scholarly journals Dual Mechanisms of Cognitive Control: A Hierarchical Bayesian Approach to Test-Retest Reliability

2021 ◽  
Author(s):  
◽  
Jean-Paul Snijder
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Control ◽  
Attentional Control ◽  
Stroop Effect ◽  
Hierarchical Bayesian ◽  
Reasoning Problem ◽  
Wide Range ◽  
Test Retest Reliability ◽  
Measurement Practices

Cognitive control, also known as attentional control or executive function, is a set of fundamental processes that are utilized in a wide range of cognitive functioning: including working memory, reasoning, problem solving, and decision making. Currently, no existing theory of cognitive control unifies experimental and individual differences approaches. Some even argue that cognitive control as a psychometric construct does not exist at all. These disparities may exist in part because individual differences research in cognitive control utilizes tasks optimized for experimental effects (i.e., Stroop effect). As a result, many cognitive control tasks do not have reliable individual differences despite robust experimental effects (Hedge, Powell, & Sumner, 2018). In the current study, we examine the efficacy of a new task battery based on the Dual Mechanisms of Cognitive Control theory (DMCC; Braver, 2012) to provide reliable estimates of individual differences in cognitive control. With two sets of analyses, the first traditional (e.g., split-half, ICC, and rho), and the second hierarchical Bayesian, we provide evidence that (1) reliable individual differences can be extracted from experimental tasks, and (2) weak correlations between tasks of cognitive control are not solely caused by the attenuation of unreliable estimates. The implications of our findings suggest that it is unlikely that poor measurement practices are the cause of the weak between-task correlations in cognitive control, and that a psychometric construct of cognitive control should be reconsidered

Individual Differences in the Neural Dynamics of Response Inhibition

2019 ◽  
Vol 31 (12) ◽  
pp. 1976-1996 ◽  
Author(s):  
M. Fiona Molloy ◽  
Giwon Bahg ◽  
Zhong-Lin Lu ◽  
Brandon M. Turner
Keyword(s):  
Individual Differences ◽  
Cognitive Control ◽  
Response Inhibition ◽  
Bayesian Models ◽  
Accurate Estimation ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Models ◽  
Data Set ◽  
Individual Level ◽  
Aggregate Information

Response inhibition is a widely studied aspect of cognitive control that is particularly interesting because of its applications to clinical populations. Although individual differences are integral to cognitive control, so too is our ability to aggregate information across a group of individuals, so that we can powerfully generalize and characterize the group's behavior. Hence, an examination of response inhibition would ideally involve an accurate estimation of both group- and individual-level effects. Hierarchical Bayesian analyses account for individual differences by simultaneously estimating group and individual factors and compensate for sparse data by pooling information across participants. Hierarchical Bayesian models are thus an ideal tool for studying response inhibition, especially when analyzing neural data. We construct hierarchical Bayesian models of the fMRI neural time series, models assuming hierarchies across conditions, participants, and ROIs. Here, we demonstrate the advantages of our models over a conventional generalized linear model in accurately separating signal from noise. We then apply our models to go/no-go and stop signal data from 11 participants. We find strong evidence for individual differences in neural responses to going, not going, and stopping and in functional connectivity across the two tasks and demonstrate how hierarchical Bayesian models can effectively compensate for these individual differences while providing group-level summarizations. Finally, we validated the reliability of our findings using a larger go/no-go data set consisting of 179 participants. In conclusion, hierarchical Bayesian models not only account for individual differences but allow us to better understand the cognitive dynamics of response inhibition.

scholarly journals Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package

2017 ◽  
Vol 1 ◽  
pp. 24-57 ◽  
Author(s):  
Woo-Young Ahn ◽  
Nathaniel Haines ◽  
Lei Zhang
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Computational Modeling ◽  
Bayesian Modeling ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Modeling ◽  
Model Based ◽  
Modeling Approaches ◽  
Wide Range ◽  
User Friendly

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes, as exemplified by model-based analysis of data from functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, researchers often find the approach too technical and have difficulty adopting it for their research. Thus, a critical need remains to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling of an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, in which both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, the package is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons, each with a single line of coding. Users can also extract the trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational-modeling approaches to investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, we expect that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within different populations.

