Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control.

Understanding individual differences in cognitive control is a central goal in psychology and neuroscience. Reliably measuring these differences, however, has proven extremely challenging, at least when using standard measures in cognitive neuroscience such as response times or task-based fMRI activity. While prior work has pinpointed the source of the issue - the vast amount of cross-trial variability within these measures - no study has rigorously evaluated potential solutions. Here, we do so with one potential way forward: an analytic framework that combines hierarchical Bayesian modeling with multivariate decoding of trial-level fMRI data. Using this framework and longitudinal data from the Dual Mechanisms of Cognitive Control project, we estimated individuals' neural responses associated with cognitive control within a color-word Stroop task, then assessed the reliability of these individuals' responses across a time interval of several months. We show that in many prefrontal and parietal brain regions, test-retest reliability was near maximal, and that only hierarchical models were able to reveal this state of affairs. Further, when compared to traditional univariate contrasts, multivariate decoding enabled individual-level correlations to be estimated with significantly greater precision. We specifically link these improvements in precision to the optimized suppression of cross-trial variability in decoding. Together, these findings not only indicate that cognitive control-related neural responses individuate people in a highly stable manner across time, but also suggest that integrating hierarchical and multivariate models provides a powerful approach for investigating individual differences in cognitive control, one that can effectively address the issue of high-variability measures.

Parsing state mindfulness effects on neurobehavioral markers of cognitive control: A within-subject comparison of focused attention and open monitoring.

Over the past two decades, scientific interest in understanding the relationship between mindfulness and cognition has accelerated. However, despite considerable investigative efforts, pervasive methodological inconsistencies within the literature preclude a thorough understanding of whether or how mindfulness influences core cognitive functions. The purpose of the current study is to provide an initial "proof-of-concept" demonstration of a new research strategy and methodological approach designed to address previous limitations. Specifically, we implemented a novel fully within-subject state induction protocol to elucidate the neurobehavioral influence of discrete mindfulness states-focused attention (FA) and open monitoring (OM), compared against an active control-on well-established behavioral and ERP indices of executive attention and error monitoring assessed during the Eriksen flanker task. Bayesian mixed modeling was used to test preregistered hypotheses pertaining to FA and OM effects on flanker interference, the stimulus-locked P3, and the response-locked ERN and Pe. Results yielded strong but unexpected evidence that OM selectively produced a more cautious and intentional response style, characterized by higher accuracy, slower RTs, and reduced P3 amplitude. Follow-up exploratory analyses revealed that trait mindfulness moderated the influence of OM, such that individuals with greater trait mindfulness responded more cautiously and exhibited higher trial accuracy and smaller P3s. Neither FA nor OM modulated the ERN or Pe. Taken together, our findings support the promise of our approach, demonstrating that theoretically distinct mindfulness states are functionally dissociable among mindfulness-naive participants and that interactive variability associated with different operational facets of mindfulness (i.e., state vs. trait) can be modeled directly.

Dynamical models reveal anatomically reliable attractor landscapes embedded in resting state brain networks.

Analyses of functional connectivity (FC) in resting-state brain networks (RSNs) have generated many insights into cognition. However, the mechanistic underpinnings of FC and RSNs are still not well-understood. It remains debated whether resting state activity is best characterized as noise-driven fluctuations around a single stable state, or instead, as a nonlinear dynamical system with nontrivial attractors embedded in the RSNs. Here, we provide evidence for the latter, by constructing whole-brain dynamical systems models from individual resting-state fMRI (rfMRI) recordings, using the Mesoscale Individualized NeuroDynamic (MINDy) platform. The MINDy models consist of hundreds of neural masses representing brain parcels, connected by fully trainable, individualized weights. We found that our models manifested a diverse taxonomy of nontrivial attractor landscapes including multiple equilibria and limit cycles. However, when projected into anatomical space, these attractors mapped onto a limited set of canonical RSNs, including the default mode network (DMN) and frontoparietal control network (FPN), which were reliable at the individual level. Further, by creating convex combinations of models, bifurcations were induced that recapitulated the full spectrum of dynamics found via fitting. These findings suggest that the resting brain traverses a diverse set of dynamics, which generates several distinct but anatomically overlapping attractor landscapes. Treating rfMRI as a unimodal stationary process (i.e., conventional FC) may miss critical attractor properties and structure within the resting brain. Instead, these may be better captured through neural dynamical modeling and analytic approaches. The results provide new insights into the generative mechanisms and intrinsic spatiotemporal organization of brain networks.

