scholarly journals The drift diffusion model as the choice rule in inter-temporal and risky choice: A case study in medial orbitofrontal cortex lesion patients and controls

2020 ◽  
Vol 16 (4) ◽  
pp. e1007615
Author(s):  
Jan Peters ◽  
Mark D’Esposito
Keyword(s):  
Diffusion Model ◽  
Orbitofrontal Cortex ◽  
Risky Choice ◽  
Choice Rule ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Cortex Lesion

scholarly journals The drift diffusion model as the choice rule in inter-temporal and risky choice: a case study in medial orbitofrontal cortex lesion patients and controls

2019 ◽  
Author(s):  
Jan Peters ◽  
Mark D’Esposito
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Choice Model ◽  
Sequential Sampling ◽  
Temporal Discounting ◽  
Risky Choice ◽  
Choice Rule ◽  
Model Parameters ◽  
Drift Diffusion Model ◽  
Drift Diffusion

AbstractSequential sampling models such as the drift diffusion model have a long tradition in research on perceptual decision-making, but mounting evidence suggests that these models can account for response time distributions that arise during reinforcement learning and value-based decision-making. Building on this previous work, we implemented the drift diffusion model as the choice rule in inter-temporal choice (temporal discounting) and risky choice (probability discounting) using a hierarchical Bayesian estimation scheme. We validated our approach in data from nine patients with focal lesions to the ventromedial prefrontal cortex / medial orbitofrontal cortex (vmPFC/mOFC) and nineteen age- and education-matched controls. Choice model parameters estimated via standard softmax action selection were reliably reproduced using the drift diffusion model as the choice rule, both for temporal discounting and risky choice. Model comparison revealed that, for both tasks, the data were best accounted for by a variant of the drift diffusion model including a non-linear mapping from value-differences to trial-wise drift rates. Posterior predictive checks of the winning models revealed a reasonably good fit to individual participants reaction time distributions. We then applied this modeling framework and 1) reproduced our previous results regarding temporal discounting in vmPFC/mOFC patients and 2) showed in a previously unpublished data set on risky choice that vmPFC/mOFC patients exhibit increased risk-taking relative to controls. Analyses of diffusion model parameters revealed that vmPFC/mOFC damage abolished neither value sensitivity nor asymptote of the drift rate. Rather, it substantially increased non-decision times and reduced response caution during risky choice. Our results highlight that novel insights can be gained from applying sequential sampling models in studies of inter-temporal and risky decision-making in cognitive neuroscience.

scholarly journals The drift diffusion model as the choice rule in reinforcement learning

2016 ◽  
Vol 24 (4) ◽  
pp. 1234-1251 ◽  
Author(s):  
Mads Lund Pedersen ◽  
Michael J. Frank ◽  
Guido Biele
Keyword(s):  
Reinforcement Learning ◽  
Diffusion Model ◽  
Choice Rule ◽  
Drift Diffusion Model ◽  
Drift Diffusion

scholarly journals Dopamine and risky decision-making in pathological and problem gamblers

2019 ◽  
Author(s):  
Jan Peters ◽  
Taylor Vega ◽  
Dawn Weinstein ◽  
Jennifer Mitchell ◽  
Andrew Kayser
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Gambling Disorder ◽  
Risky Choice ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Problem Gamblers ◽  
Cohen’S D ◽  
Cohen's D

AbstractGambling disorder is a behavioral addiction that is associated with impairments in value-based decision-making such as increased temporal discounting and reduced risk-aversion. Dopamine regulates learning and decision-making by modulating information processing throughout fronto-striatal circuits. Although the role of alterations in dopamine neurotransmission in the etiology of gambling disorder is controversial, preliminary evidence suggests that specifically increasing frontal dopamine levels might improve cognitive functioning in pathological and problem gamblers. We therefore examined whether increasing frontal dopamine levels via the catechol-O-methyltransferase (COMT) inhibitor tolcapone would reduce risky choice in a group of pathological and problem gamblers (n=14) in a repeated-measures counter-balanced placebo-controlled double-blind study. Choice data were fit using hierarchical Bayesian parameter estimation and a modeling scheme that combined a risky choice model with the drift diffusion model to account for both choices and response time distributions. Model comparison revealed that the data were best accounted for by a variant of the drift diffusion model with a non-linear modulation of trial-wise drift rates by value differences, confirming recent findings. Contrary to our hypothesis, risk-taking was slightly increased under tolcapone vs. placebo (Cohen’s d = −.281). Examination of drug effects on diffusion model parameters revealed an increase in the value-dependency of the drift rate (Cohen’s d = .932) with a simultaneous reduction in the maximum drift rate (Cohen’s d = −1.84). These results add to previous work on the potential role of COMT inhibitors in behavioral addictions, and show no consistent beneficial effect of tolcapone on risky choice in gambling disorder. Modeling results add to mounting evidence for the applicability of diffusion models in value-based decision-making. Future work should focus on individual genetic, clinical and cognitive factors that might account for the heterogeneity in the effects of COMT inhibition.

scholarly journals Informing cognitive abstractions through neuroimaging: The neural drift diffusion model.

2015 ◽  
Vol 122 (2) ◽  
pp. 312-336 ◽  
Author(s):  
Brandon M. Turner ◽  
Leendert van Maanen ◽  
Birte U. Forstmann
Keyword(s):  
Diffusion Model ◽  
Drift Diffusion Model ◽  
Drift Diffusion

Incorporating photon recycling into the analytical drift-diffusion model of high efficiency solar cells

2014 ◽  
Vol 116 (19) ◽  
pp. 194504 ◽  
Author(s):  
Matthew P. Lumb ◽  
Myles A. Steiner ◽  
John F. Geisz ◽  
Robert J. Walters
Keyword(s):  
Solar Cells ◽  
Diffusion Model ◽  
High Efficiency ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
High Efficiency Solar Cells ◽  
Photon Recycling

scholarly journals Neural Substrates of the Drift-Diffusion Model in Brain Disorders

2022 ◽  
Vol 15 ◽  
Author(s):  
Ankur Gupta ◽  
Rohini Bansal ◽  
Hany Alashwal ◽  
Anil Safak Kacar ◽  
Fuat Balci ◽  
...  
Keyword(s):  
Diffusion Model ◽  
Response Times ◽  
Autism Spectrum ◽  
Brain Disorders ◽  
Functional Impairments ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Obsessive Compulsive ◽  
Theoretical Understanding ◽  
Compulsive Disorder

Many studies on the drift-diffusion model (DDM) explain decision-making based on a unified analysis of both accuracy and response times. This review provides an in-depth account of the recent advances in DDM research which ground different DDM parameters on several brain areas, including the cortex and basal ganglia. Furthermore, we discuss the changes in DDM parameters due to structural and functional impairments in several clinical disorders, including Parkinson's disease, Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorders, Obsessive-Compulsive Disorder (OCD), and schizophrenia. This review thus uses DDM to provide a theoretical understanding of different brain disorders.

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