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

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.

scholarly journals Think fast! The implications of emphasizing urgency in decision-making

2020 ◽  
Author(s):  
Nathan J. Evans
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Drift Rate ◽  
Decision Time ◽  
Decision Making Process ◽  
Data Sets ◽  
Decision Threshold ◽  
Evidence Accumulation ◽  
Rate Decrease ◽  
Linear Ballistic Accumulator

Evidence accumulation models (EAMs) have become the dominant explanation of how the decision-making process operates, proposing that decisions are the result of a process of evidence accumulation. The primary use of EAMs has been as "measurement tools" of the underlying decision-making process, where researchers apply EAMs to empirical data to estimate participants' task ability (i.e., the "drift rate"), response caution (i.e., the "decision threshold"), and the time taken for other processes (i.e., the "non-decision time"), making EAMs a powerful tool for discriminating between competing psychological theories. Recent studies have brought into question the mapping between the latent parameters of EAMs and the theoretical constructs that they are thought to represent, showing that emphasizing urgent responding -- which intuitively should selectively influence decision threshold -- may also influence drift rate and/or non-decision time. However, these findings have been mixed, leading to differences in opinion between experts in the field. The current study aims to provide a more conclusive answer to the implications of emphasizing urgent responding, providing a re-analyse of 6 data sets from previous studies using two different EAMs -- the diffusion model and the linear ballistic accumulator (LBA) -- with state-of-the-art methods for model selection based inference. The findings display clear evidence for a difference in conclusions between the two models, with the diffusion model suggesting that decision threshold and non-decision time decrease when urgency is emphasized, and the LBA suggesting that decision threshold and drift rate decrease when urgency is emphasized. Furthermore, although these models disagree regarding whether non-decision time or drift rate decrease under urgency emphasis, both show clear evidence that emphasizing urgency does not selectively influence decision threshold. These findings suggest that researchers should revise their assumptions about certain experimental manipulations, the specification of certain EAMs, or perhaps both.

scholarly journals Perceptual decision-making in children: Age-related differences and EEG correlates

2020 ◽  
Author(s):  
Catherine Manning ◽  
Eric-Jan Wagenmakers ◽  
Anthony Norcia ◽  
Gaia Scerif ◽  
Udo Boehm
Keyword(s):  
Decision Making ◽  
Drift Rate ◽  
Decision Time ◽  
Diffusion Models ◽  
Perceptual Task ◽  
Model Parameters ◽  
Older Children ◽  
Perceptual Decision Making ◽  
Boundary Separation ◽  
Age Related

Children make faster and more accurate decisions about perceptual information as they get older, but it is unclear how different aspects of the decision-making process change with age. Here, we used hierarchical Bayesian diffusion models to decompose performance in a perceptual task into separate processing components, testing age-related differences in model parameters and links to neural data. We collected behavioural and EEG data from 96 six- to twelve-year-olds and 20 adults completing a motion discrimination task. We used a component decomposition technique to identify two response-locked EEG components with ramping activity preceding the response in children and adults: one with activity that was maximal over centro-parietal electrodes and one that was maximal over occipital electrodes. Younger children had lower drift rates (reduced sensitivity), wider boundary separation (increased response caution) and longer non-decision times than older children and adults. Yet model comparisons suggested that the best model of children’s data included age effects only on drift rate and boundary separation (not non-decision time). Next we extracted the slope of ramping activity in our EEG components and covaried these with drift rate. The slopes of both EEG components related positively to drift rate, but the best model with EEG covariates included only the centro-parietal component. By decomposing performance into distinct components and relating them to neural markers, diffusion models have the potential to identify the reasons why children with developmental conditions perform differently to typically developing children - and to uncover processing differences inapparent in the response time and accuracy data alone.

scholarly journals 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

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.

scholarly journals Information processing under versus under punishment

2018 ◽  
Author(s):  
Timothy Ballard ◽  
David K. Sewell ◽  
Daniel Cosgrove ◽  
Andrew Neal
Keyword(s):  
Decision Making ◽  
Information Processing ◽  
Judgment Task ◽  
Perceptual Judgment ◽  
Decision Threshold ◽  
System P ◽  
Linear Ballistic Accumulator ◽  
Decision Environment ◽  
Reward And Punishment

Much is known about the effects of reward and punishment on behavior, yet little research has considered how these incentives influence the information processing dynamics that underlie decision making. We fit the linear ballistic accumulator to data from a perceptual judgment task to examine the impacts of reward- and punishment-based incentives on three distinct components of information processing: the quality of the information processed, the quantity of that information, and the decision threshold. The threat of punishment lowered the average quality and quantity of information processed compared to the prospect of reward or no performance incentive at all. The threat of punishment also induced less cautious decision making by lowering people’s decision thresholds relative to the prospect of reward. These findings suggest that information processing dynamics are not wholly determined by objective properties of the decision environment, but also by the higher order goals of the system.

