Neural Substrates of the Drift-Diffusion Model in Brain Disorders

Gupta, Ankur; Bansal, Rohini; Alashwal, Hany; Kaçar, Anıl Şafak; Balcı, Fuat; Moustafa, Ahmed A.

doi:10.3389/fncom.2021.678232

Cited by 22 publications

(14 citation statements)

References 169 publications

(228 reference statements)

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“…CBGT circuits can control decision parameters under uncertainty. Both theoretical (9,12,14,(19)(20)(21) and experimental (18) evidence suggest that the CBGT circuits play a critical role in the evidence accumulation process (for a review see 36 (22)). The canonical CBGT circuit (Fig.…”

Section: Resultsmentioning

confidence: 99%

Competing neural representations of choice shape evidence accumulation in humans

Bond

Rasero

Madan

et al. 2022

Preprint

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The ability to change your mind when the local environment changes relies critically on cortico-basal ganglia-thalamic (CBGT) circuits. In silico experiments on the CBGT pathways show how shifts in decision policy are driven by learning-induced changes in competition between action plans, both within and across action representations. We empirically validate this idea, using whole-brain hemodynamic imaging in homo sapiens to show how competition between action representations in CBGT circuits adaptively shifts the rate of evidence accumulation in response to action-outcome contingency changes.

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Section: Resultsmentioning

confidence: 99%

Competing neural representations of choice shape evidence accumulation in humans

Bond

Rasero

Madan

et al. 2022

Preprint

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“…An advantage of applying the DDM was that many studies have examined the correspondence between DDM’s each parameter and neural substrate. It has been reported that the prefrontal cortex, frontal eye field, lateral intraparietal area, and medial prefrontal cortex effected drift rate parameter, and the subthalamic nucleus and pre-supplementary motor area effected decision threshold [ 44 – 47 ]. Recently, noninvasive brain stimulation on the dorsolateral prefrontal cortex has been reported to be effective as a smoking cessation treatment [ 48 ], and this stimulation site coincides with an area that is related to drift rate.…”

Section: Discussionmentioning

confidence: 99%

Explicit and implicit attitudes toward smoking: Dissociation of attitudes and different characteristics for an implicit attitude in smokers and nonsmokers

Gao

Sawamura²,

Saito³

et al. 2022

PLoS ONE

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Smoking is a global health risk for premature death and disease. Recently, addictive behaviors, like smoking, were considered to be guided by explicit and implicit processes. The existence of a dissociation between the two attitudes in nonsmokers and the causes of the differences in implicit attitudes toward smoking have not been fully investigated. We investigated the explicit and implicit attitudes toward smoking via a self-reported scale and the single category implicit association test (SC-IAT), respectively, among undergraduate and graduate health sciences students. In addition, we applied the drift-diffusion model (DDM) on the SC-IAT and examined the behavioral characteristics that caused differences in implicit attitude toward smoking between smokers and nonsmokers. The results showed the existence of a dissociation between explicit and implicit attitudes toward smoking among nonsmokers. In addition, nonsmokers had a higher decision threshold than smokers and a higher drift rate in the condition where negative words were associated with smoking. Nonsmokers engaged in SC-IAT with more cautious attitudes and responded more easily in a negative condition since it was consistent with their true attitudes. Conversely, smokers did not show a significant difference in the drift rate between the conditions. These results suggested that the differences in an implicit attitude between smokers and nonsmokers were caused by differences in evidence accumulation speed between the positive and negative conditions. The existence of dissociation between implicit and explicit attitudes toward smoking may indicate the difficulty of measuring true attitude in nonsmokers in a questionnaire survey. Additionally, the DDM results explained the difference of implicit attitude between smokers and nonsmokers; it may provide information on the mechanisms of addictive behaviors and a basis for therapy. However, whether these results are affected by cultural differences requires further investigation.

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“…Although first described over 50 years ago, the DDM has recently enjoyed widespread use, partly due to the development of powerful and freely-available software implementing computationally-intensive model-fitting algorithms, and partly due to an accumulating literature documenting that the DDM can indeed shed light on latent cognitive processes that are not necessarily evident from traditional hypothesis-driven methods of behavioral data analysis (Deghan et al, 2022), and that have been linked to specific brain regions (Mulder et al, 2012;Mueller et al, 2017;Weigard and Sripada, 2021;Gupta et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Importantly, the current article is not meant to offer a comprehensive review of the DDM literature (for good reviews, see, e.g., Forstmann et al, 2016;Ratcliff et al, 2016;Evans and Wagenmakers, 2020;Gupta et al, 2022), nor a general tutorial on good computational modeling practices (see, e.g., Daw, 2011;Heathcote et al, 2015;Wilson and Collins, 2019); however, it may provide a useful springboard for cognitive psychologists and neuroscientists considering the use of the drift diffusion model, and related computational models, in their own research.…”

Section: Introductionmentioning

confidence: 99%

A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences

Myers

Interian²,

Moustafa³

2022

Front. Psychol.

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Recent years have seen a rapid increase in the number of studies using evidence-accumulation models (such as the drift diffusion model, DDM) in the fields of psychology and neuroscience. These models go beyond observed behavior to extract descriptions of latent cognitive processes that have been linked to different brain substrates. Accordingly, it is important for psychology and neuroscience researchers to be able to understand published findings based on these models. However, many articles using (and explaining) these models assume that the reader already has a fairly deep understanding of (and interest in) the computational and mathematical underpinnings, which may limit many readers’ ability to understand the results and appreciate the implications. The goal of this article is therefore to provide a practical introduction to the DDM and its application to behavioral data – without requiring a deep background in mathematics or computational modeling. The article discusses the basic ideas underpinning the DDM, and explains the way that DDM results are normally presented and evaluated. It also provides a step-by-step example of how the DDM is implemented and used on an example dataset, and discusses methods for model validation and for presenting (and evaluating) model results. Supplementary material provides R code for all examples, along with the sample dataset described in the text, to allow interested readers to replicate the examples themselves. The article is primarily targeted at psychologists, neuroscientists, and health professionals with a background in experimental cognitive psychology and/or cognitive neuroscience, who are interested in understanding how DDMs are used in the literature, as well as some who may to go on to apply these approaches in their own work.

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Neural Substrates of the Drift-Diffusion Model in Brain Disorders

Cited by 22 publications

References 169 publications

Competing neural representations of choice shape evidence accumulation in humans

Competing neural representations of choice shape evidence accumulation in humans

Explicit and implicit attitudes toward smoking: Dissociation of attitudes and different characteristics for an implicit attitude in smokers and nonsmokers

A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences

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