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

Common neural dysfunction of economic decision-making across psychiatric conditions

Adaptive decision-making, which is often impaired in various psychiatric conditions, is essential for well-being. Recent evidence has indicated that decision-making capacity in multiple tasks could be accounted for by latent dimensions, enlightening the question of whether there is a common disruption of brain networks in economic decision-making across psychiatric conditions. Here, we addressed the issue by combining activation/lesion network mapping analyses with a transdiagnostic brain imaging meta-analysis. Our findings indicate that there were transdiagnostic alterations in the thalamus and ventral striatum during the decision or outcome stage of decision-making. The identified regions represent key nodes in a large-scale network, which is composed of multiple heterogeneous brain regions and plays a causal role in motivational functioning. The findings suggest that disturbances in the network associated with emotion- and reward-related processing play a key role in dysfunctions of decision-making observed in various psychiatric conditions. This study provides the first meta-analytic evidence of common neural alterations linked to deficits in economic decision-making.

Impulsivity is a stable, measurable, and predictive psychological trait

Impulsivity is a personality construct frequently employed to explain and predict important human behaviors. Major inconsistencies in its definition and measurement, however, have led some researchers to call for an outright rejection of impulsivity as a psychological construct. We address this highly unsatisfactory state with a large-scale, preregistered study (N = 1,676) in which each participant completed 48 measures of impulsivity derived from 10 self-report scales and 10 behavioral tasks and reported frequencies of seven impulsivity-related behaviors (e.g., impulsive buying and social media usage); a subsample (N = 196) then completed a retest session 3 mo later. We found that correlations between self-report measures were substantially higher than those between behavioral tasks and between self-report measures and behavioral tasks. Bifactor analysis of these measures exacted one general factor of impulsivity I, akin to the general intelligence factor g, and six specific factors. Factor I was related mainly to self-report measures, had high test-retest reliability, and could predict impulsivity-related behaviors better than existing measures. We further developed a scale named the adjustable impulsivity scale (AIMS) to measure I. AIMS possesses excellent psychometric properties that are largely retained in shorter versions and could predict impulsivity-related behaviors equally well as I. These findings collectively support impulsivity as a stable, measurable, and predictive trait, indicating that it may be too early to reject it as a valid and useful psychological construct. The bifactorial structure of impulsivity and AIMS, meanwhile, significantly advance the conceptualization and measurement of construct impulsivity.

On the (un)reliability of common behavioral and electrophysiological measures from the stop signal task: Measures of inhibition lack stability over time

Response inhibition, the intentional stopping of planned or initiated actions, is often considered a key facet of control, impulsivity, and self-regulation. The stop signal task is argued to be the purest inhibition task we have, and it is thus central to much work investigating the role of inhibition in areas like development and psychopathology. Most of this work quantifies stopping behavior by calculating the stop signal reaction time as a measure of individual stopping latency. Individual difference studies aiming to investigate why and how stopping latencies differ between people often do this under the assumption that the stop signal reaction time indexes a stable, dispositional trait. However, empirical support for this assumption is lacking, as common measures of inhibition and control tend to show low test-retest reliability and thus appear unstable over time. The reasons for this could be methodological, where low stability is driven by measurement noise, or substantive, where low stability is driven by a larger influence of state-like and situational factors. To investigate this, we characterized the split-half and test-retest reliability of a range of common behavioral and electrophysiological measures derived from the stop signal task. Across three independent studies, different measurement modalities, and a systematic review of the literature, we found a pattern of low temporal stability for inhibition measures and higher stability for measures of manifest behavior and non-inhibitory processing. This pattern could not be explained by measurement noise and low internal consistency. Consequently, response inhibition appears to have mostly state-like and situational determinants, and there is little support for the validity of conceptualizing common inhibition measures as reflecting stable traits.

Clarifying the longitudinal factor structure, temporal stability, and construct validity of Go/No-Go task-related neural activation across adolescence and young adulthood

This study aimed to clarify the psychometric properties and development of Go/No-Go (GNG) task-related neural activation across critical periods of neurobiological maturation by examining its longitudinal stability, factor structure, developmental change, and associations with a computational index of task-general cognitive control. A longitudinal sample (N=289) of adolescents from the Michigan Longitudinal Study was assessed at four time-points (mean number of timepoints per participant=2.05; standard deviation=0.89) spanning early adolescence (ages 10-13) to young adulthood (22-25). Results suggested that regional neural activations from the "successful inhibition" (SI>GO) and "failed inhibition" (FI>GO; error-monitoring) contrasts are each described well by a single general factor. Neural activity across both contrasts showed developmental increases throughout adolescence that plateau in young adulthood. Neural activity metrics evidenced low temporal stability across this period of marked developmental change, and the SI>GO factor showed no relations with a behavioral index of cognitive control. The FI>GO factor displayed stronger criterion validity in the form of significant, positive associations with behaviorally measured cognitive control. Findings emphasize the utility of well-validated psychometric methods and longitudinal data for clarifying the measurement properties of functional neuroimaging metrics and improving measurement practices in developmental cognitive neuroscience.