scholarly journals Catecholaminergic modulation of the avoidance of cognitive control

2017 ◽  
Author(s):  
Monja I. Froböse ◽  
Jennifer C. Swart ◽  
Jennifer L. Cook ◽  
Dirk E.M. Geurts ◽  
Hanneke E.M. den Ouden ◽  
...  
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Control ◽  
Task Performance ◽  
Task Switching ◽  
Control Process ◽  
Healthy Adults ◽  
Large Sample ◽  
Trait Impulsivity ◽  
Undermining Effect

AbstractThe catecholamines have long been associated with cognitive control and value-based decision-making. More recently, we proposed that the catecholamines might modulate value-based decision-making about whether or not to engage in cognitive control. We test this hypothesis by assessing effects of a catecholamine challenge in a large sample of young, healthy adults (n = 100) on the avoidance of a cognitively demanding control process: task switching. Prolonging catecholamine transmission by blocking reuptake with methylphenidate altered the avoidance, but not the execution of cognitive control. Crucially, these effects could be isolated by taking into account individual differences in trait impulsivity, so that participants with higher trait impulsivity became more avoidant of cognitive control, despite faster task performance. One implication of these findings is that performance-enhancing effects of methylphenidate may be accompanied by an undermining effect on the willingness to exert cognitive control. Taken together, these findings integrate hitherto segregated literatures on catecholamines’ roles in value-based learning/choice and cognitive control.

Executive functions at work: The impact of Individual Differences in Cognitive Control on Managerial Decision-Making

2009 ◽  
Author(s):  
Daniella Laureiro Martinez ◽  
Stefano Brusoni ◽  
Nicola Canessa ◽  
Stefano Cappa ◽  
Maurizio Zollo ◽  
...  
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Control ◽  
Executive Functions ◽  
Managerial Decision ◽  
Managerial Decision Making ◽  
The Impact

scholarly journals Cognitive Control and Individual Differences in Economic Ultimatum Decision-Making

PLoS ONE ◽  
2011 ◽  
Vol 6 (11) ◽  
pp. e27107 ◽  
Author(s):  
Wim De Neys ◽  
Nikolay Novitskiy ◽  
Leen Geeraerts ◽  
Jennifer Ramautar ◽  
Johan Wagemans
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Control

scholarly journals Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner

2021 ◽  
Author(s):  
Ying-Qiu Zheng ◽  
Seyedeh-Rezvan Farahibozorg ◽  
Weikang Gong ◽  
Hossein Rafipoor ◽  
Saad Jbabdi ◽  
...  
Keyword(s):  
Individual Differences ◽  
Resting State ◽  
Predictive Accuracy ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Human Connectome Project ◽  
Wide Range ◽  
Dual Regression ◽  
Project Data ◽  
Test Retest Reliability

Modelling and predicting individual differences in task-evoked FMRI activity can have a wide range of applications from basic to clinical neuroscience. It has been shown that models based on resting-state activity can have high predictive accuracy. Here we propose several improvements to such models. Using a sparse ensemble leaner, we show that (i) features extracted using Stochastic Probabilistic Functional Modes (sPROFUMO) outperform the previously proposed dual-regression approach, (ii) that the shape and overall intensity of individualised task activations can be modelled separately and explicitly, (iii) training the model on predicting residual differences in brain activity further boosts individualised predictions. These results hold for both surface-based analyses of the Human Connectome Project data as well as volumetric analyses of UK-biobank data. Overall, our model achieves state of the art prediction accuracy on par with the test-retest reliability of tfMRI scans, suggesting that it has potential to supplement traditional task localisers.

Elaborating the role of reflection and individual differences in the study of folk-economic beliefs

2018 ◽  
Vol 41 ◽  
Author(s):  
Kevin Arceneaux
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Processes ◽  
Evolutionary History ◽  
Behavioral Responses ◽  
History Of ◽  
Economic Beliefs

AbstractIntuitions guide decision-making, and looking to the evolutionary history of humans illuminates why some behavioral responses are more intuitive than others. Yet a place remains for cognitive processes to second-guess intuitive responses – that is, to be reflective – and individual differences abound in automatic, intuitive processing as well.

