The response time paradox in functional magnetic resonance imaging analyses.

Response times (RTs) are often the main signal of interest in cognitive psychology but are often ignored in functional MRI (fMRI) analyses. In fMRI analysis the intensity of the signal serves as a proxy for the intensity of local neuronal activity, but changes in either the intensity or the duration of neuronal activity can yield identical fMRI signals. Therefore, if RTs are ignored and pair with neuronal durations, fMRI results claiming intensity differences may be confounded by RTs. We show how ignoring RTs goes beyond this confound, where longer RTs are paired with larger activation estimates, to lesser-known issues where RTs become confounds in group-level analyses and, surprisingly, how the RT confound can induce other artificial group-level associations with variables that are not related to the condition contrast or RTs. We propose a new time-series model to address these issues and encourage increasing focus on what the widespread RT-based signal represents.

Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation.

We describe the following shared data from N=103 healthy adults who completed a broad set cognitive tasks, surveys, and neuroimaging measurements to examine the construct of self-regulation. The neuroimaging acquisition involved task-based fMRI, resting fMRI, and structural MRI. Each subject completed the following ten tasks in the scanner across two 90-minute scanning sessions: attention network test (ANT), cued task switching, Columbia card task, dot pattern expectancy (DPX), delay discounting, simple and motor selective stop signal, Stroop, a towers task, and a set of survey questions. Subjects also completed resting state scans. The dataset is shared openly through the OpenNeuro project, and the dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard.

Modelling human behaviour in cognitive tasks with latent dynamical systems.

Response time data collected from cognitive tasks are a cornerstone of psychology and neuroscience research, yet existing models of these data either make strong assumptions about the data-generating process or are limited to modelling single trials. We introduce task-DyVA, a deep learning framework in which expressive dynamical systems are trained to reproduce sequences of response times observed in data from individual human subjects. Models fitted to a large task-switching dataset captured subject-specific behavioural differences with high temporal precision, including task-switching costs. Through perturbation experiments and analyses of the models' latent dynamics, we find support for a rational account of switch costs in terms of a stability-flexibility trade-off. Thus, our framework can be used to discover interpretable cognitive theories that explain how the brain dynamically gives rise to behaviour.

A dual-task approach to inform the taxonomy of inhibition-related processes.

Response inhibition is key to controlled behavior and is commonly investigated with the stop-signal paradigm. The authors investigated how response inhibition is situated within a taxonomy of control processes by combining multiple forms of control within dual tasks. Response inhibition, as measured by stop-signal reaction time (SSRT), was impaired when combined with shape matching, but not the flanker task, and when combined with cued task switching, but not predictable task switching, suggesting that response inhibition may be weakly or variably impaired when combined with selective attention and set shifting demands, respectively. Response inhibition was also consistently impaired when combined with the N-back or directed forgetting tasks, putative measures of working memory. Impairments of response inhibition by other control demands appeared to be primarily driven by task context, as SSRT slowing was similar for trials where control demands were either high (e.g., task switch) or low (e.g., task stay). These results demonstrate that response inhibition processes are often impaired in the context of other control demands, even on trials where direct engagement of those other control processes is not required. This suggests a taxonomy of control in which response inhibition overlaps with related control processes, especially working memory. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Predicting brain activation maps for arbitrary tasks with cognitive encoding models.