Dynamic computational phenotyping of human cognition

Computational phenotyping has emerged as a powerful tool for characterizing individual variability across a variety of cognitive domains. An individual's computational phenotype is defined as a set of mechanistically interpretable parameters obtained from fitting computational models to behavioural data. However, the interpretation of these parameters hinges critically on their psychometric properties, which are rarely studied. To identify the sources governing the temporal variability of the computational phenotype, we carried out a 12-week longitudinal study using a battery of seven tasks that measure aspects of human learning, memory, perception and decision making. To examine the influence of state effects, each week, participants provided reports tracking their mood, habits and daily activities. We developed a dynamic computational phenotyping framework, which allowed us to tease apart the time-varying effects of practice and internal states such as affective valence and arousal. Our results show that many phenotype dimensions covary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. These results support a fundamentally dynamic understanding of cognitive variability within an individual.

Further validation of a new ADHD screening questionnaire measuring parents' explanations (time processing, cognition, and motivation) of inattention symptoms in their school-aged children

The triple pathway model suggests that different neuropsychological factors underlie symptoms of inattention (i.e., time, cognition and/or motivation problems). However, screening instruments asking individuals to judge the link between these neuropsychological factors and inattention are lacking. The recently developed screening questionnaire, PASSC, aims to examine these factors possibly causing inattention by asking parents to indicate to what extent their child experiences inattention symptoms and to what extent different neuropsychological factors explain this inattention. The present study extends prior validation research of the PASSC by examining associations between PASSC inattention explained by time, cognition, and/or motivation and children's performance on tests measuring these same three constructs. Results indicated positive correlations between PASSC inattention explained by time and less accurate performance on a time discrimination test, and between PASSC inattention explained by cognition and more working memory errors as well as higher attention switching costs. Furthermore, children whose parents indicated that their inattention was best explained by cognition showed higher switching costs than children whose inattention was best explained by motivation. This support for construct validity of the PASSC is limited to two PASSC explanations (i.e., time, cognition) and a subset of tests (i.e., time discrimination, attention switching, memory span). Future research should focus on integrating PASSC and performance test results to differentiate between children with attention problems but different underlying neuropsychological problems. Concluding, the PASSC can be a promising screening tool to identify inattention in children and the underlying explanation indicated by parents.

Reading Skill Profiles in School-Aged Italian-Speaking Children: A Latent Profile Analysis Investigation into the Interplay of Decoding, Comprehension and Attentional Control

Our study examined the complex relationships among reading performance (decoding, comprehension) and language, visuo-spatial, and attentional control abilities in 115 Italian-speaking children. Latent profile analysis was used to identify distinct clusters of participants showcasing quantitative differences in decoding skills, including word, pseudo-word, text reading speed and accuracy. Then, we used this classification to investigate group differences in a variety of linguistic, working memory, and visuo-spatial tasks, as well as in reading comprehension skills, by means of multivariate and univariate tests. Our results reveal significant links between reading proficiency and several key factors: language skills, visuo-spatial abilities, and attentional control. These findings illuminate the nuanced impact of domain-general processes that govern a series of linguistic and visuo-perceptive subcomponents during reading tasks. Additionally, using dominance analysis, predictors of written text comprehension were identified. Our findings suggest that effective reading comprehension relies on a synergistic interplay of adequate reading speed, attentional control, working memory, and verbal fluency, accounting for 23% of the explained variance. This study highlights the multifaceted nature of reading proficiency and suggests that a broader perspective is necessary to fully understand reading development and support its improvement.

Reliability of the serial reaction time task: If at first you don't succeed, try, try, try again

Procedural memory is involved in the acquisition and control of skills and habits that underlie rule and procedural learning, including the acquisition of grammar and phonology. The serial reaction time task (SRTT), commonly used to assess procedural learning, has been shown to have poor stability (test-retest reliability). We investigated factors that may affect the stability of the SRTT in adults. Experiment 1 examined whether the similarity of sequences learned in two sessions would impact stability: test-retest correlations were low regardless of sequence similarity (r < .31). Experiment 2 added a third session to examine whether individual differences in learning would stabilise with further training. There was a small (but nonsignificant) improvement in stability for later sessions (Sessions 1 and 2: r = .42; Sessions 2 and 3: r = .60). Stability of procedural learning on the SRTT remained suboptimal in all conditions, posing a serious obstacle to the use of this task as a sensitive predictor of individual differences and ultimately theoretical advance.

Reliable affordances: A generative modeling approach for test-retest reliability of the affordances task

The affordances task serves as an important tool for the assessment of cognition and visuomotor functioning, and yet its test-retest reliability has not been established. In the affordances task, participants attend to a goal-directed task (e.g., classifying manipulable objects such as cups and pots) while suppressing their stimulus-driven, irrelevant reactions afforded by these objects (e.g., grasping their handles). This results in cognitive conflicts manifesting at the task level and the response level. In the current study, we assessed the reliability of the affordances task for the first time. While doing so, we referred to the "reliability paradox," according to which behavioral tasks that produce highly replicable group-level effects often yield low test-retest reliability due to the inadequacy of traditional correlation methods in capturing individual differences between participants. Alongside the simple test-retest correlations, we employed a Bayesian generative model that was recently demonstrated to result in a more precise estimation of test-retest reliability. Two hundred and ninety-five participants completed an online version of the affordances task twice, with a one-week gap. Performance on the online version replicated results obtained under in-lab administrations of the task. While the simple correlation method resulted in weak test-retest measures of the different effects, the generative model yielded a good reliability assessment. The current results support the utility of the affordances task as a reliable behavioral tool for the assessment of group-level and individual differences in cognitive and visuomotor functioning. The results further support the employment of generative modeling in the study of individual differences.

