scholarly journals Impaired Evidence Accumulation as a Transdiagnostic Vulnerability Factor in Psychopathology

2020 ◽  
Author(s):  
Chandra Sripada ◽  
Alexander Samuel Weigard
Keyword(s):  
Computational Models ◽  
Sequential Sampling ◽  
Self Report ◽  
Evidence Accumulation ◽  
Hyperactivity Disorder ◽  
Sequential Sampling Models ◽  
Intelligence Scores ◽  
Choice Tasks ◽  
Substantial Interest ◽  
Healthy Participants

There is substantial interest in identifying biobehavioral dimensions of individual variation that cut across heterogenous disorder categories, and computational models can play a major role in advancing this goal. In this report, we focused on efficiency of evidence accumulation (EEA), a computationally characterized variable derived from sequential sampling models of choice tasks. We created an EEA factor from three behavioral tasks in the UCLA Phenomics dataset (n=272), which includes healthy participants (n=130) as well participants with schizophrenia (n=50), bipolar disorder (n=49), and attention-deficit/hyperactivity disorder (n=43). We found that the EEA factor was significantly reduced in all three disorders, and that it correlated with an overall severity score for psychopathology as well as self-report measures of impulsivity. Although EEA was significantly correlated with general intelligence, it remained associated with psychopathology and symptom scales even after controlling for intelligence scores. Taken together, these findings suggest EEA is a promising computationally-characterized dimension of neurocognitive variation, with diminished EEA conferring transdiagnostic vulnerability to psychopathology.

scholarly journals Impaired Evidence Accumulation as a Transdiagnostic Vulnerability Factor in Psychopathology

2021 ◽  
Vol 12 ◽  
Author(s):  
Chandra Sripada ◽  
Alexander Weigard
Keyword(s):  
Computational Models ◽  
Sequential Sampling ◽  
Self Report ◽  
Evidence Accumulation ◽  
Hyperactivity Disorder ◽  
Sequential Sampling Models ◽  
Intelligence Scores ◽  
Choice Tasks ◽  
Substantial Interest ◽  
Healthy Participants

There is substantial interest in identifying biobehavioral dimensions of individual variation that cut across heterogenous disorder categories, and computational models can play a major role in advancing this goal. In this report, we focused on efficiency of evidence accumulation (EEA), a computationally characterized variable derived from sequential sampling models of choice tasks. We created an EEA factor from three behavioral tasks in the UCLA Phenomics dataset (n = 272), which includes healthy participants (n = 130) as well-participants with schizophrenia (n = 50), bipolar disorder (n = 49), and attention-deficit/hyperactivity disorder (n = 43). We found that the EEA factor was significantly reduced in all three disorders, and that it correlated with an overall severity score for psychopathology as well as self-report measures of impulsivity. Although EEA was significantly correlated with general intelligence, it remained associated with psychopathology and symptom scales even after controlling for intelligence scores. Taken together, these findings suggest EEA is a promising computationally-characterized dimension of neurocognitive variation, with diminished EEA conferring transdiagnostic vulnerability to psychopathology.

scholarly journals Unification of Countermanding and Perceptual Decision-making

2017 ◽  
Author(s):  
Paul G. Middlebrooks ◽  
Bram B. Zandbelt ◽  
Gordon D. Logan ◽  
Thomas J. Palmeri ◽  
Jeffrey D. Schall
Keyword(s):  
Decision Making ◽  
Response Inhibition ◽  
Sequential Sampling ◽  
Race Model ◽  
Decision Time ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Evidence Accumulation ◽  
Sequential Sampling Models ◽  
Choice Tasks

Perceptual decision-making, studied using two-alternative forced-choice tasks, is explained by sequential sampling models of evidence accumulation, which correspond to the dynamics of neurons in sensorimotor structures of the brain1 2. Response inhibition, studied using stop-signal (countermanding) tasks, is explained by a race model of the initiation or canceling of a response, which correspond to the dynamics of neurons in sensorimotor structures3 4. Neither standard model accounts for performance of the other task. Sequential sampling models incorporate response initiation as an uninterrupted non-decision time parameter independent of task-related variables. The countermanding race model does not account for the choice process. Here we show with new behavioral, neural and computational results that perceptual decision making of varying difficulty can be countermanded with invariant efficiency, that single prefrontal neurons instantiate both evidence accumulation and response inhibition, and that an interactive race between two GO and one STOP stochastic accumulator fits countermanding choice behavior. Thus, perceptual decision-making and response control, previously regarded as distinct mechanisms, are actually aspects of more flexible behavior supported by a common neural and computational mechanism. The identification of this aspect of decision-making with response production clarifies the component processes of decision-making.

scholarly journals Timing of readiness potentials reflect a decision-making process in the human brain

