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

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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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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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

10.31234/osf.io/jxfqd ◽

2021 ◽

Author(s):

Mads Lund Pedersen ◽

Dag Alnæs ◽

Dennis van der Meer ◽

Sara Fernandez ◽

Pierre Berthet ◽

...

Keyword(s):

Decision Making ◽

Working Memory ◽

Mental Disorders ◽

Computational Modeling ◽

Decision Time ◽

Decision Threshold ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Cardiometabolic Diseases ◽

Back Task

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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Lexical-semantic and executive deficits revealed by computational modelling: a drift diffusion model perspective

10.31234/osf.io/kdpgz ◽

2019 ◽

Author(s):

Lara Todorova ◽

David Neville ◽

Vitória Piai

Keyword(s):

Semantic Memory ◽

Diffusion Model ◽

Drift Rate ◽

Related Word ◽

Model Parameters ◽

Decision Threshold ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Lexical Semantic ◽

Two Systems

Flexible language use requires coordinated functioning of two systems: conceptual representations and control. The interaction between two systems can be observed when people are asked to match a word to a picture. Participants are slower and less accurate for related word-picture pairs (word: banana, picture: apple) relative to unrelated pairs (word: banjo, picture: apple). The mechanism underlying interference however is still unclear. We analyzed word-picture verification (WPM) performance of patients with stroke-induced lesions to the left-temporal (N = 5) or left-frontal cortex (N = 5) and matched controls (N = 12) using the drift diffusion model (DDM). In DDM the process of making a decision is described as the stochastic accumulation of evidence towards a response. The parameters of the DDM model that characterize this process are decision threshold, drift rate, starting point and non-decision time. Each of them bears cognitive interpretability and we compared the estimated model parameters from controls and patients to investigate the mechanisms of WPM interference. WPM performance in controls was explained by the amount of information needed to make a decision (decision threshold): a higher threshold was associated with related word-picture pairs relative to unrelated ones. No difference was found in the quality of the evidence (drift rate). This suggests an executive rather than semantic mechanism underlying WPM interference. Both patients with temporal and frontal lesions exhibited both increased drift rate and decision threshold for unrelated pairs relative to related ones. Left-frontal and temporal damage affected the computations required by WPM similarly, resulting in systematic deficits across lexical-semantic memory and executive functions. These results support a diverse but interactive role of lexical-semantic memory and semantic control mechanisms.

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Brain network segregation and integration during an epoch-related working memory fMRI experiment

10.1101/252338 ◽

2018 ◽

Author(s):

Peter Fransson ◽

Björn C. Schiffler ◽

William Hedley Thompson

Keyword(s):

Working Memory ◽

Brain Network ◽

High Temporal Resolution ◽

Imaging Data ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Point Estimates ◽

Fmri Experiment ◽

Temporal Profiles

AbstractThe characterization of brain subnetwork segregation and integration has previously focused on changes that are detectable at the level of entire sessions or epochs of imaging data. In this study, we applied time-varying functional connectivity analysis together with temporal network theory to calculate point-by-point estimates in subnetwork segregation and integration during an epoch-based (2-back, 0-back, baseline) working memory fMRI experiment as well as during resting-state. This approach allowed us to follow task-related changes in subnetwork segregation and integration at a high temporal resolution. At a global level, the cognitively more taxing 2-back epochs elicited an overall stronger response of integration between subnetworks compared to the 0-back epochs. Moreover, the visual and fronto-parietal subnetworks displayed characteristic and distinct temporal profiles of segregation and integration during the 0- and 2-back epochs. During the interspersed epochs of baseline, many subnetworks, including the default mode, visual, fronto-parietal, cingulo-opercular and dorsal attention subnetworks showed pronounced increases in segregation. Using a drift diffusion model we show that the response time for the 2-back trials are correlated with integration for the fronto-parietal subnetwork and correlated with segregation for the visual subnetwork. Our results elucidate the fast-evolving events with regard to subnetwork integration and segregation that occur in an epoch-related task fMRI experiment. Our findings suggest that minute changes in subnetwork integration are of importance for task performance.