Drivers of the socio-economic disadvantage gap in England: Sequential pathways that include the home learning environment and self-regulation as mediators

BACKGROUND

Socio-economic status (SES) is a powerful predictor of attainment. Research has identified multiple mechanisms that underpin the effect of SES on attainment. For example, self-regulation (processes through which individuals direct and control their attention, emotion and behaviour) has been identified as one mechanism mediating the SES attainment gap. However, previous studies have not directly tested the statistical pathways by which children from lower SES backgrounds develop low self-regulation skills and subsequently poor attainment at the end of primary school. Adding the home learning environment, which is associated with both SES and self-regulation, further fleshes out the longitudinal pathways.

AIMS

We propose and test a new model where the relationship between SES and school attainment is sequentially mediated by the family home learning environment and the child's self-regulation.

SAMPLE

This study uses the Effective Pre-school, Primary and Secondary Education data set to study 2311 English children.

METHODS

We measured SES (via socio-economic disadvantage) based on an index of low parental education, occupation and income at age 3+. The home learning environment was measured by the Home Learning Environment Index at age 3+; self-regulation was a teacher report on the Child Social Behaviour Questionnaire at age 4+ and attainment was measured via scores on national assessments of English and Maths at age 11.

RESULTS

Our measure of disadvantage predicted attainment. The home learning environment predicted children's self-regulation skills. The relationship between disadvantage and attainment was sequentially mediated by the home learning environment and self-regulation.

CONCLUSIONS

These findings suggest that home learning environment and self-regulation may play a sequential role in perpetuating socio-economic disparities in education.

Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts

Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, and time. For an embodied agent such as a human, decisions are also shaped by physical aspects of actions. Beyond the effects of reward outcomes on learning processes, to what extent can modeling of behavior in a reinforcement-learning task be complicated by other sources of variance in sequential action choices? What of the effects of action bias (for actions per se) and action hysteresis determined by the history of actions chosen previously? The present study addressed these questions with incremental assembly of models for the sequential choice data from a task with hierarchical structure for additional complexity in learning. With systematic comparison and falsification of computational models, human choices were tested for signatures of parallel modules representing not only an enhanced form of generalized reinforcement learning but also action bias and hysteresis. We found evidence for substantial differences in bias and hysteresis across participants-even comparable in magnitude to the individual differences in learning. Individuals who did not learn well revealed the greatest biases, but those who did learn accurately were also significantly biased. The direction of hysteresis varied among individuals as repetition or, more commonly, alternation biases persisting from multiple previous actions. Considering that these actions were button presses with trivial motor demands, the idiosyncratic forces biasing sequences of action choices were robust enough to suggest ubiquity across individuals and across tasks requiring various actions. In light of how bias and hysteresis function as a heuristic for efficient control that adapts to uncertainty or low motivation by minimizing the cost of effort, these phenomena broaden the consilient theory of a mixture of experts to encompass a mixture of expert and nonexpert controllers of behavior.

Capturing individual differences in sentence processing: How reliable is the self-paced reading task?

Advances in research on language processing have originally come from group-level comparisons, but there is now a growing interest in individual differences. To investigate individual differences, tasks that have shown robust group-level differences are often used with the implicit assumption that they will also be reliable when used as an individual differences measure. Here, we examined whether one of the primary tasks used in psycholinguistic research on language processing, the self-paced reading task, can reliably measure individual differences in relative clause processing. We replicated the well-established effects of relative clauses at the group level, with object relative clauses being more difficult to process than subject relative clauses. However, when using difference scores, the reliability of the size of the relative clause effect was close to zero because the self-paced reading times for the different relative clause types were highly correlated within individuals. Nonetheless, we found that the self-paced reading task can be used to reliably capture individual differences in overall reading speed as well as key sentence regions when the two types of relative clause sentences are considered separately. Our results indicate that both the reliability and validity of different sentence regions need to be assessed to determine whether and when self-paced reading can be used to examine individual differences in language processing.

Understanding divergence: Placing developmental neuroscience in its dynamic context

Neurodevelopment is not merely a process of brain maturation, but an adaptation to constraints unique to each individual and to the environments we co-create. However, our theoretical and methodological toolkits often ignore this reality. There is growing awareness that a shift is needed that allows us to study divergence of brain and behaviour across conventional categorical boundaries. However, we argue that in future our study of divergence must also incorporate the developmental dynamics that capture the emergence of those neurodevelopmental differences. This crucial step will require adjustments in study design and methodology. If our ultimate aim is to incorporate the developmental dynamics that capture how, and ultimately when, divergence takes place then we will need an analytic toolkit equal to these ambitions. We argue that the over reliance on group averages has been a conceptual dead-end with regard to the neurodevelopmental differences. This is in part because any individual differences and developmental dynamics are inevitably lost within the group average. Instead, analytic approaches which are themselves new, or simply newly applied within this context, may allow us to shift our theoretical and methodological frameworks from groups to individuals. Likewise, methods capable of modelling complex dynamic systems may allow us to understand the emergent dynamics only possible at the level of an interacting neural system.