On the psychometric evaluation of cognitive control tasks: An Investigation with the Dual Mechanisms of Cognitive Control (DMCC) battery.

The domain of cognitive control has been a major focus of experimental, neuroscience, and individual differences research. Currently, however, no theory of cognitive control successfully unifies both experimental and individual differences findings. Some perspectives deny that there even exists a unified psychometric cognitive control construct to be measured at all. These shortcomings of the current literature may reflect the fact that current cognitive control paradigms are optimized for the detection of within-subject experimental effects rather than individual differences. In the current study, we examine the psychometric properties of the Dual Mechanisms of Cognitive Control (DMCC) task battery, which was designed in accordance with a theoretical framework that postulates common sources of within-subject and individual differences variation. We evaluated both internal consistency and test-retest reliability, and for the latter, utilized both classical test theory measures (i.e., split-half methods, intraclass correlation) and newer hierarchical Bayesian estimation of generative models. Although traditional psychometric measures suggested poor reliability, the hierarchical Bayesian models indicated a different pattern, with good to excellent test-retest reliability in almost all tasks and conditions examined. Moreover, within-task, between-condition correlations were generally increased when using the Bayesian model-derived estimates, and these higher correlations appeared to be directly linked to the higher reliability of the measures. In contrast, between-task correlations remained low regardless of theoretical manipulations or estimation approach. Together, these findings highlight the advantages of Bayesian estimation methods, while also pointing to the important role of reliability in the search for a unified theory of cognitive control.

Precision data-driven modeling of cortical dynamics reveals idiosyncratic mechanisms underlying canonical oscillations.

Task-free brain activity affords unique insight into the functional structure of brain network dynamics and is a strong marker of individual differences. In this work, we present an algorithmic optimization framework that makes it possible to directly invert and parameterize brain-wide dynamical-systems models involving hundreds of interacting brain areas, from single-subject time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions. We extensively validate the models' performance in forecasting future brain activity and predicting individual variability in key M/EEG markers. Lastly, we demonstrate the power of our technique in resolving individual differences in the generation of alpha and beta-frequency oscillations. We characterize subjects based upon model attractor topology and a dynamical-systems mechanism by which these topologies generate individual variation in the expression of alpha vs. beta rhythms. We trace these phenomena back to global variation in excitation-inhibition balance, highlighting the explanatory power of our framework in generating mechanistic insights.

Increased cognitive effort costs in healthy aging and preclinical Alzheimer's disease.

Life-long engagement in cognitively demanding activities may mitigate against declines in cognitive ability observed in healthy or pathological aging. However, the "mental costs" associated with completing cognitive tasks also increase with age and may be partly attributed to increases in preclinical levels of Alzheimer's disease (AD) pathology, specifically amyloid. We test whether cognitive effort costs increase in a domain-general manner among older adults, and further, whether such age-related increases in cognitive effort costs are associated with working memory (WM) capacity or amyloid burden, a signature pathology of AD. In two experiments, we administered a behavioral measure of cognitive effort costs (cognitive effort discounting) to a sample of older adults recruited from online sources (Experiment 1) or from ongoing longitudinal studies of aging and dementia (Experiment 2). Experiment 1 compared age-related differences in cognitive effort costs across two domains, WM and speech comprehension. Experiment 2 compared cognitive effort costs between a group of participants who were rated positive for amyloid relative to those with no evidence of amyloid. Results showed age-related increases in cognitive effort costs were evident in both domains. Cost estimates were highly correlated between the WM and speech comprehension tasks but did not correlate with WM capacity. In addition, older adults who were amyloid positive had higher cognitive effort costs than those who were amyloid negative. Cognitive effort costs may index a domain-general trait that consistently increases in aging. Differences in cognitive effort costs associated with amyloid burden suggest a potential neurobiological mechanism for age-related differences. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Dual Mechanisms of Cognitive Control (DMCC) project: Validation of an online behavioural task battery.