A deep understanding of the neural architecture of mental function should enable the accurate prediction of a specific pattern of brain activity for any psychological task, based only on the cognitive functions known to be engaged by that task. Encoding models (EMs), which predict neural responses from known features (e.g., stimulus properties), have succeeded in circumscribed domains (e.g., visual neuroscience), but implementing domain-general EMs that predict brain-wide activity for arbitrary tasks has been limited mainly by availability of datasets that 1) sufficiently span a large space of psychological functions, and 2) are sufficiently annotated with such functions to allow robust EM specification. We examine the use of EMs based on a formal specification of psychological function, to predict cortical activation patterns across a broad range of tasks. We utilized the Multi-Domain Task Battery, a dataset in which 24 subjects completed 32 ten-minute fMRI scans, switching tasks every 35 s and engaging in 44 total conditions of diverse psychological manipulations. Conditions were annotated by a group of experts using the Cognitive Atlas ontology to identify putatively engaged functions, and region-wise cognitive EMs (CEMs) were fit, for individual subjects, on neocortical responses. We found that CEMs predicted cortical activation maps of held-out tasks with high accuracy, outperforming a permutation-based null model while approaching the noise ceiling of the data, without being driven solely by either cognitive or perceptual-motor features. Hierarchical clustering on the similarity structure of CEM generalization errors revealed relationships amongst psychological functions. Spatial distributions of feature importances systematically overlapped with large-scale resting-state functional networks (RSNs), supporting the hypothesis of functional specialization within RSNs while grounding their function in an interpretable data-driven manner. Our implementation and validation of CEMs provides a proof of principle for the utility of formal ontologies in cognitive neuroscience and motivates the use of CEMs in the further testing of cognitive theories.

Relating psychiatric symptoms and self-regulation during the COVID-19 crisis.

Disruptions of self-regulation are a hallmark of numerous psychiatric disorders. Here, we examine the relationship between transdiagnostic dimensions of psychopathology and changes in self-regulation in the early phase of the COVID-19 pandemic. We used a data-driven approach on a large number of cognitive tasks and self-reported surveys in training datasets. Then, we derived measures of self-regulation and psychiatric functioning in an independent population sample (N = 102) tested both before and after the onset of the COVID-19 pandemic, when the restrictions in place represented a threat to mental health and forced people to flexibly adjust to modifications of daily routines. We found independent relationships between transdiagnostic dimensions of psychopathology and longitudinal alterations in specific domains of self-regulation defined using a diffusion decision model. Compared to the period preceding the onset of the pandemic, a symptom dimension related to anxiety and depression was characterized by a more cautious behavior, indexed by the need to accumulate more evidence before making a decision. Instead, social withdrawal related to faster non-decision processes. Self-reported measures of self-regulation predicted variance in psychiatric symptoms both concurrently and prospectively, revealing the psychological dimensions relevant for separate transdiagnostic dimensions of psychiatry, but tasks did not. Taken together, our results are suggestive of potential cognitive vulnerabilities in the domain of self-regulation in people with underlying psychiatric difficulties in face of real-life stressors. More generally, they also suggest that the study of cognition needs to take into account the dynamic nature of real-world events as well as within-subject variability over time.

Momentary Influences on Self-Regulation in Two Populations With Health Risk Behaviors: Adults Who Smoke and Adults Who Are Overweight and Have Binge-Eating Disorder.

Introduction: Self-regulation has been implicated in health risk behaviors and is a target of many health behavior interventions. Despite most prior research focusing on self-regulation as an individual-level trait, we hypothesize that self-regulation is a time-varying mechanism of health and risk behavior that may be influenced by momentary contexts to a substantial degree. Because most health behaviors (e.g., eating, drinking, smoking) occur in the context of everyday activities, digital technologies may help us better understand and influence these behaviors in real time. Using a momentary self-regulation measure, the current study (which was part of a larger multi-year research project on the science of behavior change) used ecological momentary assessment (EMA) to assess if self-regulation can be engaged and manipulated on a momentary basis in naturalistic, non-laboratory settings. Methods: This one-arm, open-label exploratory study prospectively collected momentary data for 14 days from 104 participants who smoked regularly and 81 participants who were overweight and had binge-eating disorder. Four times per day, participants were queried about momentary self-regulation, emotional state, and social and environmental context; recent smoking and exposure to smoking cues (smoking sample only); and recent eating, binge eating, and exposure to binge-eating cues (binge-eating sample only). This study used a novel, momentary self-regulation measure comprised of four subscales: momentary perseverance, momentary sensation seeking, momentary self-judgment, and momentary mindfulness. Participants were also instructed to engage with Laddr, a mobile application that provides evidence-based health behavior change tools via an integrated platform. The association between momentary context and momentary self-regulation was explored via mixed-effects models. Exploratory assessments of whether recent Laddr use (defined as use within 12 h of momentary responses) modified the association between momentary context and momentary self-regulation were performed via mixed-effects models. Results: Participants (mean age 35.2; 78% female) in the smoking and binge-eating samples contributed a total of 3,233 and 3,481 momentary questionnaires, respectively. Momentary self-regulation subscales were associated with several momentary contexts, in the combined as well as smoking and binge-eating samples. For example, in the combined sample momentary perseverance was associated with location, positively associated with positive affect, and negatively associated with negative affect, stress, and tiredness. In the smoking sample, momentary perseverance was positively associated with momentary difficulty in accessing cigarettes, caffeine intake, and momentary restraint in smoking, and negatively associated with temptation and urge to smoke. In the binge-eating sample, momentary perseverance was positively associated with difficulty in accessing food and restraint in eating, and negatively associated with urge to binge eat. While recent Laddr use was not associated directly with momentary self-regulation subscales, it did modify several of the contextual associations, including challenging contexts. Conclusions: Overall, this study provides preliminary evidence that momentary self-regulation may vary in response to differing momentary contexts in samples from two exemplar populations with risk behaviors. In addition, the Laddr application may modify some of these relationships. These findings demonstrate the possibility of measuring momentary self-regulation in a trans-diagnostic way and assessing the effects of momentary, mobile interventions in context. Health behavior change interventions may consider measuring and targeting momentary self-regulation in addition to trait-level self-regulation to better understand and improve health risk behaviors. This work will be used to inform a later stage of research focused on assessing the transdiagnostic mediating effect of momentary self-regulation on medical regimen adherence and health outcomes. Clinical Trial Registration: ClinicalTrials.gov, Identifier: NCT03352713.