Lifespan trajectories of risk preference, impulsivity, and self-control: A dataset containing self-report, informant-report, behavioral, hormone and functional neuroimaging measures from a cross-sectional human sample

This paper describes data collected from a cross-sectional convenience sample of 200 healthy human volunteers between 16 and 81 years of age. We assembled an extensive battery of measures of risk preference, impulsivity, and self-control, as well as a range of demographic and cognitive measures, Crucially, we adopted different measure categories, including self-reports, informant reports, behavioral measures, and biological measures (hormones, brain function) to capture individual differences, and adopted a within-participant design. Data collection took place over multiple sessions. First, participants completed a laboratory session at the university during which we collected computer-assisted self-report measures (i.e., standardized questionnaires) as well as behavioral measures using computerized tasks. Second, participants independently completed a home session that included the completion of self-report measures, and the collection of saliva samples. In parallel, we acquired informant reports from up to three individuals nominated by the study participants. Third, participants completed a final session at the local hospital during which we collected structural and functional neuroimaging data, as well as further self-report measures. The data was collected to address questions concerning the developmental trajectories of risk preference and related constructs while assessing the impact of the assessment method; however, we invite fellow researchers to benefit from and further explore the data for research on decision-making under risk and uncertainty in general, and to apply novel analytical approaches (e.g., machine-learning applications to the neuroimaging data). Combining a large set of measures with a within-participant design affords a wealth of opportunities for further insights and a more robust evidence base supporting current theorizing on (age-related) differences in risk preference, impulsivity, and self-control.

Emotional problems and peer victimization in adolescents born very preterm and full-term: Role of self-control skills in childhood

The aim of the current study was to examine whether self-control skills in childhood moderate the association between very preterm birth (<32 weeks of gestational age) and emotional problems and peer victimization in adolescence. We used data from four prospective cohort studies, which included 29,378 participants in total (N = 645 very preterm; N = 28,733 full-term). Self-control was mother-reported in childhood at 5-11 years whereas emotional problems and peer victimization were both self- and mother-reported at 12-17 years of age. Findings of individual participant data meta-analysis showed that self-control skills in childhood do not moderate the association between very preterm birth and adolescence emotional problems and peer victimization. It was shown that higher self-control skills in childhood predict lower emotional problems and peer victimization in adolescence similarly in very preterm and full-term borns.

Emotions under control? Better cognitive control is associated with reduced negative emotionality but increased negative emotional reactivity within individuals

Associations between impaired cognitive control and maladaptive emotion regulation have been extensively studied between individuals. However, it remains unclear if this relationship holds within individuals. In this study, we tested the assumption that momentary within-person fluctuation in cognitive control (working memory updating and response inhibition) is associated with emotional reactivity in everyday life. We conducted an experience sampling study (eight two-hourly prompts daily) where participants repeatedly performed short 2-back and Go/no-go tasks in daily life. We assessed negative and positive affective states, and unpleasantness of a recent event to capture emotional reactivity. We analyzed two overlapping samples: a Go/no-go and a 2-back dataset (N = 161/158). Our results showed that better momentary working memory updating was associated with decreased negative affect if the recent event was on average unpleasant for the given individual. However, better-than-average working memory updating in interaction with higher event-unpleasantness predicted higher negative affect levels (i.e., higher negative emotional reactivity). These findings may challenge the account of better cognitive control being universally related to adaptive emotion regulation. Although it is unlikely that emotional reactivity boosts working memory, future studies should establish the direction of causality.

Improved estimation of general cognitive ability and its neural correlates with a large battery of cognitive tasks

Elucidating the neural mechanisms of general cognitive ability (GCA) is an important mission of cognitive neuroscience. Recent large-sample cohort studies measured GCA through multiple cognitive tasks and explored its neural basis, but they did not investigate how task number, factor models, and neural data type affect the estimation of GCA and its neural correlates. To address these issues, we tested 1,605 Chinese young adults with 19 cognitive tasks and Raven's Advanced Progressive Matrices (RAPM) and collected resting state and n-back task fMRI data from a subsample of 683 individuals. Results showed that GCA could be reliably estimated by multiple tasks. Increasing task number enhances both reliability and validity of GCA estimates and reliably strengthens their correlations with brain data. The Spearman model and hierarchical bifactor model yield similar GCA estimates. The bifactor model has better model fit and stronger correlation with RAPM but explains less variance and shows weaker correlations with brain data than does the Spearman model. Notably, the n-back task-based functional connectivity patterns outperform resting-state fMRI in predicting GCA. These results suggest that GCA derived from a multitude of cognitive tasks serves as a valid measure of general intelligence and that its neural correlates could be better characterized by task fMRI than resting-state fMRI data.

Brain-phenotype predictions can survive across diverse real-world data

Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.