Cognitive control serves a crucial role in human higher mental functions. The Dual Mechanisms of Control theoretical framework provides a unifying account that decomposes cognitive control into two qualitatively distinct mechanisms-proactive control and reactive control. Here, we describe the Dual Mechanisms of Cognitive Control (DMCC) task battery, which was developed to probe cognitive control modes in a theoretically targeted manner, along with detailed descriptions of the experimental manipulations used to encourage shifts to proactive or reactive mode in each of four prototypical domains of cognition: selective attention, context processing, multitasking, and working memory. We present results from this task battery, conducted from a large (N > 100), online sample that rigorously evaluates the group effects of these manipulations in primary indices of proactive and reactive control, establishing the validity of the battery in providing dissociable yet convergent measures of the two cognitive control modes. The DMCC battery may be a useful tool for the research community to examine cognitive control in a theoretically targeted manner across different individuals and groups.

The multi-angle extended three-dimensional activities (META) stimulus set: A tool for studying event cognition.

To study complex human activity and how it is perceived and remembered, it is valuable to have large-scale, well-characterized stimuli that are representative of such activity. We present the Multi-angle Extended Three-dimensional Activities (META) stimulus set, a structured and highly instrumented set of extended event sequences performed in naturalistic settings. Performances were captured with two color cameras and a Kinect v2 camera with color and depth sensors, allowing the extraction of three-dimensional skeletal joint positions. We tracked the positions and identities of objects for all chapters using a mixture of manual coding and an automated tracking pipeline, and hand-annotated the timings of high-level actions. We also performed an online experiment to collect normative event boundaries for all chapters at a coarse and fine grain of segmentation, which allowed us to quantify event durations and agreement across participants. We share these materials publicly to advance new discoveries in the study of complex naturalistic activity.

Cognitive Effort-Based Decision-Making Across Experimental and Daily Life Indices in Younger and Older Adults.

OBJECTIVES: The study investigated whether cognitive effort decision-making measured via a neuroeconomic paradigm that manipulated framing (gain vs. loss outcomes), could predict daily life engagement in mentally demanding activities in both younger and older adults. METHOD: Younger and older adult participants (N = 310) completed the Cognitive Effort Discounting paradigm (Cog-ED), under both gain and loss conditions, to provide an experimental index of cognitive effort costs for each participant in each framing condition. A subset of participants (N = 230) also completed a 7-day Ecological Momentary Assessment (EMA) protocol measuring engagement in mentally demanding daily life activities. RESULTS: In a large, online sample, we replicated a robust increase in cognitive effort costs among older, relative to younger, adults. Additionally, costs were found to be reduced in the loss relative to gain frame, although these effects were only reliable at high levels of task difficulty and were not moderated by age. Critically, participants who had lower effort costs in the gain frame tended to report engaging in more mentally demanding daily life activities, but the opposite pattern was observed in the loss frame. Further analyses demonstrated the specificity of reward-related cognitive motivation in predicting daily life mentally demanding activities. DISCUSSION: Together, these results suggest that cognitive effort costs, as measured through behavioral choice patterns in a neuroeconomic decision-making task, can be used to predict and explain engagement in mentally demanding activities during daily life among both older and younger adults.

Working memory capacity preferentially enhances implementation of proactive control.

Previous research has linked working memory capacity (WMC) with enhanced proactive control. However, it remains unclear the extent to which this relationship reflects the influence of WMC on the tendency to engage proactive control, or rather, the ability to implement it. The current study sought to clarify this ambiguity by leveraging the Dual Mechanisms of Cognitive Control (DMCC) version of the AX-CPT task, in which the mode of cognitive control is experimentally manipulated across distinct testing sessions. To adjudicate between competing hypotheses, Bayesian mixed modeling was used to conduct sequential analyses involving two separate data sets. Posterior parameter estimates obtained from the initial analysis were entered as informed priors during the replication analysis to evaluate the influence of new data on previous estimates. Results yielded strong evidence demonstrating that the influence of WMC on proactive control is most robust under experimentally controlled conditions, during which use of proactive control is standardized across participants via explicit training and instruction. Critically, the observed pattern of findings suggests that the relationship between WMC and proactive control may be better characterized as individual differences in the ability to implement proactive control, rather than a more generalized tendency to engage it. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An fMRI protocol for administering liquid incentives to human participants.

