, the attractor neural network model has successfully captured both behavioral and neuronal data observed in many decision experiments. However, a recent experimental study revealed additional details that were not considered in the original attractor model. In particular, the study shows that the inhibitory neurons in the posterior parietal cortex of mice are as selective to decision making results as the excitatory neurons, whereas the original attractor model assumes the inhibitory neurons to be unselective. In this study, we investigate a more general attractor model with selective inhibition, and analyze in detail how the computational ability of the network changes with selectivity. We proposed a reduced model for the selective model, and showed that selectivity adds a time-varying component to the energy landscape. This time dependence of the energy landscape allows the selective model to integrate information carefully in initial stages, then quickly converge to an attractor once the choice is clear. This results in the selective model having a more efficient speed-accuracy trade-off that is unreachable by unselective models.

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Testing the drift-diffusion model

Cited by 39 publications

References 46 publications

Speed, Quality, and the Optimal Timing of Complex Decisions: Field Evidence

Speed, Quality, and the Optimal Timing of Complex Decisions: Field Evidence

Dissociating sub-processes of aftereffects of completed intentions and costs to the ongoing task in prospective memory: A mouse-tracking approach

Choose carefully, act quickly: Efficient decision making with selective inhibition in attractor neural networks

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