Differential Threshold of Length and Response Criterion for Inspecting Irregular Objects

1997 ◽  
Vol 85 (2) ◽  
pp. 723-735
Author(s):  
Chia-Fen Chi ◽  
Chin-Lung Chen
Keyword(s):  
Decision Making ◽  
Signal Detection ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Decision Time ◽  
Visual Sensitivity ◽  
Differential Threshold ◽  
Human Beings ◽  
Experimental Conditions ◽  
Defective Items

This research investigated human visual sensitivity and bias in inspecting irregular objects. A preliminary study was conducted using the method of constants to determine the threshold value for judgment of size. A factorial experiment was conducted using payoffs, rate of defective items, and detectability in the signal-detection theory as the factors. In total, eight experimental conditions were tested. 10 college students were recruited as subjects. Each subject was asked to compare 40 teapot shapes to a standard teapot shape under eight experimental conditions. Defective shapes were generated by lengthening the vertical dimension of a standard teapot shape by a factor of 1.01 and 1.04 for ‘low’ and ‘high’ detectability. The decision time and responses of ‘identical’ or ‘different’ were collected under all experimental conditions. Analysis indicates that the decision-making strategy used to inspect this irregular object was very close to maximizing the accuracy of decision-making by considering the rate of defective items. This result is different from most research findings in signal-detection theory in which responses of human beings are similar to degraded Bayes optimizers. The standard deviation of the signal distribution was about 1.30 and 1.41 times that of the noise distributions for ‘low’ and ‘high’ detectability.

scholarly journals Information Processing Under Reward Versus Under Punishment

2019 ◽  
Vol 30 (5) ◽  
pp. 757-764
Author(s):  
Timothy Ballard ◽  
David K. Sewell ◽  
Daniel Cosgrove ◽  
Andrew Neal
Keyword(s):  
Decision Making ◽  
Information Processing ◽  
Judgment Task ◽  
Perceptual Judgment ◽  
Decision Threshold ◽  
Average Quality ◽  
Linear Ballistic Accumulator ◽  
Decision Environment ◽  
Reward And Punishment

Much is known about the effects of reward and punishment on behavior, yet little research has considered how these incentives influence the information-processing dynamics that underlie decision making. We fitted the linear ballistic accumulator to data from a perceptual-judgment task to examine the impacts of reward- and punishment-based incentives on three distinct components of information processing: the quality of the information processed, the quantity of that information, and the decision threshold. The threat of punishment lowered the average quality and quantity of information processed, compared with the prospect of reward or no performance incentive at all. The threat of punishment also induced less cautious decision making by lowering people’s decision thresholds relative to the prospect of reward. These findings suggest that information-processing dynamics are determined not only by objective properties of the decision environment but also by the higher order goals of the system.

scholarly journals Unraveling the Relation between EEG Correlates of Attentional Orienting and Sound Localization Performance: A Diffusion Model Approach

2020 ◽  
Vol 32 (5) ◽  
pp. 945-962
Author(s):  
Laura-Isabelle Klatt ◽  
Daniel Schneider ◽  
Anna-Lena Schubert ◽  
Christina Hanenberg ◽  
Jörg Lewald ◽  
...  
Keyword(s):  
Diffusion Model ◽  
Sound Localization ◽  
Drift Rate ◽  
Free Field ◽  
Decision Time ◽  
Model Parameters ◽  
Alpha Power ◽  
Model Framework ◽  
Performance Accuracy ◽  
Localization Performance