Out of control: computational dynamic control dysfunction in stress- and anxiety-related disorders

Control theory, which has played a central role in technological progress over the last 150 years, has also yielded critical insights into biology and neuroscience. Recently, there has been a surging interest in integrating control theory with computational psychiatry. Here, we review the state of the field of using control theory approaches in computational psychiatry and show that recent research has mapped a neural control circuit consisting of frontal cortex, parietal cortex, and the cerebellum. This basic feedback control circuit is modulated by estimates of reward and cost via the basal ganglia as well as by arousal states coordinated by the insula, dorsal anterior cingulate cortex, amygdala, and locus coeruleus. One major approach within the broader field of control theory, known as proportion-integral-derivative (PID) control, has shown promise as a model of human behavior which enables precise and reliable estimates of underlying control parameters at the individual level. These control parameters correlate with self-reported fear and with both structural and functional variation in affect-related brain regions. This suggests that dysfunctional engagement of stress and arousal systems may suboptimally modulate parameters of domain-general goal-directed control algorithms, impairing performance in complex tasks involving movement, cognition, and affect. Future directions include clarifying the causal role of control deficits in stress- and anxiety-related disorders and developing clinically useful tools based on insights from control theory.

Adapting Open Science and Pre-registration to Longitudinal Research

Open science practices, such as pre-registration and data sharing, increase transparency and may improve the replicability of developmental science. However, developmental science has lagged behind other fields in implementing open science practices. This lag may arise from unique challenges and considerations of longitudinal research. In this paper, preliminary guidelines are provided for adapting open science practices to longitudinal research to facilitate researchers' use of these practices. The guidelines propose a serial and modular approach to registration that includes an initial pre-registration of the methods and focal hypotheses of the longitudinal study, along with subsequent pre- or co-registered questions, hypotheses, and analysis plans associated with specific papers. Researchers are encouraged to share their research materials and relevant data with associated papers, and to report sufficient information for replicability. In addition, there should be careful consideration about requirements regarding the timing of data sharing, to avoid disincentivizing longitudinal research.

Digital and precision clinical trials: innovations for testing mental health medications, devices, and psychosocial treatments

Mental health treatment advances - including neuropsychiatric medications and devices, psychotherapies, and cognitive treatments - lag behind other fields of clinical medicine such as cardiovascular care. One reason for this gap is the traditional techniques used in mental health clinical trials, which slow the pace of progress, produce inequities in care, and undermine precision medicine goals. Newer techniques and methodologies, which we term digital and precision trials, offer solutions. These techniques consist of (1) decentralized (i.e., fully-remote) trials which improve the speed and quality of clinical trials and increase equity of access to research, (2) precision measurement which improves success rate and is essential for precision medicine, and (3) digital interventions, which offer increased reach of, and equity of access to, evidence-based treatments. These techniques and their rationales are described in detail, along with challenges and solutions for their utilization. We conclude with a vignette of a depression clinical trial using these techniques.

Closing the loop in psychiatric deep brain stimulation: physiology, psychometrics, and plasticity

Deep brain stimulation (DBS) is an invasive approach to precise modulation of psychiatrically relevant circuits. Although it has impressive results in open-label psychiatric trials, DBS has also struggled to scale to and pass through multi-center randomized trials. This contrasts with Parkinson disease, where DBS is an established therapy treating thousands of patients annually. The core difference between these clinical applications is the difficulty of proving target engagement, and of leveraging the wide range of possible settings (parameters) that can be programmed in a given patient's DBS. In Parkinson's, patients' symptoms change rapidly and visibly when the stimulator is tuned to the correct parameters. In psychiatry, those same changes take days to weeks, limiting a clinician's ability to explore parameter space and identify patient-specific optimal settings. I review new approaches to psychiatric target engagement, with an emphasis on major depressive disorder (MDD). Specifically, I argue that better engagement may come by focusing on the root causes of psychiatric illness: dysfunction in specific, measurable cognitive functions and in the connectivity and synchrony of distributed brain circuits. I overview recent progress in both those domains, and how it may relate to other technologies discussed in companion articles in this issue.

The complexity of measuring reliability in learning tasks: An illustration using the Alternating Serial Reaction Time Task

Despite the fact that reliability estimation is crucial for robust inference, it is underutilized in neuroscience and cognitive psychology. Appreciating reliability can help researchers increase statistical power, effect sizes, and reproducibility, decrease the impact of measurement error, and inform methodological choices. However, accurately calculating reliability for many experimental learning tasks is challenging. In this study, we highlight a number of these issues, and estimate multiple metrics of internal consistency and split-half reliability of a widely used learning task on a large sample of 180 subjects. We show how pre-processing choices, task length, and sample size can affect reliability and its estimation. Our results show that the Alternating Serial Reaction Time Task has respectable reliability, especially when learning scores are calculated based on reaction times and two-stage averaging. We also show that a task length of 25 blocks can be sufficient to meet the usual thresholds for minimally acceptable reliability. We further illustrate how relying on a single point estimate of reliability can be misleading, and the calculation of multiple metrics, along with their uncertainties, can lead to a more complete characterization of the psychometric properties of tasks.