Design issues and solutions for stop-signal data from the Adolescent Brain Cognitive Development (ABCD) study.

The Adolescent Brain Cognitive Development (ABCD) study is an unprecedented longitudinal neuroimaging sample that tracks the brain development of over 9-10 year olds through adolescence. At the core of this study are the three tasks that are completed repeatedly within the MRI scanner, one of which is the stop-signal task. In analyzing the available stopping experimental code and data, we identified a set of design issues that we believe significantly compromise its value. These issues include but are not limited to variable stimulus durations that violate basic assumptions of dominant stopping models, trials in which stimuli are incorrectly not presented, and faulty stop-signal delays. We present eight issues, show their effect on the existing ABCD data, suggest prospective solutions including task changes for future data collection and preliminary computational models, and suggest retrospective solutions for data users who wish to make the most of the existing data.

Severe violations of independence in response inhibition tasks.

The stop-signal paradigm, a primary experimental paradigm for understanding cognitive control and response inhibition, rests upon the theoretical foundation of race models, which assume that a go process races independently against a stop process that occurs after a stop-signal delay (SSD). We show that severe violations of this independence assumption at short SSDs occur systematically across a wide range of conditions, including fast and slow reaction times, auditory and visual stop signals, manual and saccadic responses, and especially in selective stopping. We also reanalyze existing data and show that conclusions can change when short SSDs are excluded. Last, we suggest experimental and analysis techniques to address this violation, and propose adjustments to extant models to accommodate this finding.

The cognitive and perceptual correlates of ideological attitudes: a data-driven approach.

Although human existence is enveloped by ideologies, remarkably little is understood about the relationships between ideological attitudes and psychological traits. Even less is known about how cognitive dispositions-individual differences in how information is perceived and processed- sculpt individuals' ideological worldviews, proclivities for extremist beliefs and resistance (or receptivity) to evidence. Using an unprecedented number of cognitive tasks (n = 37) and personality surveys (n = 22), along with data-driven analyses including drift-diffusion and Bayesian modelling, we uncovered the specific psychological signatures of political, nationalistic, religious and dogmatic beliefs. Cognitive and personality assessments consistently outperformed demographic predictors in accounting for individual differences in ideological preferences by 4 to 15-fold. Furthermore, data-driven analyses revealed that individuals' ideological attitudes mirrored their cognitive decision-making strategies. Conservatism and nationalism were related to greater caution in perceptual decision-making tasks and to reduced strategic information processing, while dogmatism was associated with slower evidence accumulation and impulsive tendencies. Religiosity was implicated in heightened agreeableness and risk perception. Extreme pro-group attitudes, including violence endorsement against outgroups, were linked to poorer working memory, slower perceptual strategies, and tendencies towards impulsivity and sensation-seeking-reflecting overlaps with the psychological profiles of conservatism and dogmatism. Cognitive and personality signatures were also generated for ideologies such as authoritarianism, system justification, social dominance orientation, patriotism and receptivity to evidence or alternative viewpoints; elucidating their underpinnings and highlighting avenues for future research. Together these findings suggest that ideological worldviews may be reflective of low-level perceptual and cognitive functions. This article is part of the theme issue 'The political brain: neurocognitive and computational mechanisms'.

