The counterintuitive impact of responses and response times on parameter estimates in the drift diffusion model

2019 ◽  
Vol 73 (2) ◽  
pp. 289-315
Author(s):  
Pascal Jordan
Keyword(s):  
Diffusion Model ◽  
Response Times ◽  
Parameter Estimates ◽  
Drift Diffusion Model ◽  
Drift Diffusion

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.

scholarly journals Bayesian confidence for drift diffusion observers in dynamic stimuli tasks

2020 ◽  
Author(s):  
Joshua Calder-Travis ◽  
Rafal Bogacz ◽  
Nick Yeung
Keyword(s):  
Diffusion Model ◽  
Drift Rate ◽  
Response Times ◽  
Time Dependent ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Dynamic Stimuli ◽  
Computationally Expensive ◽  
Insight Into ◽  
Confidence Data

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.

THE DUAL-PROCESS DRIFT DIFFUSION MODEL: EVIDENCE FROM RESPONSE TIMES

2015 ◽  
Vol 54 (2) ◽  
pp. 1274-1282 ◽  
Author(s):  
Andrew Caplin ◽  
Daniel Martin
Keyword(s):  
Diffusion Model ◽  
Response Times ◽  
Dual Process ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Model Evidence

scholarly journals Gamification of Two Alternative Forced Choice: Validation based on Drift Diffusion Model and Individual Differences

2019 ◽  
Author(s):  
Esin Turkakin ◽  
Ceyla Karamancı ◽  
Kaan Karamancı ◽  
Fuat Balcı
Keyword(s):  
Individual Differences ◽  
Diffusion Model ◽  
Response Times ◽  
Forced Choice ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Obsessive Compulsive ◽  
Constant Variance ◽  
Decision Making Processes ◽  
Evidence Integration

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.

scholarly journals Benefits of Neuroeconomic Modeling: New Policy Interventions and Predictors of Preference

2014 ◽  
Vol 104 (5) ◽  
pp. 501-506 ◽  
Author(s):  
Ian Krajbich ◽  
Bastiaan Oud ◽  
Ernst Fehr
Keyword(s):  
Diffusion Model ◽  
Choice Behavior ◽  
Response Times ◽  
Time Limit ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Policy Interventions ◽  
Indifference Points

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.

Perceptual robustness of stochastic visual, vibrotactile, and bimodal stimuli

2018 ◽  
Author(s):  
Mercedes B. Villalonga ◽  
Rachel F. Sussman ◽  
Robert Sekuler
Keyword(s):  
Logistic Regression ◽  
Response Times ◽  
Visual Signals ◽  
Optimal Decision ◽  
Parameter Estimates ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Bimodal Stimulation ◽  
Random Samples ◽  
Rate Information

In a study of perceptual robustness, 23 subjects discriminated between two rates (3 and 6 Hz) at which brief visual, vibrotactile, or concurrent visual and vibrotactile pulses were presented. On two-thirds of the trials, inter-pulse intervals (IPIs) were stochastic, perturbed by random samples from a zero-mean Gaussian distribution. Directional changes in IPIs increased or diminished the likelihood of confusing the pulse rates, making it possible to gauge the influence of successive IPIs on subjects’ judgments. Logistic regression revealed a strong primacy effect: subjects’ decisions were disproportionately influenced by a trial’s initial IPIs. Both response times and drift-diffusion model parameter estimates indicated that information accumulates more rapidly with bimodal stimulation than with either unimodal stimulus. Error analysis suggested consistent reliance on statistically optimal decision criteria. Finally, rate information delivered by vibrotactile signals proved just as robust as information conveyed by visual signals, confirming vibrotactile stimulation’s potential for timely communication.

scholarly journals The critical role of interference control in metaphor comprehension evidenced by the drift–diffusion model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hee-Dong Yoon ◽  
Minho Shin ◽  
Hyeon-Ae Jeon
Keyword(s):  
Executive Functions ◽  
Diffusion Model ◽  
Word Association ◽  
Response Times ◽  
Critical Role ◽  
Interference Control ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Metaphor Comprehension ◽  
Fine Grained

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.

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
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