Psychometric validation and clinical correlates of an experiential foraging task

Measuring the function of decision-making systems is a central goal of computational psychiatry. Individual measures of decisional function could be used to describe neurocognitive profiles that underpin psychopathology and offer insights into deficits that are shared across traditional diagnostic classes. However, there are few demonstrably reliable and mechanistically relevant metrics of decision making that can accurately capture the complex overlapping domains of cognition whilst also quantifying the heterogeneity of function between individuals. The WebSurf task is a reverse-translational human experiential foraging paradigm which indexes naturalistic and clinically relevant decision-making. To determine its potential clinical utility, we examined the psychometric properties and clinical correlates of behavioural parameters extracted from WebSurf in an initial exploratory experiment and a pre-registered validation experiment. Behaviour was stable over repeated administrations of the task, as were individual differences. The ability to measure decision making consistently supports the potential utility of the task in predicting an individual's propensity for response to psychiatric treatment, in evaluating clinical change during treatment, and in defining neurocognitive profiles that relate to psychopathology. Specific aspects of WebSurf behaviour also correlate with anhedonic and externalising symptoms. Importantly, these behavioural parameters may measure dimensions of psychological variance that are not captured by traditional rating scales. WebSurf and related paradigms might therefore be useful platforms for computational approaches to precision psychiatry.

Measuring self-regulation in everyday life: Reliability and validity of smartphone-based experiments in alcohol use disorder

Self-regulation, the ability to guide behavior according to one's goals, plays an integral role in understanding loss of control over unwanted behaviors, for example in alcohol use disorder (AUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors usually occur, namely outside the laboratory, and in clinical populations such as people with AUD. Moreover, lab-based tasks have been criticized for poor test-retest reliability and lack of construct validity. Smartphones can be used to deploy tasks in the field, but often require shorter versions of tasks, which may further decrease reliability. Here, we show that combining smartphone-based tasks with joint hierarchical modeling of longitudinal data can overcome at least some of these shortcomings. We test four short smartphone-based tasks outside the laboratory in a large sample (N = 488) of participants with AUD. Although task measures indeed have low reliability when data are analyzed traditionally by modeling each session separately, joint modeling of longitudinal data increases reliability to good and oftentimes excellent levels. We next test the measures' construct validity and show that extracted latent factors are indeed in line with theoretical accounts of cognitive control and decision-making. Finally, we demonstrate that a resulting cognitive control factor relates to a real-life measure of drinking behavior and yields stronger correlations than single measures based on traditional analyses. Our findings demonstrate how short, smartphone-based task measures, when analyzed with joint hierarchical modeling and latent factor analysis, can overcome frequently reported shortcomings of experimental tasks.

Why many studies of individual differences with inhibition tasks may not localize correlations

Individual difference exploration of cognitive domains is predicated on being able to ascertain how well performance on tasks covary. Yet, establishing correlations among common inhibition tasks such as Stroop or flanker tasks has proven quite difficult. It remains unclear whether this difficulty occurs because there truly is a lack of correlation or whether analytic techniques to localize correlations perform poorly real-world contexts because of excessive measurement error from trial noise. In this paper, we explore how well correlations may localized in large data sets with many people, tasks, and replicate trials. Using hierarchical models to separate trial noise from true individual variability, we show that trial noise in 24 extant tasks is about 8 times greater than individual variability. This degree of trial noise results in massive attenuation in correlations and instability in Spearman corrections. We then develop hierarchical models that account for variation across trials, variation across individuals, and covariation across individuals and tasks. These hierarchical models also perform poorly in localizing correlations. The advantage of these models is not in estimation efficiency, but in providing a sense of uncertainty so that researchers are less likely to misinterpret variability in their data. We discuss possible improvements to study designs to help localize correlations.

Rapid, reliable mobile assessment of affect-related motor processing

Mobile technologies can be used for behavioral assessments to associate changes in behavior with environmental context and its influence on mental health and disease. Research on real-time motor control with a joystick, analyzed using a computational proportion-derivative (PD) modeling approach, has shown that model parameters can be estimated with high reliability and are related both to self-reported fear and to brain structures important for affective regulation, such as the anterior cingulate cortex. Here we introduce a mobile version of this paradigm, the rapid assessment of motor processing (RAMP) paradigm, and show that it provides robust, reliable, and accessible behavioral measurements relevant to mental health. A smartphone version of a previous joystick sensorimotor task was developed in which participants control a virtual car to a stop sign and stop. A sample of 89 adults performed the task, with 66 completing a second retest session. A PD modeling approach was applied to compute Kp (drive) and Kd (damping) parameters. Both Kp and Kd exhibited high test-retest reliabilities (ICC .81 and .78, respectively). Replicating a previous finding from a different sample with the joystick version of the task, both Kp and Kd were negatively associated with self-reported fear. The RAMP paradigm, a mobile sensorimotor assessment, can be used to assess drive and damping during motor control, which is robustly associated with subjective affect. This paradigm could be useful for examining dynamic contextual modulation of affect-related processing, which could improve assessment of the effects of interventions for psychiatric disorders in a real-world context.

