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.

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 Visuo-Perceptual and Decision-Making Contributions to Visual Hallucinations in Mild Cognitive Impairment in Lewy Body Disease: Insights from a Drift Diffusion Analysis

2020 ◽  
Vol 10 (8) ◽  
pp. 540
Author(s):  
Lauren Revie ◽  
Calum A Hamilton ◽  
Joanna Ciafone ◽  
Paul C Donaghy ◽  
Alan Thomas ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Reaction Time ◽  
Diffusion Model ◽  
Lewy Body ◽  
Drift Rate ◽  
Lewy Body Disease ◽  
Visual Hallucinations ◽  
Drift Diffusion Model ◽  
Drift Diffusion

Background: Visual hallucinations (VH) are a common symptom in dementia with Lewy bodies (DLB); however, their cognitive underpinnings remain unclear. Hallucinations have been related to cognitive slowing in DLB and may arise due to impaired sensory input, dysregulation in top-down influences over perception, or an imbalance between the two, resulting in false visual inferences. Methods: Here we employed a drift diffusion model yielding estimates of perceptual encoding time, decision threshold, and drift rate of evidence accumulation to (i) investigate the nature of DLB-related slowing of responses and (ii) their relationship to visuospatial performance and visual hallucinations. The EZ drift diffusion model was fitted to mean reaction time (RT), accuracy and RT variance from two-choice reaction time (CRT) tasks and data were compared between groups of mild cognitive impairment (MCI-LB) LB patients (n = 49) and healthy older adults (n = 25). Results: No difference was detected in drift rate between patients and controls, but MCI-LB patients showed slower non-decision times and boundary separation values than control participants. Furthermore, non-decision time was negatively correlated with visuospatial performance in MCI-LB, and score on visual hallucinations inventory. However, only boundary separation was related to clinical incidence of visual hallucinations. Conclusions: These results suggest that a primary impairment in perceptual encoding may contribute to the visuospatial performance, however a more cautious response strategy may be related to visual hallucinations in Lewy body disease. Interestingly, MCI-LB patients showed no impairment in information processing ability, suggesting that, when perceptual encoding was successful, patients were able to normally process information, potentially explaining the variability of hallucination incidence.

scholarly journals At the Zebra Crossing: Modelling Complex Decision Processes with Variable-Drift Diffusion Models

2019 ◽  
Author(s):  
Oscar Terence Giles ◽  
Gustav Markkula ◽  
Jami Pekkanen ◽  
Naoki Yokota ◽  
Naoto Matsunaga ◽  
...  
Keyword(s):  
Diffusion Model ◽  
Drift Rate ◽  
Sensory Information ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Multimodal Distributions ◽  
Complex Models ◽  
Complex Decision ◽  
Pedestrian Crossing ◽  
Decision Tasks

Drift diffusion (or evidence accumulation) models have found widespread use in the modelling of simple decision tasks. Extensions of these models, in which the model’s instantaneous drift rate is not fixed but instead allowed to vary over time as a function of a stream of perceptual inputs, have allowed these models to account for more complex sensorimotor decision tasks. However, many real-world tasks seemingly rely on a myriad of even more complex underlying processes. One interesting example is the task of deciding whether to cross a road with an approaching vehicle. This action decision seemingly depends on sensory information both about own affordances (whether one can make it across before the vehicle) and action intention of others (whether the vehicle is yielding to oneself). Here, we compared three extensions of a standard drift diffusion model, with regards to their ability to capture timing of pedestrian crossing decisions in a virtual reality environment. We find that a single variable-drift diffusion model (S-VDDM) in which the varying drift rate is determined by visual quantities describing vehicle approach and deceleration, saturated at an upper and lower bound, can explain multimodal distributions of crossing times well across a broad range vehicle approach scenarios. More complex models, which attempt to partition the final crossing decision into constituent perceptual decisions, improve the fit to the human data but further work is needed before firm conclusions can be drawn from this finding.

scholarly journals On the recovery of the doping profile in an time-dependent drift-diffusion model

2014 ◽  
Vol 22 (2) ◽  
pp. 253-274
Author(s):  
Bin Wu ◽  
Zewen Wang
Keyword(s):  
Inverse Problem ◽  
Diffusion Model ◽  
Electric Potential ◽  
Parabolic Equations ◽  
Time Dependent ◽  
Doping Profile ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
System Of Parabolic Equations ◽  
Theoretical Results

AbstractWe consider an inverse problem arising from an time-dependent drift-diffusion model in semiconductor devices, which is formulated in terms of a system of parabolic equations for the electron and hole densities and the Poisson equation for the electric potential. This inverse problem aims to identify the doping profile from the final overdetermination data of the electric potential. By using the Schauder’s fixed point theorem in suitable Sobolev space, the existence of this inverse problem are obtained. Moreover by means of Gronwall inequality, we prove the uniqueness of this inverse problem for small measurement time. For this nonlinear inverse problem, our theoretical results guarantee the solvability for the proposed physical model.

scholarly journals Face masks influence how facial expressions are perceived: A drift-diffusion model of emotion judgments

2021 ◽  
Author(s):  
W. Craig Williams ◽  
Eisha Haque ◽  
Becky Mai ◽  
Vinod Venkatraman
Keyword(s):  
Diffusion Model ◽  
Facial Expressions ◽  
Drift Rate ◽  
Diffusion Modeling ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Lower Face ◽  
Upper Face

Face masks slow the spread of SARS-CoV-2, but it has been unknown whether masks influence how individuals communicate emotion through facial expressions. Masks could influence how accurately—or how quickly—individuals perceive expressions, and how rapidly they accumulate evidence for emotion. Over two independent pre-registered studies, conducted three and six months into the COVID-19 pandemic, participants judged expressions of 6 emotions (anger, disgust, fear, happiness, sadness, surprise) with the lower or upper face “masked” or unmasked. Participants in Study 1 (N = 228) identified expressions above chance with lower face masks. However, they were less likely—and slower—to correctly identify these expressions versus without masks, and they accumulated evidence for emotion more slowly—via decreased drift rate in drift-diffusion modeling. This pattern replicated and intensified three months later in Study 2 (N = 264). These data could inform interventions to promote mask wearing by addressing concerns with emotion communication.

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