The computations that support simple decision-making: A comparison between the diffusion and urgency-gating models

Evans, Nathan J.; Hawkins, Guy E.; Boehm, Udo; Wagenmakers, Eric‐Jan; Brown, Scott

doi:10.1038/s41598-017-16694-7

Cited by 47 publications

(74 citation statements)

References 41 publications

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“…Many of our findings would be obfuscated had we not analyzed each subject separately, and we showed that the models are exceedingly difficult to disentangle when there are few trials per subject. Our insights are also relevant to an ongoing debate about whether subjects' decisions are better explained by an urgency-gating model (Cisek et al, 2009;Thura et al, 2012;Carland et al, 2015a;Carland et al, 2015b), which posits little to no integration, or a driftdiffusion model (Winkel et al, 2014;Hawkins et al, 2015;Evans et al, 2017). A subject's strategy could lie somewhere between no integration and perfect integration or in a completely different space of models.…”

Section: Discussionmentioning

confidence: 77%

Differentiating between integration and non-integration strategies in perceptual decision making

Stine

Zylberberg

Ditterich

et al. 2020

Preprint

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ABSTRACTMany tasks used to study decision-making encourage subjects to integrate evidence over time. Such tasks are useful to understand how the brain operates on multiple samples of information over prolonged timescales, but only if subjects actually integrate evidence to form their decisions. We explored the behavioral observations that corroborate evidence-integration in a number of task-designs. Several commonly accepted signs of integration were also predicted by non-integration strategies. Furthermore, an integration model could fit data generated by non-integration models. We identified the features of non-integration models that allowed them to mimic integration and used these insights to design a motion discrimination task that disentangled the models. In human subjects performing the task, we falsified a non-integration strategy in each and confirmed prolonged integration in all but one subject. The findings illustrate the difficulty of identifying a decision-maker’s strategy and support solutions to achieve this goal.

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Section: Discussionmentioning

confidence: 77%

Differentiating between integration and non-integration strategies in perceptual decision making

Stine

Zylberberg

Ditterich

et al. 2020

Preprint

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“…Knowing how each random variate might influence subjects' judgments allowed us to test whether all IPIs in a series of IPIs contributed equally to the ultimate response. Such a test is important because theories of evidence accumulation in cognitive tasks typically assume that successive stimulus samples are given equal weight in the decision-making process (Evans et al, 2017).…”

Section: Modeling the Influence Of Individual Ipismentioning

confidence: 99%

“…Additionally, as explained later, we wanted to exploit random variation in the IPIs for a detailed analysis of how successive pulses were processed. Sequential sampling models of decision making vary with respect to their assumptions about evidence accumulation, some assuming a constant rate, and others emphasizing the role of later evidence, akin to a recency effect (Evans et al, 2017). Specifically, we asked whether all IPIs in a stochastic pulse sequence contributed equally to a response, as assumed by drift-diffusion models, or whether specific IPI positions carried additional weight.…”

Section: Introductionmentioning

confidence: 99%

Feeling the Beat (and Seeing It, Too): Vibrotactile, Visual, and Bimodal Rate Discrimination

2020

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Abstract Beats are among the basic units of perceptual experience. Produced by regular, intermittent stimulation, beats are most commonly associated with audition, but the experience of a beat can result from stimulation in other modalities as well. We studied the robustness of visual, vibrotactile, and bimodal signals as sources of beat perception. Subjects attempted to discriminate between pulse trains delivered at 3 Hz or at 6 Hz. To investigate signal robustness, we intentionally degraded signals on two-thirds of the trials using temporal-domain noise. On these trials, inter-pulse intervals (IPIs) were stochastic, perturbed independently from the nominal IPI by random samples from zero-mean Gaussian distributions with different variances. These perturbations produced directional changes in the IPIs, which either increased or decreased the likelihood of confusing the two pulse rates. In addition to affording an assay of signal robustness, this paradigm made it possible to gauge how subjects’ judgments were influenced by successive IPIs. Logistic regression revealed a strong primacy effect: subjects’ decisions were disproportionately influenced by a trial’s initial IPIs. Response times and parameter estimates from drift-diffusion modeling showed that information accumulates more rapidly with bimodal stimulation than with either unimodal stimulus alone. Analysis of error rates within each condition suggested consistently optimal decision making, even with increased IPI variability. Finally, beat information delivered by vibrotactile signals proved just as robust as information conveyed by visual signals, confirming vibrotactile stimulation’s potential as a communication channel.

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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

Self Cite

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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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The computations that support simple decision-making: A comparison between the diffusion and urgency-gating models

Cited by 47 publications

References 41 publications

Differentiating between integration and non-integration strategies in perceptual decision making

Differentiating between integration and non-integration strategies in perceptual decision making

Feeling the Beat (and Seeing It, Too): Vibrotactile, Visual, and Bimodal Rate Discrimination

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

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