This protocol describes the materials and approaches for administering liquid incentives to human participants during fMRI scanning. We first describe preparation of the liquid solutions (e.g., neutral solution and saltwater) and liquid delivery setups. We then detail steps to connect the setups to the computer-controlled syringe pump in the MRI control room, followed by procedures for testing the syringe pump dispensing a liquid bolus during the task. Description of custom software and required adapters for implementing the liquid setup are included. For complete details on the use and execution of this protocol, please refer to Yee et al. (2021).

Delay of gratification dissociates cognitive control and valuation brain regions in healthy young adults.

Delay of gratification (DofG) refers to an inter-temporal choice phenomenon that is of great interest in many domains, including animal learning, cognitive development, economic decision-making, and executive control. Yet experimental tools for investigating DofG in human adults are almost non-existent, and as a consequence, very little is known regarding the brain basis of core DofG behaviors. Here, we utilize a novel DofG paradigm, adapted for use in neuroimaging contexts, to examine event-related changes in neural activity as healthy young adult participants made repeated choices to continue waiting for a delayed reward, rather than take an immediately available one of lesser value. On DofG trials, choose-to-wait events were associated with increased activation in fronto-parietal and cingulo-opercular regions associated with cognitive control. Activity in the right lateral prefrontal cortex (PFC) was also associated with individual variability in task performance and strategy. Fronto-parietal activity was clearly dissociable from that observed in ventromedial PFC, as this latter region exhibited a ramping-up pattern of activity during the waiting period prior to reward delivery. Ventromedial PFC ramping activity dynamics were further selective to DofG trials associated with increased future reward rate, consistent with the involvement of this region in subjective reward valuation that incorporates higher-order task structure. These results provide important initial validation of this experimental paradigm as a useful tool for investigating and isolating unique DofG neural mechanisms, which can now be utilized to study a wide-variety of populations and task factors.

The Dual Mechanisms of Cognitive Control dataset, a theoretically-guided within-subject task fMRI battery.

Cognitive control is a critical higher mental function, which is subject to considerable individual variation, and is impaired in a range of mental health disorders. We describe here the initial release of Dual Mechanisms of Cognitive Control (DMCC) project data, the DMCC55B dataset, with 55 healthy unrelated young adult participants. Each participant performed four well-established cognitive control tasks (AX-CPT, Cued Task-Switching, Sternberg Working Memory, and Stroop) while undergoing functional MRI scanning. The dataset includes a range of state and trait self-report questionnaires, as well as behavioural tasks assessing individual differences in cognitive ability. The DMCC project is on-going and features additional components (e.g., related participants, manipulations of cognitive control mode, resting state fMRI, longitudinal testing) that will be publicly released following study completion. This DMCC55B subset is released early with the aim of encouraging wider use and greater benefit to the scientific community. The DMCC55B dataset is suitable for benchmarking and methods exploration, as well as analyses of task performance and individual differences.

Domain-general cognitive motivation: Evidence from economic decision-making - Final Registered Report.

Stable individual differences in cognitive motivation (i.e., the tendency to engage in and enjoy effortful cognitive activities) have been documented with self-report measures, yet convergent support for a trait-level construct is still lacking. In the present study, we used an innovative decision-making paradigm (COG-ED) to quantify the costs of cognitive effort, a metric of cognitive motivation, across two distinct cognitive domains: working memory (an N-back task) and speech comprehension (understanding spoken sentences in background noise). We hypothesized that cognitive motivation operates similarly within individuals, regardless of domain. Specifically, in 104 adults aged 18-40 years, we tested whether individual differences in effort costs are stable across domains, even after controlling for other potential sources of shared individual variation. Conversely, we evaluated whether the costs of cognitive effort across domains may be better explained in terms of other relevant cognitive and personality-related constructs, such as working memory capacity or reward sensitivity. We confirmed a reliable association among effort costs in both domains, even when these other sources of individual variation, as well as task load, are statistically controlled. Taken together, these results add support for trait-level variation in cognitive motivation impacting effort-based decision making across multiple domains.

We need to be braver about the generalizability crisis.

We applaud the effort to draw attention to generalizability concerns in twenty-first-century psychological research. Yet we do not feel that a pessimistic perspective is warranted. We outline a continuum of available methodological tools and perspectives, including incremental steps and meta-analytic approaches that can be readily and easily deployed by researchers to advance generalizability claims in a forward-looking manner.

Incorporating ecological momentary assessment into multimethod investigations of cognitive aging: Promise and practical considerations.