2018 ◽  
Author(s):  
Kitty K. Lui ◽  
Michael D. Nunez ◽  
Jessica M. Cassidy ◽  
Joachim Vandekerckhove ◽  
Steven C. Cramer ◽  
...  
Keyword(s):  
Decision Making ◽  
Response Time ◽  
Response Selection ◽  
Sequential Sampling ◽  
Perceptual Categorization ◽  
Accumulation Process ◽  
Time Data ◽  
Evidence Accumulation ◽  
Sequential Sampling Models ◽  
Readiness Potentials

AbstractDecision-making in two-alternative forced choice tasks has several underlying components including stimulus encoding, perceptual categorization, response selection, and response execution. Sequential sampling models of decision-making are based on an evidence accumulation process to a decision boundary. Animal and human studies have focused on perceptual categorization and provide evidence linking brain signals in parietal cortex to the evidence accumulation process. In this exploratory study, we use a task where the dominant contribution to response time is response selection and model the response time data with the drift-diffusion model. EEG measurement during the task show that the Readiness Potential (RP) recorded over motor areas has timing consistent with the evidence accumulation process. The duration of the RP predicts decision-making time, the duration of evidence accumulation, suggesting that the RP partly reflects an evidence accumulation process for response selection in the motor system. Thus, evidence accumulation may be a neural implementation of decision-making processes in both perceptual and motor systems. The contributions of perceptual categorization and response selection to evidence accumulation processes in decision-making tasks can be potentially evaluated by examining the timing of perceptual and motor EEG signals.

scholarly journals Task-general efficiency of evidence accumulation as a computationally-defined neurocognitive trait: Implications for clinical neuroscience

2020 ◽  
Author(s):  
Alexander Samuel Weigard ◽  
Chandra Sripada
Keyword(s):  
Computational Models ◽  
Sequential Sampling ◽  
Experimental Manipulation ◽  
Higher Order ◽  
Mathematical Psychology ◽  
Cognitive Tasks ◽  
Neurocognitive Deficits ◽  
Clinical Neuroscience ◽  
Evidence Accumulation ◽  
Data Generating Process

Quantifying individual differences in higher-order cognitive functions is a foundational area of cognitive science that also has profound implications for research on psychopathology. For the last two decades, the dominant approach in these fields has been to attempt to fractionate higher-order functions into hypothesized components (e.g., “inhibition”, “updating”) through a combination of experimental manipulation and factor analysis. However, the putative structures obtained through this paradigm have recently been met with substantial criticism on both theoretical and empirical grounds. Concurrently, an alternative approach has emerged focusing on parameters of formal computational models of cognition that have been developed in mathematical psychology. These models posit biologically plausible and experimentally validated explanations of the data-generating process for cognitive tasks, allowing them to be used to measure the latent mechanisms that underlie performance. One of the primary insights provided by recent applications of such models is that individual and clinical differences in performance on a wide variety of cognitive tasks, ranging from simple choice tasks to complex executive paradigms, are largely driven by efficiency of evidence accumulation (EEA), a computational mechanism defined by sequential sampling models. This review assembles evidence for the hypothesis that EEA is a central individual difference dimension that explains neurocognitive deficits in multiple clinical disorders and identifies ways in which in this insight can advance clinical neuroscience research. We propose that recognition of EEA as a major driver of neurocognitive differences will allow the field to make clearer inferences about cognitive abnormalities in psychopathology and their links to neurobiology.

scholarly journals Sequential Sampling Models Without Random Between-Trial Variability: The Racing Diffusion Model of Speeded Decision

2017 ◽  
Author(s):  
Gabriel Tillman
Keyword(s):  
Response Time ◽  
Diffusion Model ◽  
Response Times ◽  
Sequential Sampling ◽  
Good Reason ◽  
Accumulation Process ◽  
Time Data ◽  
Evidence Accumulation ◽  
Sequential Sampling Models ◽  
Sequential Sampling Model

Most current sequential sampling models have random between-trial variability in their parameters. These sources of variability make the models more complex in order to fit response time data, do not provide any further explanation to how the data were generated, and have recently been criticised for allowing infinite flexibility in the models. To explore and test the need of between-trial variability parameters we develop a simple sequential sampling model of N-choice speeded decision making: the racing diffusion model. The model makes speeded decisions from a race of evidence accumulators that integrate information in a noisy fashion within a trial. The racing diffusion does not assume that any evidence accumulation process varies between trial, and so, the model provides alternative explanations of key response time phenomena, such as fast and slow error response times relative to correct response times. Overall, our paper gives good reason to rethink including between-trial variability parameters in sequential sampling models

scholarly journals Evidence Accumulates for Individual Attributes during Value-Based Decisions