Understanding the contribution of cognitive processes and their underlying neurophysiological signals to behavioral phenomena has been a key objective in recent neuroscience research. Using a diffusion model framework, we investigated to what extent well-established correlates of spatial attention in the electroencephalogram contribute to behavioral performance in an auditory free-field sound localization task. Younger and older participants were instructed to indicate the horizontal position of a predefined target among three simultaneously presented distractors. The central question of interest was whether posterior alpha lateralization and amplitudes of the anterior contralateral N2 subcomponent (N2ac) predict sound localization performance (accuracy, mean RT) and/or diffusion model parameters (drift rate, boundary separation, non-decision time). Two age groups were compared to explore whether, in older adults (who struggle with multispeaker environments), the brain–behavior relationship would differ from younger adults. Regression analyses revealed that N2ac amplitudes predicted drift rate and accuracy, whereas alpha lateralization was not related to behavioral or diffusion modeling parameters. This was true irrespective of age. The results indicate that a more efficient attentional filtering and selection of information within an auditory scene, reflected by increased N2ac amplitudes, was associated with a higher speed of information uptake (drift rate) and better localization performance (accuracy), while the underlying response criteria (threshold separation), mean RTs, and non-decisional processes remained unaffected. The lack of a behavioral correlate of poststimulus alpha power lateralization constrasts with the well-established notion that prestimulus alpha power reflects a functionally relevant attentional mechanism. This highlights the importance of distinguishing anticipatory from poststimulus alpha power modulations.

scholarly journals A spurious correlation between difference scores in evidence accumulation model parameters

2021 ◽  
Author(s):  
James A. Grange ◽  
Stefanie Schuch
Keyword(s):  
Negative Correlation ◽  
Drift Rate ◽  
Response Selection ◽  
Selection Process ◽  
Decision Time ◽  
Model Parameters ◽  
Experimental Conditions ◽  
Difference Scores ◽  
Evidence Accumulation ◽  
Accumulation Model

Evidence-accumulation models are a useful tool for investigating the cognitive processes that give rise to behavioural data patterns in reaction times (RTs) and error rates. In their simplest form, evidence-accumulation models include three parameters: The average rate of evidence accumulation over time (drift rate) and the amount of evidence that needs to be accumulated before a response becomes selected (boundary) both characterise the response-selection process; a third parameter summarises all processes before and after the response-selection process (non-decision time). Researchers often compute experimental effects as simple difference scores between two within-subject conditions and such difference scores can also be computed on model parameters. In the present paper, we report spurious correlations between such model parameter difference scores, both in empirical data and in computer simulations. The most pronounced spurious effect is a negative correlation between boundary difference and non-decision difference, which amounts to r = –.70 or larger. In the simulations, we only observed this spurious negative correlation when either (a) there was no true difference in model parameters between simulated experimental conditions, or (b) only drift rate was manipulated between simulated experimental conditions; when a true difference existed in boundary separation, non-decision time, or all three main parameters, the correlation disappeared. We suggest that care should be taken when using evidence-accumulation model difference scores for correlational approaches, because the parameter difference scores can correlate in the absence of any true inter-individual differences at the population level.

scholarly journals An EZ-Diffusion Model Analysis of Attentional Ability in Patients With Retinal Pigmentosa

2021 ◽  
Vol 14 ◽  
Author(s):  
Yan-Lin Luo ◽  
Yuan-Ying Wang ◽  
Su-Fang Zhu ◽  
Li Zhao ◽  
Yan-Ling Yin ◽  
...  
Keyword(s):  
Visual Field ◽  
Diffusion Model ◽  
Drift Rate ◽  
Visual Field Loss ◽  
Decision Time ◽  
Spatial Processing ◽  
Control Group ◽  
Attentional Orienting ◽  
Large Stimulus ◽  
Switching Task

Retinitis pigmentosa (RP) is characterized by visual acuity decrease and visual field loss. However, the impact of visual field loss on the cognitive performance of RP patients remains unknown. In the present study, in order to understand whether and how RP affects spatial processing and attentional function, one spatial processing task and three attentional tasks were conducted on RP patients and healthy controls. In addition, an EZ-diffusion model was performed for further data analysis with four parameters, mean decision time, non-decision time, drift rate, and boundary separation. It was found that in the spatial processing task, compared with the control group, the RP group exhibited a slower response speed in large and medium visual eccentricities, and slower drift rate for the large stimulus, which is strongly verified by the significant linear correlation between the visual field eccentricity with both reaction time (p = 0.047) and non-decision time (p = 0.043) in RP patients. In the attentional orienting task and the attentional switching task, RP exerted a reduction of speed and an increase of non-decision time on every condition, with a decrease of drift rate in the orienting task and boundary separation in the switching task. In addition, the switching cost for large stimulus was observed in the control group but not in the RP group. The stop-signal task demonstrated similar inhibition function between the two groups. These findings implied that RP exerted the impairment of spatial cognition correlated with the visual field eccentricity, mainly in the peripheral visual field. Moreover, specific to the peripheral visual field, RP patients had deficits in the attentional orienting and flexibility but not in the attentional inhibition.

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

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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