Ecological momentary assessment (EMA) represents a promising approach to study cognitive aging. In contrast to laboratory-based studies, EMA involves the repeated sampling of experiences in daily life contexts, enabling investigators to gain access to dynamic processes (e.g., situational contexts, intraindividual variability) that are likely to strongly contribute to aging and age-related change across the adult life-span. As such, EMA approaches complement the prevailing research methods in the field of cognitive aging (e.g., laboratory-based paradigms, neuroimaging), while also providing the opportunity to replicate and extend findings from the laboratory in more naturalistic contexts. Following an overview of the methodological and conceptual strengths of EMA approaches in cognitive aging research, we discuss best practices for researchers interested in implementing EMA studies. A key goal is to highlight the tremendous potential for combining EMA methods with other laboratory-based approaches, in order to increase the robustness, replicability, and real-world implications of research findings in the field of cognitive aging. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Control-theoretic integration of stimulation and electrophysiology for cognitive enhancement.

Transcranial electrical stimulation (tES) technology and neuroimaging are increasingly coupled in basic and applied science. This synergy has enabled individualized tES therapy and facilitated causal inferences in functional neuroimaging. However, traditional tES paradigms have been stymied by relatively small changes in neural activity and high inter-subject variability in cognitive effects. In this perspective, we propose a tES framework to treat these issues which is grounded in dynamical systems and control theory. The proposed paradigm involves a tight coupling of tES and neuroimaging in which M/EEG is used to parameterize generative brain models as well as control tES delivery in a hybrid closed-loop fashion. We also present a novel quantitative framework for cognitive enhancement driven by a new computational objective: shaping how the brain reacts to potential "inputs" (e.g., task contexts) rather than enforcing a fixed pattern of brain activity.

Investigating mindfulness influences on cognitive function: On the promise and potential of converging research strategies.

Research investigating the effects and underlying mechanisms of mindfulness on cognitive functioning has accelerated exponentially over the past two decades. Despite the rapid growth of the literature and its influential role in garnering public interest in mindfulness, inconsistent methods in defining and measuring mindfulness have yielded variable findings, which contribute to the overall dearth of clear generalizable conclusions. The focus of this article is to address the lack of cohesion in the collective methodologies used in this domain by providing a new perspective grounded in classic cognitive and experimental psychology principles. We leverage the concept of converging operations to demonstrate how seemingly disparate research strategies can be integrated towards a more unified and systematic approach. An organizing taxonomic framework is described to provide useful structure in how mindfulness can be operationalized, measured, and investigated. We illustrate the rationale and core organizing principles of the framework through a selective review of studies on mindfulness and cognitive control. We then demonstrate the utility of the approach by showing how it can be applied to synthesize extant methodologies and guide the development of future research. Specific suggestions and examples pertaining to experimental design and statistical analysis are provided.

Aversive motivation and cognitive control.

Aversive motivation plays a prominent role in driving individuals to exert cognitive control. However, the complexity of behavioral responses attributed to aversive incentives creates significant challenges for developing a clear understanding of the neural mechanisms of this motivation-control interaction. We review the animal learning, systems neuroscience, and computational literatures to highlight the importance of experimental paradigms that incorporate both motivational context manipulations and mixed motivational components (e.g., bundling of appetitive and aversive incentives). Specifically, we postulate that to understand aversive incentive effects on cognitive control allocation, a critical contextual factor is whether such incentives are associated with negative reinforcement or punishment. We further illustrate how the inclusion of mixed motivational components in experimental paradigms enables increased precision in the measurement of aversive influences on cognitive control. A sharpened experimental and theoretical focus regarding the manipulation and assessment of distinct motivational dimensions promises to advance understanding of the neural, monoaminergic, and computational mechanisms that underlie the interaction of motivation and cognitive control.

Enhancing task fMRI preprocessing via individualized model-based filtering of intrinsic activity dynamics.

Brain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work, we describe a method to improve the estimation of task-evoked brain activity by first "filtering-out the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; Singh et al. 2020b) models built from individualized resting-state data to subtract the propagation of spontaneous activity from the task-fMRI signal (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, use of MINDy-based filtering increased the similarity of neural activation profiles and prediction accuracy of individual differences in behavior across tasks measuring the same construct (cognitive control). Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural effects.