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The Leaky Integrating Threshold and its impact on evidence accumulation models of choice RT

10.31234/osf.io/5t6yz ◽

2020 ◽

Author(s):

Stijn Verdonck ◽

Tim Loossens ◽

Marios G. Philiastides

Keyword(s):

Diffusion Model ◽

Motor Preparation ◽

Difficulty Level ◽

Choice Response ◽

Decision Threshold ◽

Actual Process ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Evidence Accumulation ◽

Speed Accuracy

A common assumption in choice response time modelling is that after evidence accumulation reaches a certain decision threshold, the choice is categorically communicated to the motor system that then executes the response. However, neurophysiological findings suggest that motor preparation partly overlaps with evidence accumulation, and is not independent from stimulus difficulty level. We propose to model this entanglement by changing the nature of the decision criterion from a simple threshold to an actual process. More specifically, we propose a secondary, motor preparation related, leaky accumulation process that takes the accumulated evidence of the original decision process as a continuous input, and triggers the actual response when it reaches its own threshold. We analytically develop this Leaky Integrating Threshold (LIT), applying it to a simple constant drift diffusion model, and show how its parameters can be estimated with the D*M method. Reanalyzing three different datasets, the LIT extension is shown to outperform a standard drift diffusion model using multiple statistical approaches. Further, the LIT leak parameter is shown to be better at explaining the speed/accuracy trade-off manipulation than the commonly used boundary separation parameter. These improvements can also be verified using traditional diffusion model analyses, for which the LIT predicts the violation of several common selective parameter influence assumptions. These predictions are consistent with what is found in the data and with what is reported experimentally in the literature. Crucially, this work offers a new benchmark against which to compare neural data to offer neurobiological validation for the proposed processes.

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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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Informing cognitive abstractions through neuroimaging: The neural drift diffusion model.

Psychological Review ◽

10.1037/a0038894 ◽

2015 ◽

Vol 122 (2) ◽

pp. 312-336 ◽

Cited By ~ 86

Author(s):

Brandon M. Turner ◽

Leendert van Maanen ◽

Birte U. Forstmann

Keyword(s):

Diffusion Model ◽

Drift Diffusion Model ◽

Drift Diffusion

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Dopamine Increases the Gain of the Input-Output Response of Rat Prefrontal Pyramidal Neurons

Journal of Neurophysiology ◽

10.1152/jn.01098.2007 ◽

2008 ◽

Vol 99 (6) ◽

pp. 2985-2997 ◽

Cited By ~ 66

Author(s):

Kay Thurley ◽

Walter Senn ◽

Hans-Rudolf Lüscher

Keyword(s):

Working Memory ◽

Neuronal Excitability ◽

Pyramidal Neurons ◽

D1 Receptors ◽

Input Output ◽

Prefrontal Cortical ◽

Noisy Input ◽

Relationship Of

Dopaminergic modulation of prefrontal cortical activity is known to affect cognitive functions like working memory. Little consensus on the role of dopamine modulation has been achieved, however, in part because quantities directly relating to the neuronal substrate of working memory are difficult to measure. Here we show that dopamine increases the gain of the frequency-current relationship of layer 5 pyramidal neurons in vitro in response to noisy input currents. The gain increase could be attributed to a reduction of the slow afterhyperpolarization by dopamine. Dopamine also increases neuronal excitability by shifting the input-output functions to lower inputs. The modulation of these response properties is mainly mediated by D1 receptors. Integrate-and-fire neurons were fitted to the experimentally recorded input-output functions and recurrently connected in a model network. The gain increase induced by dopamine application facilitated and stabilized persistent activity in this network. The results support the hypothesis that catecholamines increase the neuronal gain and suggest that dopamine improves working memory via gain modulation.

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