Computational modeling of the N-Back task in the ABCD study: associations of drift diffusion model parameters to polygenic scores of mental disorders and cardiometabolic diseases

Background. Cognitive dysfunction is common in mental disorders and represents a potential risk factor in childhood. The nature and extent of associations between childhood cognitive function and polygenic risk for mental disorders is unclear. We applied computational modeling to gain insight into mechanistic processes underlying decision making and working memory in childhood and their associations with PRS for mental disorders and comorbid cardiometabolic diseases. Methods. We used the drift diffusion model to infer latent computational processes underlying decision-making and working memory during the N-back task in 3707 children aged 9-10 from the ABCD Study. SNP-based heritability was estimated for cognitive phenotypes, including computational parameters, aggregated N-back task performance and neurocognitive assessments. PRS was calculated for Alzheimer’s disease (AD), bipolar disorder, coronary artery disease (CAD), major depressive disorder, obsessive-compulsive disorder, schizophrenia and type 2 diabetes. Results. Heritability estimates of cognitive phenotypes ranged from 12 to 39%. Bayesian mixed models revealed that slower accumulation of evidence was associated with higher PRS for CAD and schizophrenia. Longer non-decision time was associated with higher PRS for AD and lower PRS for CAD. Narrower decision threshold was associated with higher PRS for CAD. Load-dependent effects on non-decision time and decision threshold were associated with PRS for AD and CAD, respectively. Aggregated neurocognitive test scores were not associated with PRS for any of the mental or cardiometabolic phenotypes.Conclusions. We identified distinct associations between computational cognitive processes to genetic risk for mental illness and cardiometabolic disease, which could represent childhood cognitive risk factors.

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Evidence or Confidence: What Really Accumulates During a Decision?

10.1101/2021.04.02.438275 ◽

2021 ◽

Author(s):

Douglas G. Lee ◽

Giovanni Pezzulo

Keyword(s):

Decision Making ◽

Empirical Evidence ◽

Alternative Hypothesis ◽

Decision Threshold ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Evidence Accumulation ◽

Confidence Assessment ◽

Standard Models ◽

Post Hoc

Assessing one's confidence in one's choices is of paramount importance to making adaptive decisions, and it is thus no surprise that humans excel in this ability. However, standard models of decision-making, such as the drift-diffusion model (DDM), treat confidence assessment as a post-hoc or parallel process that does not directly influence the choice -- the latter depends only on accumulated evidence. Here, we pursue the alternative hypothesis that what is accumulated during a decision is confidence (that the to-be selected option is the best) rather than raw evidence. Accumulating confidence has the appealing consequence that the decision threshold corresponds to a desired level of confidence for the choice, and that confidence improvements can be traded off against the resources required to secure them. We show that most previous findings on perceptual and value-based decisions traditionally interpreted from an evidence-accumulation perspective can be explained more parsimoniously from our novel confidence-driven perspective. Furthermore, we show that our novel confidence-driven DDM (cDDM) naturally generalizes to any number of decisions -- which is notoriously extemporaneous using traditional DDM or related models. Finally, we discuss future empirical evidence that could be useful in adjudicating between these alternatives.

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Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence

10.1101/2020.12.21.423791 ◽

2020 ◽

Author(s):

Rachel Rac-Lubashevsky ◽

Michael J Frank

Keyword(s):

Working Memory ◽

Time Windows ◽

Neural Dynamics ◽

Control Mechanisms ◽

Decision Threshold ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Motor Action ◽

Motor Information ◽

Action Representations

Adaptive cognitive-control is achieved through a hierarchical cortico-striatal gating system that supports selective updating, maintenance, and retrieval of useful cognitive and motor information. Here, we developed a novel task that independently manipulated selective gating operations of working-memory (input), from working-memory (output), and in response (motor) and tested the neural dynamics and computational principles that support them. Increases in gating demands, captured by gate switches, were expressed by distinct EEG correlates at each gating level that evolved dynamically in partially overlapping time windows. EEG decoding analysis further showed that neural indexes of working-memory (category) and motor (action) representations were prioritized particularly when the corresponding gate was switching. Finally, the control mechanisms involved in gate switches were quantified by the drift diffusion model, showing elevated motor decision threshold in all gating levels. Together these results support the notion that cognitive gating operations scaffold on top of mechanisms involved in motor gating.

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Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008971 ◽

2021 ◽

Vol 17 (6) ◽

pp. e1008971

Author(s):

Rachel Rac-Lubashevsky ◽

Michael J. Frank

Keyword(s):

Working Memory ◽

Time Windows ◽

Neural Dynamics ◽

Decision Threshold ◽

Gating System ◽

Input Output ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Motor Responses ◽

Motor Information

Adaptive cognitive-control involves a hierarchical cortico-striatal gating system that supports selective updating, maintenance, and retrieval of useful cognitive and motor information. Here, we developed a task that independently manipulates selective gating operations into working-memory (input gating), from working-memory (output gating), and of responses (motor gating) and tested the neural dynamics and computational principles that support them. Increases in gating demands, captured by gate switches, were expressed by distinct EEG correlates at each gating level that evolved dynamically in partially overlapping time windows. Further, categorical representations of specific maintained items and of motor responses could be decoded from EEG when the corresponding gate was switching, thereby linking gating operations to prioritization. Finally, gate switching at all levels was related to increases in the motor decision threshold as quantified by the drift diffusion model. Together these results support the notion that cognitive gating operations scaffold on top of mechanisms involved in motor gating.