Group Experiences and Individual Differences in Stuttering

2019 ◽  
Vol 62 (12) ◽  
pp. 4335-4350 ◽  
Author(s):  
Seth E. Tichenor ◽  
J. Scott Yaruss
Keyword(s):  
Individual Differences ◽  
Support Groups ◽  
Support Group ◽  
Affective States ◽  
Life Outcomes ◽  
Self Help ◽  
Wide Range ◽  
Group Experiences ◽  
History Of ◽  
Adults Who Stutter

Purpose This study explored group experiences and individual differences in the behaviors, thoughts, and feelings perceived by adults who stutter. Respondents' goals when speaking and prior participation in self-help/support groups were used to predict individual differences in reported behaviors, thoughts, and feelings. Method In this study, 502 adults who stutter completed a survey examining their behaviors, thoughts, and feelings in and around moments of stuttering. Data were analyzed to determine distributions of group and individual experiences. Results Speakers reported experiencing a wide range of both overt behaviors (e.g., repetitions) and covert behaviors (e.g., remaining silent, choosing not to speak). Having the goal of not stuttering when speaking was significantly associated with more covert behaviors and more negative cognitive and affective states, whereas a history of self-help/support group participation was significantly associated with a decreased probability of these behaviors and states. Conclusion Data from this survey suggest that participating in self-help/support groups and having a goal of communicating freely (as opposed to trying not to stutter) are associated with less negative life outcomes due to stuttering. Results further indicate that the behaviors, thoughts, and experiences most commonly reported by speakers may not be those that are most readily observed by listeners.

Measures of RSA Suppression, Attentional Control, and Negative Affect Predict Self-Ratings of Executive Functions

2011 ◽  
Vol 25 (4) ◽  
pp. 164-173 ◽  
Author(s):  
Brian Healy ◽  
Aaron Treadwell ◽  
Mandy Reagan
Keyword(s):  
College Students ◽  
Heart Rate ◽  
Regression Analysis ◽  
Individual Differences ◽  
Negative Affect ◽  
Respiratory Sinus Arrhythmia ◽  
Attentional Control ◽  
Executive Processes ◽  
Sinus Arrhythmia ◽  
Low Levels

The current study was an attempt to determine the degree to which the suppression of respiratory sinus arrhythmia (RSA) and attentional control were influential in the ability to engage various executive processes under high and low levels of negative affect. Ninety-four college students completed the Stroop Test while heart rate was being recorded. Estimates of the suppression of RSA were calculated from each participant in response to this test. The participants then completed self-ratings of attentional control, negative affect, and executive functioning. Regression analysis indicated that individual differences in estimates of the suppression of RSA, and ratings of attentional control were associated with the ability to employ executive processes but only when self-ratings of negative affect were low. An increase in negative affect compromised the ability to employ these strategies in the majority of participants. The data also suggest that high attentional control in conjunction with attenuated estimates of RSA suppression may increase the ability to use executive processes as negative affect increases.

A Bird in the Hand Isn’t Good for Long

2018 ◽  
Vol 65 (1) ◽  
pp. 23-31 ◽  
Author(s):  
Stefan Scherbaum ◽  
Simon Frisch ◽  
Maja Dshemuchadse
Keyword(s):  
Decision Making ◽  
Cognitive Control ◽  
The Other ◽  
Decision Task ◽  
Short Term ◽  
Additional Time ◽  
Control Processes ◽  
Folk Wisdom ◽  
Study Participants ◽  
Insight Into

Abstract. Folk wisdom tells us that additional time to make a decision helps us to refrain from the first impulse to take the bird in the hand. However, the question why the time to decide plays an important role is still unanswered. Here we distinguish two explanations, one based on a bias in value accumulation that has to be overcome with time, the other based on cognitive control processes that need time to set in. In an intertemporal decision task, we use mouse tracking to study participants’ responses to options’ values and delays which were presented sequentially. We find that the information about options’ delays does indeed lead to an immediate bias that is controlled afterwards, matching the prediction of control processes needed to counter initial impulses. Hence, by using a dynamic measure, we provide insight into the processes underlying short-term oriented choices in intertemporal decision making.

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