2021 ◽  
Author(s):  
Douglas G. Lee ◽  
Todd A. Hare
Keyword(s):  
Computational Models ◽  
Response Times ◽  
Sequential Sampling ◽  
Choice Data ◽  
Attribute Importance ◽  
Snack Foods ◽  
Sequential Sampling Models ◽  
Level Information ◽  
Binary Choices ◽  
Important Evidence

When choosing between different options, we tend to consider specific attribute qualities rather than deliberating over some general sense of the objects' overall values. The importance of each attribute together with its quality will determine our preference rankings over the available alternatives. Here, we show that the relative importance of the latent attributes within food rewards reliably differs when the items are evaluated in isolation compared to when binary choices are made between them. Specifically, we used standard regression and sequential sampling models to examine six datasets in which participants evaluated, and chose between, multi-attribute snack foods. We show that models that assume that attribute importance remains constant across evaluation and choice contexts fail to reproduce fundamental patterns in the choice data and provide quantitatively worse fits to the choice outcomes, response times, and confidence reports compared to models that allow for attribute importance to vary across preference elicitation methods. Our results provide important evidence that incorporating attribute-level information into computational models helps us to better understand the cognitive processes involved in value-based decision-making.

scholarly journals A model of dynamic, within-trial conflict resolution for decision making

2019 ◽  
Author(s):  
Emily Ruth Weichart ◽  
Brandon Turner ◽  
Per B. Sederberg
Keyword(s):  
Cognitive Control ◽  
Lateral Inhibition ◽  
Time Course ◽  
Flanker Task ◽  
Sequential Sampling ◽  
Behavioral Data ◽  
Emergent Properties ◽  
Evidence Accumulation ◽  
Sequential Sampling Models ◽  
Bayesian Approximation

Growing evidence for moment-to-moment fluctuations in visual attention has led to questions about the impetus and time course of cognitive control. These questions are typically investigated with paradigms like the flanker task, which require participants to inhibit an automatic response before making a decision. Connectionist modeling work suggests that between-trial changes in attention result from fluctuations in conflict--as conflict occurs, attention needs to be up-regulated in order to resolve it. Current sequential sampling models (SSMs) of within-trial effects, however, suggest that attention focuses on a goal-relevant target as a function of time. We propose that within-trial changes in cognitive control and attention are emergent properties of the dynamics of the decision itself. We tested our hypothesis by developing a set of SSMs, each making alternative assumptions about attention modulation and evidence accumulation mechanisms. Combining the SSM framework with likelihood-free Bayesian approximation methods allowed us to conduct quantified comparisons between subject-level fits. Models included either time- or control-based attention mechanisms, and either strongly- (via feedforward inhibition) or weakly-correlated (via leak and lateral inhibition) evidence accumulation mechanisms. We fit all models to behavioral data collected in variants of the flanker task, one accompanied by EEG measures. Across three experiments, we found converging evidence that control-based attention processes in combination with evidence accumulation mechanisms governed by leak and lateral inhibition provided the best fits to behavioral data, and uniquely mapped onto observed decision-related signals in the brain.

Samuel, D.B. & Ranseen, J.R. (2006, October). Stimulant medication’s effects on adult ADHD symptoms and NEO PI-R personality traits

2018 ◽  
Author(s):  
Douglas Samuel ◽  
John D. Ranseen
Keyword(s):  
Personality Traits ◽  
Neuropsychological Tests ◽  
Adult Adhd ◽  
Self Report ◽  
Emotional States ◽  
Limited Information ◽  
Stimulant Treatment ◽  
Hyperactivity Disorder ◽  
Dsm Iv ◽  
Adhd Symptomatology

Previous studies have indicated a consistent profile of basic personality traits correlated with adult Attention Deficit Hyperactivity Disorder (ADHD) (e.g., Ranseen, Campbell, & Baer, 1998; Nigg et al., 2002). In particular, research has found that low scores of the Conscientiousness trait and high scores on Neuroticism have been correlated with ADHD symptomatology. However, to date there is limited information concerning the range of effect resulting from medication treatment for adult ADHD. During an 18 month period, 60 adults were diagnosed with ADHD based on strict, DSM-IV criteria at an outpatient clinic. This evaluation included a battery of neuropsychological tests and a measure of general personality (i.e., the NEO PI-R). Eleven of these participants returned to complete the battery a second time. The pre-post comparisons revealed significant changes following sustained stimulant treatment on both the neuropsychological and self-report measures. These individuals also displayed significant changes on two domains of the NEO PI-R. They showed a significant decrease on the domain of Neuroticism, indicating that now see themselves as less prone to experience negative emotional states such as anxiety and depression. Additionally, they also reported a significant increase on their scores on the domain of conscientiousness. This increase suggests that they see themselves as more organized and dependable.

Sign in / Sign up

Export Citation Format

Share Document