The Dual Mechanisms of Cognitive Control Project

2021 ◽  
pp. 1-26
Author(s):  
Todd S. Braver ◽  
Alexander Kizhner ◽  
Rongxiang Tang ◽  
Michael C. Freund ◽  
Joset A. Etzel
Keyword(s):  
Individual Differences ◽  
Cognitive Control ◽  
Monozygotic Twin ◽  
Reactive Control ◽  
Full Data ◽  
Data Set ◽  
Project Completion ◽  
Prefrontal Function ◽  
Human Connectome Project ◽  
Spatio Temporal

Abstract We describe an ambitious ongoing study that has been strongly influenced and inspired by Don Stuss's career-long efforts to identify key cognitive processes that characterize executive control, investigate potential unifying dimensions that define prefrontal function, and carefully attend to individual differences. The Dual Mechanisms of Cognitive Control project tests a theoretical framework positing two key control dimensions: proactive and reactive. The framework's central tenets are that proactive and reactive control modes reflect domain-general dimensions of individual variation, with distinctive neural signatures, involving the lateral pFC as a central node within associated brain networks (e.g., fronto-parietal, cingulo-opercular). In the Dual Mechanisms of Cognitive Control project, each participant is scanned while performing theoretically targeted variants of multiple well-established cognitive control tasks (Stroop, cued task-switching, AX-CPT, Sternberg working memory) in three separate imaging sessions, that each encourages utilization of different control modes plus also completes an extensive out-of-scanner individual differences battery. Additional key features of the project include a high spatio-temporal resolution (multiband) acquisition protocol and a sample that includes a substantial subset of monozygotic twin pairs and participants recruited from the Human Connectome Project. Although data collection is still continuing (target n = 200), we provide an overview of the study design and protocol, along with initial results (n = 80) revealing evidence of a domain-general neural signature of cognitive control and its modulation under reactive conditions. Aligned with Don Stuss's legacy of scientific community building, a partial data set has been publicly released, with the full data set released at project completion, so it can serve as a valuable resource.

scholarly journals The Dual Mechanisms of Cognitive Control (DMCC) Project

2020 ◽  
Author(s):  
Todd S. Braver ◽  
Alexander Kizhner ◽  
Rongxiang Tang ◽  
Michael C. Freund ◽  
Joset A. Etzel
Keyword(s):  
Cognitive Control ◽  
Empirical Test ◽  
Community Building ◽  
Monozygotic Twin ◽  
Reactive Control ◽  
Project Completion ◽  
Human Connectome Project ◽  
Neural Signature ◽  
Spatio Temporal ◽  
Initial Results

AbstractThe Dual Mechanisms of Cognitive Control (DMCC) project provides an ambitious and rigorous empirical test of a theoretical framework that posits two key cognitive control modes: proactive and reactive. The framework’s central tenets are that proactive and reactive control reflect domain-general dimensions of individual variation, with distinctive neural signatures, involving lateral prefrontal cortex (PFC) in interactions with other brain networks and circuits (e.g., frontoparietal, cingulo-opercular). In the DMCC project, each participant is scanned while performing theoretically-targeted variants of multiple well-established cognitive control tasks (Stroop, Cued Task-Switching, AX-CPT, Sternberg Working Memory) in three separate imaging sessions, that each encourage utilization of different control modes, plus also completes an extensive out-of-scanner individual differences battery. Additional key features of the project include a high spatio-temporal resolution (multiband) acquisition protocol, and a sample that includes a substantial subset of monozygotic twin pairs and participants recruited from the Human Connectome Project. Although data collection is still continuing (target N=200), we provide an overview of the study design and protocol, planned analytic approaches and methodological development, along with initial results (N=80) revealing novel evidence of a domain-general neural signature of reactive control. In the interests of scientific community building, the dataset will be made public at project completion, so it can serve as a valuable resource.

