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 ◽  
...  

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.

2019 ◽  
Author(s):  
Esin Turkakin ◽  
Ceyla Karamancı ◽  
Kaan Karamancı ◽  
Fuat Balcı

Two alternative forced choice (2AFC) paradigms, coupled with the unified analysis of accuracy and response times within specific decision theoretic frameworks, have provided a wealth of information regarding decision-making processes. One problem of associated experimental tasks is that they are typically not engaging and do not contain stimuli or task representations familiar to participants, resulting in contaminants in the data due to boredom and distraction. Furthermore, when investigating decision strategies, use of noisy stimulus attributes result in undesired variance in the perceptual process complicating the analysis and interpretation of results. To address these fundamental issues, we developed a 2AFC soccer game in which participants’ task is to score goals by making leftward or rightward shots after observing the trajectory of the goalkeeper within a trial. The goalkeeper’s location is repeatedly sampled from a normal distribution with a constant variance and a mean either to the left or right of the midpoint. We tested participants on three difficulty levels parameterized by the distance between the two means and expected the rate of evidence integration to decrease with increasing difficulty and after errors as characteristic of standard 2AFC tasks. Drift- diffusion model provided good fits to data, and their outputs confirmed our predictions outlined above. Furthermore, we found the evidence integration rates to be negatively correlated with individual differences in maladaptive perfectionism, but not in anxiety or obsessive-compulsive traits.


Author(s):  
Joshua Calder-Travis ◽  
Rafal Bogacz ◽  
Nick Yeung

AbstractMuch work has explored the possibility that the drift diffusion model, a model of response times and choices, could be extended to account for confidence reports. Many methods for making predictions from such models exist, although these methods either assume that stimuli are static over the course of a trial, or are computationally expensive, making it difficult to capitalise on trial-by-trial variability in dynamic stimuli. Using the framework of the drift diffusion model with time-dependent thresholds, and the idea of a Bayesian confidence readout, we derive expressions for the probability distribution over confidence reports. In line with current models of confidence, the derivations allow for the accumulation of “pipeline” evidence which has been received but not processed by the time of response, the effect of drift rate variability, and metacognitive noise. The expressions are valid for stimuli which change over the course of a trial with normally distributed fluctuations in the evidence they provide. A number of approximations are made to arrive at the final expressions, and we test all approximations via simulation. The derived expressions only contain a small number of standard functions, and only require evaluating once per trial, making trial-by-trial modelling of confidence data in dynamic stimuli tasks more feasible. We conclude by using the expressions to gain insight into the confidence of optimal observers, and empirically observed patterns.


2017 ◽  
Vol 31 (2) ◽  
pp. 173-180 ◽  
Author(s):  
Angelo Pirrone ◽  
Abigail Dickinson ◽  
Rosanna Gomez ◽  
Tom Stafford ◽  
Elizabeth Milne

2014 ◽  
Vol 104 (5) ◽  
pp. 501-506 ◽  
Author(s):  
Ian Krajbich ◽  
Bastiaan Oud ◽  
Ernst Fehr

Neuroeconomics strives to use knowledge from neuroscience to improve models of decisionmaking. Here we introduce a biologically plausible, drift-diffusion model that is able to jointly predict choice behavior and response times across different choice environments. The model has both normative and positive implications for economics. First, we consistently observe that decisionmakers inefficiently allocate their time to choices for which they are close to indifference. We demonstrate that we can improve subjects' welfare using a simple intervention that puts a time limit on their choices. Second, response times can be used to predict indifference points and the strength of preferences.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hee-Dong Yoon ◽  
Minho Shin ◽  
Hyeon-Ae Jeon

AbstractWe address the question of, among several executive functions, which one has a strong influence on metaphor comprehension. To this end, participants took part in a metaphor comprehension task where metaphors had varying levels of familiarity (familiar vs. novel metaphors) with different conditions of context (supporting vs. opposing contexts). We scrutinized each participant’s detailed executive functions using seven neuropsychological tests. More interestingly, we modelled their responses in metaphor comprehension using the drift–diffusion model, in an attempt to provide more systematic accounts of the processes underlying metaphor comprehension. Results showed that there were significant negative correlations between response times in metaphor comprehension and scores of the Controlled Oral Word Association Test (COWAT)-Semantic, suggesting that better performances in comprehending metaphors were strongly associated with better interference control. Using the drift–diffusion model, we found that the familiarity, compared to context, had greater leverage in the decision process for metaphor comprehension. Moreover, individuals with better performance in the COWAT-Semantic test demonstrated higher drift rates. In conclusion, with more fine-grained analysis of the decisions involved in metaphor comprehension using the drift–diffusion model, we argue that interference control plays an important role in processing metaphors.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kenneth S. Kendler ◽  
Henrik Ohlsson ◽  
Jan Sundquist ◽  
Kristina Sundquist

AbstractTo clarify the structure of genetic risks for 11 major psychiatric disorders, we calculated, from morbidity risks for disorders in 1st–5th degree relatives controlling for cohabitation effects, in the Swedish population born between 1932 and 1995 (n = 5,830,014), the family genetic risk scores (FGRS) for major depression (MD), anxiety disorders (AD), obsessive-compulsive disorder (OCD), bipolar disorder (BD), schizophrenia (SZ), bulimia (BUL), anorexia nervosa (AN), alcohol use disorder (AUD), drug use disorder (DUD), ADHD, and autism-spectrum disorder (ASD). For all affected individuals, we calculated their mean standardized FGRS for each disorder. The patterns of FGRS were quite similar for MD and AD, and for AUD and DUD, but substantially less similar for BUL and AN, BD and SZ, and ADHD and ASD. While OCD had high levels of FGRS for MD and AD, the overall FGRS profile differed considerably from MD and AD. ADHD FGRS scores were substantially elevated in AUD and DUD. FGRS scores for BD, OCD, AN, ASD, ADHD, and especially SZ were relatively disorder-specific while genetic risk for MD and AD had more generalized effects. The levels of FGRS for BMI, coronary artery disease, and educational attainment across our disorders replicated prior associations found using molecular genetic methods. All diagnostic categories examined had elevated FGRS for many disorders producing, for each condition, an informative FGRS profile. Using a novel method which approximates, from pedigree data, aggregate genetic risk, we have replicated and extended prior insights into the structure of genetic risk factors for key psychiatric illnesses.


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