Correlation Database of 60 Cross-Disciplinary Surveys and Cognitive Tasks Assessing Self-Regulation.

Self-regulation is studied across various disciplines, including personality, social, cognitive, health, developmental, and clinical psychology; psychiatry; neuroscience; medicine; pharmacology; and economics. Widespread interest in self-regulation has led to confusion regarding both the constructs within the nomological network of self-regulation and the measures used to assess these constructs. To facilitate the integration of cross-disciplinary measures of self-regulation, we estimated product-moment and distance correlations among 60 cross-disciplinary measures of self-regulation (23 self-report surveys, 37 cognitive tasks) and measures of health and substance use based on 522 participants. The correlations showed substantial variability, though the surveys demonstrated greater convergent validity than did the cognitive tasks. Variables derived from the surveys only weakly correlated with variables derived from the cognitive tasks (M = .049, range = .000 to .271 for the absolute value of the product-moment correlation; M = .085, range = .028 to .241 for the distance correlation), thus challenging the notion that these surveys and cognitive tasks measure the same construct. We conclude by outlining several potential uses for this publicly available database of correlations.

Dataset decay and the problem of sequential analyses on open datasets.

Open data allows researchers to explore pre-existing datasets in new ways. However, if many researchers reuse the same dataset, multiple statistical testing may increase false positives. Here we demonstrate that sequential hypothesis testing on the same dataset by multiple researchers can inflate error rates. We go on to discuss a number of correction procedures that can reduce the number of false positives, and the challenges associated with these correction procedures.

Open exploration.

Arguments in support of open science tend to focus on confirmatory research practices. Here we argue that exploratory research should also be encouraged within the framework of open science. We lay out the benefits of 'open exploration' and propose two complementary ways to implement this with little infrastructural change.

Uncovering the structure of self-regulation through data-driven ontology discovery.

Psychological sciences have identified a wealth of cognitive processes and behavioral phenomena, yet struggle to produce cumulative knowledge. Progress is hamstrung by siloed scientific traditions and a focus on explanation over prediction, two issues that are particularly damaging for the study of multifaceted constructs like self-regulation. Here, we derive a psychological ontology from a study of individual differences across a broad range of behavioral tasks, self-report surveys, and self-reported real-world outcomes associated with self-regulation. Though both tasks and surveys putatively measure self-regulation, they show little empirical relationship. Within tasks and surveys, however, the ontology identifies reliable individual traits and reveals opportunities for theoretic synthesis. We then evaluate predictive power of the psychological measurements and find that while surveys modestly and heterogeneously predict real-world outcomes, tasks largely do not. We conclude that self-regulation lacks coherence as a construct, and that data-driven ontologies lay the groundwork for a cumulative psychological science.

A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task.

Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.

Large-scale analysis of test-retest reliabilities of self-regulation measures.

The ability to regulate behavior in service of long-term goals is a widely studied psychological construct known as self-regulation. This wide interest is in part due to the putative relations between self-regulation and a range of real-world behaviors. Self-regulation is generally viewed as a trait, and individual differences are quantified using a diverse set of measures, including self-report surveys and behavioral tasks. Accurate characterization of individual differences requires measurement reliability, a property frequently characterized in self-report surveys, but rarely assessed in behavioral tasks. We remedy this gap by (i) providing a comprehensive literature review on an extensive set of self-regulation measures and (ii) empirically evaluating test-retest reliability of this battery in a new sample. We find that dependent variables (DVs) from self-report surveys of self-regulation have high test-retest reliability, while DVs derived from behavioral tasks do not. This holds both in the literature and in our sample, although the test-retest reliability estimates in the literature are highly variable. We confirm that this is due to differences in between-subject variability. We also compare different types of task DVs (e.g., model parameters vs. raw response times) in their suitability as individual difference DVs, finding that certain model parameters are as stable as raw DVs. Our results provide greater psychometric footing for the study of self-regulation and provide guidance for future studies of individual differences in this domain.