Erotic cue exposure increases physiological arousal, biases choices toward immediate rewards, and attenuates model-based reinforcement learning

Computational psychiatry focuses on identifying core cognitive processes that appear altered across distinct psychiatric disorders. Temporal discounting of future rewards and model-based control during reinforcement learning have proven as two promising candidates. Despite its trait-like stability, temporal discounting may be at least partly under contextual control. Highly arousing cues were shown to increase discounting, although evidence to date remains somewhat mixed. Whether model-based reinforcement learning is similarly affected by arousing cues remains unclear. Here, we tested cue-reactivity effects (erotic pictures) on subsequent temporal discounting and model-based reinforcement learning in a within-subjects design in n = 39 healthy heterosexual male participants. Self-reported and physiological arousal (cardiac activity and pupil dilation) were assessed before and during cue exposure. Arousal was increased during exposure of erotic versus neutral cues both on the subjective and autonomic level. Erotic cue exposure increased discounting as reflected by more impatient choices. Hierarchical drift diffusion modeling (DDM) linked increased discounting to a shift in the starting point bias of evidence accumulation toward immediate options. Model-based control during reinforcement learning was reduced following erotic cues according to model-agnostic analysis. Notably, DDM linked this effect to attenuated forgetting rates of unchosen options, leaving the model-based control parameter unchanged. Our findings replicate previous work on cue-reactivity effects in temporal discounting and for the first time show similar effects in model-based reinforcement learning in a heterosexual male sample. This highlights how environmental cues can impact core human decision processes and reveal that comprehensive modeling approaches can yield novel insights in reward-based decision processes.

An individual differences perspective on pragmatic abilities in the preschool years

Pragmatic abilities are fundamental to successful language use and learning. Individual differences studies contribute to understanding the psychological processes involved in pragmatic reasoning. Small sample sizes, insufficient measurement tools, and a lack of theoretical precision have hindered progress, however. Three studies addressed these challenges in three- to 5-year-old German-speaking children (N = 228, 121 female). Studies 1 and 2 assessed the psychometric properties of six pragmatics tasks. Study 3 investigated relations among pragmatics tasks and between pragmatics and other cognitive abilities. The tasks were found to measure stable variation between individuals. Via a computational cognitive model, individual differences were traced back to a latent pragmatics construct. This presents the basis for understanding the relations between pragmatics and other cognitive abilities. RESEARCH HIGHLIGHTS: Individual differences in pragmatic abilities are important to understanding variation in language development. Research in this domain lacks a precise theoretical framework and psychometrically high-quality measures. We present six tasks capturing a wide range of pragmatic abilities with excellent re-test reliability. We use a computational cognitive model to provide a substantive theory of individual differences in pragmatic abilities.

Mechanisms of cognitive disinhibition explain individual differences in adult attention deficit hyperactivity disorder traits

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) in adults is strongly associated with psychiatric comorbidity and functional impairment. Here, we aimed to use a newly developed online cognitive battery with strong psychometric properties for measuring individual differences in three cognitive mechanisms proposed to underlie ADHD traits in adults: 1) attentional control - the ability to mobilize cognitive resources to stop a prepotent motor response; 2) information sampling/gathering - adequate sampling of information in a stimulus detection task prior to making a decision; and 3) shifting - the ability to adapt behavior in response to positive and negative contingencies.

METHODS

This cross-sectional and correlational study recruited 650 adults (330 males) aged 18-69 years (M = 33.06; MD = 31.00; SD = 10.50), with previously diagnosed ADHD (n = 329) and those from the general community without a history of ADHD (n = 321). Self-report measures of ADHD traits (i.e., inattention/disorganization, impulsivity, hyperactivity) and the cognitive battery were completed online.

RESULTS

Latent class analysis, exploratory structural equation modeling and factor mixture modeling revealed self-reported ADHD traits formed a unidimensional and approximately normally distributed phenotype. Bayesian structural equation modeling demonstrated that all three mechanisms measured by the cognitive battery, explained unique, incremental variance in ADHD traits, with a total of 15.9% explained in the ADHD trait factor.

CONCLUSIONS

Attentional control and shifting, as well as the less researched cognitive process of information gathering, explain individual difference variance in self-reported ADHD traits with potential to yield genetic and neurobiological insights into adult ADHD.

Inattentive responding can induce spurious associations between task behaviour and symptom measures

Although online samples have many advantages for psychiatric research, some potential pitfalls of this approach are not widely understood. Here we detail circumstances in which spurious correlations may arise between task behaviour and symptom scores. The problem arises because many psychiatric symptom surveys have asymmetric score distributions in the general population, meaning that careless responders on these surveys will show apparently elevated symptom levels. If these participants are similarly careless in their task performance, this may result in a spurious association between symptom scores and task behaviour. We demonstrate this pattern of results in two samples of participants recruited online (total N = 779) who performed one of two common cognitive tasks. False-positive rates for these spurious correlations increase with sample size, contrary to common assumptions. Excluding participants flagged for careless responding on surveys abolished the spurious correlations, but exclusion based on task performance alone was less effective.