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 The Dual Mechanisms of Cognitive Control (DMCC) project: Validation of an on-line behavioral task battery

2021 ◽  
Author(s):  
Rongxiang Tang ◽  
Julie Bugg ◽  
Jean-Paul Snijder ◽  
Andrew R. A. Conway ◽  
Todd Samuel Braver
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Cognitive Control ◽  
Selective Attention ◽  
Crucial Role ◽  
Proactive Control ◽  
Behavioral Task ◽  
Reactive Control ◽  
Context Processing ◽  
On Line

Cognitive control serves a crucial role in human higher mental functions. The Dual Mechanisms of Control (DMC) account provides a unifying theoretical framework that decomposes cognitive control into two qualitatively distinct mechanisms – proactive control and reactive control. While prior behavioral and neuroimaging work has demonstrated the validity of individual tasks in isolating these two mechanisms of control, there has not been a comprehensive, theoretically-guided task battery specifically designed to tap into proactive and reactive control across different domains of cognition. To address this critical limitation and provide useful methodological tools for future investigations, the Dual Mechanisms of Cognitive Control (DMCC) task battery was developed to probe these two control modes, as well as their intra-individual and inter-individual differences, across four prototypical domains of cognition: selective attention, context processing, multi-tasking, and working memory. We present this task battery, along with detailed descriptions of the experimental manipulations used to encourage shifts to proactive or reactive control in each of the four task domains. We rigorously evaluate the group effects of these manipulations in primary indices of proactive and reactive control, establishing the validity of the DMCC task battery in providing dissociable yet convergent measures of the two cognitive control modes.

scholarly journals Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals

2020 ◽  
Vol 32 (2) ◽  
pp. 241-255 ◽  
Author(s):  
Emily W. Avery ◽  
Kwangsun Yoo ◽  
Monica D. Rosenberg ◽  
Abigail S. Greene ◽  
Siyuan Gao ◽  
...  
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Functional Connectivity ◽  
Sustained Attention ◽  
Fluid Intelligence ◽  
Memory Performance ◽  
Data Set ◽  
Human Connectome Project ◽  
Connectivity Patterns ◽  
Intelligence Models

Individual differences in working memory relate to performance differences in general cognitive ability. The neural bases of such individual differences, however, remain poorly understood. Here, using a data-driven technique known as connectome-based predictive modeling, we built models to predict individual working memory performance from whole-brain functional connectivity patterns. Using n-back or rest data from the Human Connectome Project, connectome-based predictive models significantly predicted novel individuals' 2-back accuracy. Model predictions also correlated with measures of fluid intelligence and, with less strength, sustained attention. Separate fluid intelligence models predicted working memory score, as did sustained attention models, again with less strength. Anatomical feature analysis revealed significant overlap between working memory and fluid intelligence models, particularly in utilization of prefrontal and parietal regions, and less overlap in predictive features between working memory and sustained attention models. Furthermore, showing the generality of these models, the working memory model developed from Human Connectome Project data generalized to predict memory in an independent data set of 157 older adults (mean age = 69 years; 48 healthy, 54 amnestic mild cognitive impairment, 55 Alzheimer disease). The present results demonstrate that distributed functional connectivity patterns predict individual variation in working memory capability across the adult life span, correlating with constructs including fluid intelligence and sustained attention.

Processing linguistic variation through dual mechanisms of cognitive control

2016 ◽  
Vol 2 (s1) ◽  
Author(s):  
Grant M. Berry
Keyword(s):  
Individual Differences ◽  
Cognitive Control ◽  
Language Processing ◽  
Language Production ◽  
Linguistic Variation ◽  
Connectionist Model ◽  
Control Mechanisms ◽  
Reactive Control ◽  
Processing Strategies ◽  
Control Hypothesis

AbstractWhile rarely difficult for the average speaker/listener, the ubiquity of variation at all levels of linguistic production is a challenge for modern psycholinguistic models of language processing. Variation is perhaps most extreme at the levels of phonetics and phonology, but many models of language processing all but eschew these levels altogether. The current paper posits that cognitive control mechanisms, when divided into proactive and reactive control via a dual mechanisms framework may effectively describe the strategies individuals use to process linguistic variation and, when incorporated into language processing models, can generate novel, testable predictions regarding the origin and propagation of individual differences. By means of example, I illustrate how dual mechanisms of control could be incorporated into a connectionist model of language production. I then describe how dual mechanisms of cognitive control might be relevant for the Adaptive Control Hypothesis and how individual differences in processing strategies may modulate participation in language changes-in-progress.