Dopaminergic medication shifts the balance between going and stopping in Parkinson's disease.

The present behavioral study delineates the impact of Parkinson's disease (PD) and of dopaminergic medication on action control over voluntary behavior. Previous studies reported either prolonged responding or stopping latencies in PD compared to healthy controls (HC). Few studies investigated the effects of dopaminergic medication on these processes concurrently. We administered a stop-change task, an extended version of the stop task, that required (i) speeded responding to a go signal (i.e., going), (ii) inhibiting ongoing motor responses (i.e., stopping), and (iii) changing to an alternative response. PD performance (n = 33) was collected once during regular dopaminergic medication conditions (On state) and once after a medication washout period (Off state). A group of age-matched HC (n = 21) performed the stop-change task once. Response latencies to go signals were comparable between HC and PD Off, indicative of unimpaired going. Compared to HC, PD Off showed prolonged stopping latencies. Within the clinical group, stopping latencies significantly improved after taking dopaminergic medication. Interestingly, the shorter stopping latencies observed in the On state were paralleled by longer response latencies to go signals. The degree of the inhibition improvement observed in the medication state was correlated with the degree of response slowing. Change RT did not vary between groups or between medication states. These patterns of results are discussed in terms of a tradeoff between going versus stopping of motor responses in PD patients. Shifts of this tradeoff seem to be driven by dopaminergic medication, which has potential clinical implications.

Applying novel technologies and methods to inform the ontology of self-regulation.

Self-regulation is a broad construct representing the general ability to recruit cognitive, motivational and emotional resources to achieve long-term goals. This construct has been implicated in a host of health-risk behaviors, and is a promising target for fostering beneficial behavior change. Despite its clear importance, the behavioral, psychological and neural components of self-regulation remain poorly understood, which contributes to theoretical inconsistencies and hinders maximally effective intervention development. We outline a research program that seeks to define a neuropsychological ontology of self-regulation, articulating the cognitive components that compose self-regulation, their relationships, and their associated measurements. The ontology will be informed by two large-scale approaches to assessing individual differences: first purely behaviorally using data collected via Amazon's Mechanical Turk, then coupled with neuroimaging data collected from a separate population. To validate the ontology and demonstrate its utility, we will then use it to contextualize health risk behaviors in two exemplar behavioral groups: overweight/obese adults who binge eat and smokers. After identifying ontological targets that precipitate maladaptive behavior, we will craft interventions that engage these targets. If successful, this work will provide a structured, holistic account of self-regulation in the form of an explicit ontology, which will better clarify the pattern of deficits related to maladaptive health behavior, and provide direction for more effective behavior change interventions.

Resisting distraction and response inhibition trigger similar enhancements of future performance.

Resisting distraction and response inhibition are crucial aspects of cognitive control. Interestingly, each of these abilities transiently improves just after it is utilized. Competing views differ, however, as to whether utilizing either of these abilities (e.g., resisting distraction) enhances future performance involving the other ability (e.g., response inhibition). To distinguish between these views, we combined a Stroop-like task that requires resisting distraction with a restraint variant of the stop-signal task that requires response inhibition. We observed similar sequential-trial effects (i.e., performance enhancements) following trials in which participants (a) resisted distraction (i.e., incongruent go trials) and (b) inhibited a response (i.e., congruent stop trials). First, the congruency effect in go trials, which indexes overall distractibility, was smaller after both incongruent go trials and congruent stop trials than it was after congruent go trials. Second, stop failures were less frequent after both incongruent go trials and congruent stop trials than after congruent go trials. A control experiment ruled out the possibility that perceptual conflict or surprise engendered by occasional stop signals triggers sequential-trial effects independent of stopping. Thus, our findings support a novel, integrated view in which resisting distraction and response inhibition trigger similar sequential enhancements of future performance.