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Cognitive Control of Working Memory: A Model-Based Approach

Brain Sciences ◽

10.3390/brainsci11060721 ◽

2021 ◽

Vol 11 (6) ◽

pp. 721

Author(s):

Russell J. Boag ◽

Niek Stevenson ◽

Roel van Dooren ◽

Anne C. Trutti ◽

Zsuzsika Sjoerds ◽

...

Keyword(s):

Decision Making ◽

Working Memory ◽

Cognitive Control ◽

Relevant Information ◽

Decision Time ◽

Control Processes ◽

Prepotent Response ◽

New Information ◽

Empirical Performance ◽

Flow Of Information

Working memory (WM)-based decision making depends on a number of cognitive control processes that control the flow of information into and out of WM and ensure that only relevant information is held active in WM’s limited-capacity store. Although necessary for successful decision making, recent work has shown that these control processes impose performance costs on both the speed and accuracy of WM-based decisions. Using the reference-back task as a benchmark measure of WM control, we conducted evidence accumulation modeling to test several competing explanations for six benchmark empirical performance costs. Costs were driven by a combination of processes, running outside of the decision stage (longer non-decision time) and showing the inhibition of the prepotent response (lower drift rates) in trials requiring WM control. Individuals also set more cautious response thresholds when expecting to update WM with new information versus maintain existing information. We discuss the promise of this approach for understanding cognitive control in WM-based decision making.

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Flexible categorization in perceptual decision making

Nature Communications ◽

10.1038/s41467-021-21501-z ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 1

Author(s):

Genís Prat-Ortega ◽

Klaus Wimmer ◽

Alex Roxin ◽

Jaime de la Rocha

Keyword(s):

Decision Making ◽

Network Models ◽

Drift Diffusion Model ◽

Perceptual Decision Making ◽

Drift Diffusion ◽

Perceptual Decision ◽

Attractor Dynamics ◽

Attractor Model ◽

Sensory Evidence ◽

Winner Take All

AbstractPerceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

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Speed, Accuracy, and the Optimal Timing of Choices

The American Economic Review ◽

10.1257/aer.20150742 ◽

2018 ◽

Vol 108 (12) ◽

pp. 3651-3684 ◽

Cited By ~ 36

Author(s):

Drew Fudenberg ◽

Philipp Strack ◽

Tomasz Strzalecki

Keyword(s):

Sequential Sampling ◽

Decision Time ◽

Optimal Timing ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Prior Beliefs ◽

Constant Cost ◽

Speed Accuracy ◽

Decision Times ◽

Choice Probabilities

We model the joint distribution of choice probabilities and decision times in binary decisions as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant cost per unit time for gathering information. We show that choices are more likely to be correct when the agent chooses to decide quickly, provided the agent’s prior beliefs are correct. This better matches the observed correlation between decision time and choice probability than does the classical drift-diffusion model (DDM), where the agent knows the utility difference between the choices. (JEL C41, D11, D12, D83)

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The Effect of Single Bout of Acute Exercise on Working Memory Performance

Birat Journal of Health Sciences ◽

10.3126/bjhs.v3i2.20966 ◽

2018 ◽

Vol 3 (2) ◽

pp. 484-487

Author(s):

Santosh Kumar Deo ◽

Kopila Agrawal ◽

Prem Bhattrai ◽

Raju Kumar Chaudhary

Keyword(s):

Decision Making ◽

Working Memory ◽

Acute Exercise ◽

Test Procedure ◽

Step Test ◽

Moderate Intensity ◽

Exercise Session ◽

Moderate Intensity Exercise ◽

Single Bout ◽

Back Task

Introduction: Working memory is a kind of short term memory important for reasoning and guiding decision-making and behavioral process.Objective: The goal of the present research was to study the outcome of single bout of acute moderate-intensity exercise on working memory.Methodology: Twenty two male subjects were asked to perform working memory task by 2n back task in baseline resting, immediately after exercise and after five minute of exercise session. 3 minute step test procedure was used as a moderate intensity exercise intervention.Results: The percentage correctness of 2n back task of working memory was found to be 64.36% for baseline resting condition, 78.01 % for immediately after 3-minute step test and 80.70% for 5 minute after the exercise. In both exercise session (i.e. immediately after exercise and after 5 minute of exercise), significant improvement (p value <0.05) in working memory was seen as compared to the baseline resting session while no such significant beneficial improvement was seen when compared between immediately after exercise and after 5 minute of exercise.Conclusion: Improvement in working memory after moderate exercise intervention was seen, which is important for learning and memory and decision-making. BJHS 2018;3(2)6:484-487.