Neuroimaging of Individual Differences: A Latent Variable Modeling Perspective

2018 ◽  
Author(s):  
Shelly Renee Cooper ◽  
Joshua James Jackson ◽  
Deanna Barch ◽  
Todd Samuel Braver
Keyword(s):  
Individual Differences ◽  
Individual Difference ◽  
Latent Variable ◽  
Structural Equation ◽  
Behavioral Measure ◽  
Equation Modeling ◽  
Latent Variable Modeling ◽  
Psychometric Theory ◽  
Human Connectome Project ◽  
Neuroimaging Data

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

Individual Uniqueness in the Neonatal Functional Connectome

2021 ◽  
Author(s):  
Qiushi Wang ◽  
Yuehua Xu ◽  
Tengda Zhao ◽  
Zhilei Xu ◽  
Yong He ◽  
...  
Keyword(s):  
Individual Differences ◽  
Individual Identification ◽  
Imaging Data ◽  
Functional Connectome ◽  
Middle Range ◽  
Human Connectome Project ◽  
The Individual ◽  
And Behavior ◽  
Identification Analysis ◽  
The Brain

Abstract The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0–30 mm) and middle-range (30–60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

scholarly journals A data acquisition setup for data driven acoustic design

2021 ◽  
pp. 1351010X2098690
Author(s):  
Romana Rust ◽  
Achilleas Xydis ◽  
Kurt Heutschi ◽  
Nathanael Perraudin ◽  
Gonzalo Casas ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Interdisciplinary Approach ◽  
Computational Design ◽  
Surface Structures ◽  
Future Research ◽  
Data Set ◽  
Process Measures ◽  
First Results ◽  
Spatio Temporal ◽  
Automated Data Acquisition

In this paper, we present a novel interdisciplinary approach to study the relationship between diffusive surface structures and their acoustic performance. Using computational design, surface structures are iteratively generated and 3D printed at 1:10 model scale. They originate from different fabrication typologies and are designed to have acoustic diffusion and absorption effects. An automated robotic process measures the impulse responses of these surfaces by positioning a microphone and a speaker at multiple locations. The collected data serves two purposes: first, as an exploratory catalogue of different spatio-temporal-acoustic scenarios and second, as data set for predicting the acoustic response of digitally designed surface geometries using machine learning. In this paper, we present the automated data acquisition setup, the data processing and the computational generation of diffusive surface structures. We describe first results of comparative studies of measured surface panels and conclude with steps of future research.

scholarly journals Back in Time for Breakfast: An Analysis of the Changing Breakfast Cereal Aisle

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 489
Author(s):  
Emilie Croisier ◽  
Jaimee Hughes ◽  
Stephanie Duncombe ◽  
Sara Grafenauer
Keyword(s):  
Dietary Fibre ◽  
Saturated Fat ◽  
Health Claims ◽  
Whole Grain ◽  
Breakfast Cereal ◽  
Full Data ◽  
Data Set ◽  
Time Points ◽  
Total Fat ◽  
Over Time

Breakfast cereal improves overall diet quality yet is under constant scrutiny with assertions that the category has not improved over time. This study aimed to comprehensively analyse the category of breakfast cereals, the nutritional values, and health claims across eight distinct sub-categories at four time points (2013, 2015, 2018, and 2020). An audit of products from four major supermarkets in metropolitan Sydney (Aldi, Coles, IGA, and Woolworths) collected ingredient lists, nutrition information, claims and Health Star Rating (HSR) for biscuits and bites; brans; bubbles, puffs, and flakes; granola and clusters; hot cereal flavoured; hot cereal plain; muesli; breakfast biscuits. The median (IQR) were calculated for energy, protein, fat, saturated fat, carbohydrate, sugars, dietary fibre, and sodium for comparisons over time points by nutrient. Data from 2013 was compared with 2020 (by sub-category and then for a sub-section of common products available at each time point). Product numbers between 2013 (n = 283) and 2020 (n = 543) almost doubled, led by granola and clusters. Whole grain cereals ≥ 8 g/serve made up 67% of products (↑114%). While there were positive changes in nutrient composition over time within the full data set, the most notable changes were in the nutrition composition of cereals marketed as the same product in both years (n = 134); with decreases in mean carbohydrate (2%), sugar (10%) and sodium (16%) (p < 0.000), while protein and total fat increased significantly (p = 0.036; p = 0.021). Claims regarding Dietary Fibre and Whole Grain doubled since 2013. Analysis of sub-categories of breakfast cereal assisted in identifying some changes over time, but products common to both timeframes provided a clearer analysis of change within the breakfast category, following introduction of HSR. Whole grain products were lower in the two target nutrients, sodium and sugars, and well-chosen products represent a better choice within this category.

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