Drift-Diffusion Model Parameters Underlying Cognitive Mechanism and Perceptual Learning in Autism Spectrum Disorder

2020 ◽  
pp. 847-857
Author(s):  
Tanu ◽  
Deepti Kakkar
Keyword(s):  
Autism Spectrum Disorder ◽  
Perceptual Learning ◽  
Diffusion Model ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Model Parameters ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Cognitive Mechanism

scholarly journals Understanding perceptual judgment in autism spectrum disorder using the drift diffusion model.

2017 ◽  
Vol 31 (2) ◽  
pp. 173-180 ◽  
Author(s):  
Angelo Pirrone ◽  
Abigail Dickinson ◽  
Rosanna Gomez ◽  
Tom Stafford ◽  
Elizabeth Milne
Keyword(s):  
Autism Spectrum Disorder ◽  
Diffusion Model ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Perceptual Judgment ◽  
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 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.

Identifying Feigned Cognitive Impairment: Investigating the Utility of Diffusion Model Analyses

Assessment ◽  
2020 ◽  
pp. 107319112096231
Author(s):  
Elad Omer ◽  
Tomer Elbaum ◽  
Yoram Braw
Keyword(s):  
Cognitive Impairment ◽  
Diffusion Model ◽  
Cognitive Processes ◽  
Forced Choice ◽  
Model Parameters ◽  
Performance Validity ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Choice Performance ◽  
Feigned Cognitive Impairment

Forced-choice performance validity tests are routinely used for the detection of feigned cognitive impairment. The drift diffusion model deconstructs performance into distinct cognitive processes using accuracy and response time measures. It thereby offers a unique approach for gaining insight into examinees’ speed-accuracy trade-offs and the cognitive processes that underlie their performance. The current study is the first to perform such analyses using a well-established forced-choice performance validity test. To achieve this aim, archival data of healthy participants, either simulating cognitive impairment in the Word Memory Test or performing it to the best of their ability, were analyzed using the EZ-diffusion model ( N = 198). The groups differed in the three model parameters, with drift rate emerging as the best predictor of group membership. These findings provide initial evidence for the usefulness of the drift diffusion model in clarifying the cognitive processes underlying feigned cognitive impairment and encourage further research.

scholarly journals Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data

2020 ◽  
Vol 3 (4) ◽  
pp. 458-471 ◽  
Author(s):  
Mads L. Pedersen ◽  
Michael J. Frank
Keyword(s):  
Parameter Estimation ◽  
Reinforcement Learning ◽  
Diffusion Model ◽  
Simultaneous Estimation ◽  
Model Parameters ◽  
Hierarchical Bayesian ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Bayesian Parameter Estimation ◽  
The Impact

AbstractCognitive models have been instrumental for generating insights into the brain processes underlying learning and decision making. In reinforcement learning it has recently been shown that not only choice proportions but also their latency distributions can be well captured when the choice function is replaced with a sequential sampling model such as the drift diffusion model. Hierarchical Bayesian parameter estimation further enhances the identifiability of distinct learning and choice parameters. One caveat is that these models can be time-consuming to build, sample from, and validate, especially when models include links between neural activations and model parameters. Here we describe a novel extension to the widely used hierarchical drift diffusion model (HDDM) toolbox, which facilitates flexible construction, estimation, and evaluation of the reinforcement learning drift diffusion model (RLDDM) using hierarchical Bayesian methods. We describe the types of experiments most applicable to the model and provide a tutorial to illustrate how to perform quantitative data analysis and model evaluation. Parameter recovery confirmed that the method can reliably estimate parameters with varying numbers of synthetic subjects and trials. We also show that the simultaneous estimation of learning and choice parameters can improve the sensitivity to detect brain–behavioral relationships, including the impact of learned values and fronto-basal ganglia activity patterns on dynamic decision parameters.

scholarly journals Modeling flexible behaviour in autism spectrum disorder and typical development

2019 ◽  
Author(s):  
Daisy Crawley ◽  
Lei Zhang ◽  
Emily J.H. Jones ◽  
Jumana Ahmad ◽  
Antonia San Jose Caceres ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Computational Modeling ◽  
Age Groups ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Model Parameters ◽  
Typical Development ◽  
Feedback Sensitivity ◽  
Flexible Behavior

Flexible behavior is critical for everyday decision-making and has been implicated in restricted, repetitive behaviors (RRB) in autism spectrum disorder (ASD). However, how flexible behavior changes developmentally in ASD remains largely unknown. Here, we used a developmental approach and examined flexible behavior on a probabilistic reversal learning task in 572 children, adolescents and adults (ASD N=321; typical development, TD; N=251). Using computational modeling, we quantified latent variables that index mechanisms underlying perseveration and feedback sensitivity. We then assessed these variables in relation to diagnosis, developmental stage, core autism symptomatology and associated psychiatric symptoms. Autistic individuals showed on average more perseveration and less feedback sensitivity than TD individuals and, across cases and controls, older age groups showed more feedback sensitivity than younger age groups. Computational modeling revealed that dominant learning mechanisms underpinning flexible behavior differed across developmental stages and reduced flexible behavior in ASD was driven by less optimal learning on average within each age group. In autistic children, perseverative errors were positively related to anxiety symptoms, and in autistic adults, perseveration (indexed by both task errors and model parameters) was positively related to RRB. These findings provide novel insights into reduced flexible behavior in relation to clinical symptoms in ASD.

scholarly journals Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1204
Author(s):  
Adèle Helena Ribeiro ◽  
Maciel Calebe Vidal ◽  
João Ricardo Sato ◽  
André Fujita
Keyword(s):  
Autism Spectrum Disorder ◽  
Granger Causality ◽  
Brain Connectivity ◽  
Graph Model ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Sensor Technology ◽  
Model Parameters ◽  
Time Varying ◽  
Random Graph Model

Graphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. Thus, we aim to infer Granger causality (G-causality) between networks’ time series. In this case, the straightforward application of the well-established vector autoregressive model is not feasible. Consequently, we require a theoretical framework for modeling time-varying graphs. One possibility would be to consider a mathematical graph model with time-varying parameters (assumed to be random variables) that generates the network. Suppose we identify G-causality between the graph models’ parameters. In that case, we could use it to define a G-causality between graphs. Here, we show that even if the model is unknown, the spectral radius is a reasonable estimate of some random graph model parameters. We illustrate our proposal’s application to study the relationship between brain hemispheres of controls and children diagnosed with Autism Spectrum Disorder (ASD). We show that the G-causality intensity from the brain’s right to the left hemisphere is different between ASD and controls.

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