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Same model, different conclusions: An identifiability issue in the linear ballistic accumulator model of decision-making

10.31234/osf.io/2xu7f ◽

2020 ◽

Author(s):

Nathan J. Evans

Keyword(s):

Decision Making ◽

Diffusion Model ◽

Large Scale ◽

Drift Rate ◽

Decision Time ◽

Measurement Properties ◽

Decision Threshold ◽

Experimental Conditions ◽

Model Application ◽

Linear Ballistic Accumulator

Evidence accumulation models (EAMs) – the dominant modelling framework for speeded decision-making – have become an important tool for model application. Model application involves using specific model to estimate parameter values that relate to different components of the cognitive process, and how these values differ over experimental conditions and/or between groups of participants. In this context, researchers are often agnostic to the specific theoretical assumptions made by different EAM variants, and simply desire a model that will provide them with an accurate measurement of the parameters that they are interested in. However, recent research has suggested that the two most commonly applied EAMs – the diffusion model and the linear ballistic accumulator (LBA) – come to fundamentally different conclusions when applied to the same empirical data. The current study provides an in-depth assessment of the measurement properties of the two models, as well as the mapping between, using two large scale simulation studies and a reanalysis of Evans (2020a). Importantly, the findings indicate that there is a major identifiability issue within the standard LBA, where differences in decision threshold between conditions are practically unidentifiable, which appears to be caused by a tradeoff between the threshold parameter and the overall drift rate across the different accumulators. While this issue can be remedied by placing some constraint on the overall drift rate across the different accumulators – such as constraining the average drift rate or the drift rate of one accumulator to have the same value in each condition – these constraints can qualitatively change the conclusions of the LBA regarding other constructs, such as non-decision time. Furthermore, all LBA variants considered in the current study still provide qualitatively different conclusions to the diffusion model. Importantly, the current findings suggest that researchers should not use the unconstrained version of the LBA for model application, and bring into question the conclusions of previous studies using the unconstrained LBA.

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Methodological issues with value-based decision-making (VBDM) tasks: does trial wording matter?

10.31234/osf.io/va2zn ◽

2021 ◽

Author(s):

Amber Copeland ◽

Tom Stafford ◽

Matt Field

Keyword(s):

Decision Making ◽

Drift Diffusion Model ◽

Choice Data ◽

Methodological Issues ◽

Drift Diffusion ◽

Initial Validation ◽

Main Effect ◽

Value Judgements ◽

Forced Choice Task ◽

Positive Correlations

Objective: Most value-based decision-making (VBDM) tasks instruct people to make value judgements about stimuli using wording relating to consumption, however in some contexts this may be inappropriate. This study aims to explore whether variations of trial wording capture a common construct of value. Method: Pre-registered, within-subject design. Fifty-nine participants completed a two-alternative forced choice task where they chose between two food images. Participants completed three blocks of trials: one asked which they would rather consume (standard wording), one asked which image they like more, and one asked them to recall which image they rated higher during a previous block. We fitted a drift-diffusion model to the reaction time and choice data to estimate evidence accumulation (EA) processes during the different blocks. Results: There was a highly significant main effect of trial difficulty, but this was not modified by trial wording (F = 2.00, p = .11, np2 = .03, BF10 = .05). We also found highly significant positive correlations between EA rates across task blocks (rs > .44, ps < .001). Conclusions: Findings provide initial validation of substitute wording for VBDM tasks that can be used in contexts where it may be undesirable to ask participants to make consummatory judgements.

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A flexible framework for simulating and fitting generalized drift-diffusion models

10.1101/2020.03.14.992065 ◽

2020 ◽

Cited By ~ 1

Author(s):

Maxwell Shinn ◽

Norman H. Lam ◽

John D. Murray

Keyword(s):

Decision Making ◽

Diffusion Model ◽

Cognitive Neuroscience ◽

Software Package ◽

Planck Equation ◽

Experimental Designs ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Flexible Framework ◽

Decision Making Model

AbstractThe drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce the generalized drift-diffusion model (GDDM) framework for building, simulating, and fitting DDM extensions, and provide a software package which implements the framework. The GDDM framework augments traditional DDM parameters through arbitrary user-defined functions. Models are simulated numerically by directly solving the Fokker-Planck equation using efficient numerical methods, yielding a 100-fold or greater speedup over standard methodology. This speed allows GDDMs to be fit to data using maximum likelihood on the full response time (RT) distribution. We show that a GDDM fit with our framework explains a classic open dataset with better accuracy and fewer parameters than several DDMs implemented using the latest methodology. Overall, our framework will allow for decision-making model innovation and novel experimental designs.

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