Studying Individual Differences in Language Comprehension: The Challenges of Item-Level Variability and Well-Matched Control Conditions

Translating experimental tasks that were designed to investigate differences between conditions at the group-level into valid and reliable instruments to measure individual differences in cognitive skills is challenging (Hedge et al., 2018; Rouder et al., 2019; Rouder & Haaf, 2019). For psycholinguists, the additional complexities associated with selecting or constructing language stimuli, and the need for appropriate well-matched baseline conditions make this endeavour particularly complex. In a typical experiment, a process-of-interest (e.g. ambiguity resolution) is targeted by contrasting performance in an experimental condition with performance in a well-matched control condition. In many cases, careful between-condition matching precludes the same participant from encountering all stimulus items. Unfortunately, solutions that work for group-level research (e.g. constructing counterbalanced experiment versions) are inappropriate for individual-differences designs. As a case study, we report an ambiguity resolution experiment that illustrates the steps that researchers can take to address this issue and assess whether their measurement instrument is both valid and reliable. On the basis of our findings, we caution against the widespread approach of using datasets from group-level studies to also answer important questions about individual differences.

The role of impulse and interference control in aversive personality: A comprehensive assessment

Ethically and socially aversive behaviors have been attributed to several personality traits, including characteristics collectively referred to under the umbrella term of impulsivity. It is an open question, however, whether such characteristics are an integral part of ethically and socially aversive personality. Relying on three large samples (total N = 9854) and implementing longitudinal assessments, the present study provides a comprehensive investigation of the role of impulse and interference control in aversive personality. Based on contemporary conceptualizations of the impulsivity domain, a total of 17 dimensions spanning both self-reports and behavioral tasks are assessed. To represent aversive personality, we consider the D Factor of Personality (D), i.e., the basic disposition shared by all aversive traits. Results indicate that D co-occurs with a deficit in inhibiting the incorrect action when multiple actions are available, a preference for immediate rewards while failing to consider the consequences of one's actions, and maladaptive behavior directed towards regulating strong affect. However, most associations between D and dimensions of impulsivity were small or non-significant, thereby disconfirming the notion that characteristics related to a lack of impulse control are an integral feature of aversive personality in general.

Reliability of gamified reinforcement learning in densely sampled longitudinal assessments

Reinforcement learning is a core facet of motivation and alterations have been associated with various mental disorders. To build better models of individual learning, repeated measurement of value-based decision-making is crucial. However, the focus on lab-based assessment of reward learning has limited the number of measurements and the test-retest reliability of many decision-related parameters is therefore unknown. In this paper, we present an open-source cross-platform application Influenca that provides a novel reward learning task complemented by ecological momentary assessment (EMA) of current mental and physiological states for repeated assessment over weeks. In this task, players have to identify the most effective medication by integrating reward values with changing probabilities to win (according to random Gaussian walks). Participants can complete up to 31 runs with 150 trials each. To encourage replay, in-game screens provide feedback on the progress. Using an initial validation sample of 384 players (9729 runs), we found that reinforcement learning parameters such as the learning rate and reward sensitivity show poor to fair intra-class correlations (ICC: 0.22-0.53), indicating substantial within- and between-subject variance. Notably, items assessing the psychological state showed comparable ICCs as reinforcement learning parameters. To conclude, our innovative and openly customizable app framework provides a gamified task that optimizes repeated assessments of reward learning to better quantify intra- and inter-individual differences in value-based decision-making over time.

Development and clinical feasibility study of a brief version of an addiction-focused phenotyping battery in females receiving buprenorphine for opioid use disorder

INTRODUCTION

We aimed to streamline the NIDA Phenotyping Assessment Battery (PhAB), a package of self-report scales and neurobehavioral tasks used in substance use disorder (SUD) clinical trials, for clinical administration ease. Tailoring the PhAB to shorten administration time for a treatment setting is critical to expanding its acceptability in SUD clinical trials. This study's primary objectives were to develop a brief version of PhAB (PhAB-B) and assess its operational feasibility and acceptability in a female clinical treatment sample.

METHODS

Assessments of the original PhAB were evaluated along several criteria to identify a subset for the PhAB-B. Non-pregnant females (N=55) between ages 18-65, stabilized on buprenorphine for opioid use disorder (OUD) at an outpatient addiction clinic, completed this abbreviated battery remotely or after a provider visit in clinic. Participant satisfaction questions were administered. REDCap recorded the time to complete PhAB-B measures.

RESULTS

The PhAB-B included 11 measures that probed reward, cognition, negative emotionality, interoception, metacognition, and sleep. Participants who completed the PhAB-B (N =55) were 36.1 ± 8.9 years of age, White (54.5%), Black (34.5%), and non-Latinx (96.0%). Most participants completed the PhAB-B remotely (n = 42, 76.4%). Some participants completed it in-person (n = 13, 23.6%). PhAB-B mean completion time was 23.0 ± 12.0 min. Participant experiences were positive, and 96% of whom reported that they would participate in the study again.

CONCLUSION

Our findings support the clinical feasibility and acceptability of the PhAB-B among a female opioid use disorder outpatient addiction treatment sample. Future studies should assess the PhAB-B psychometric properties among broader treatment samples.

Test-retest reliability of a smartphone-based approach-avoidance task: Effects of retest period, stimulus type, and demographics

The approach-avoidance task (AAT) is an implicit task that measures people's behavioral tendencies to approach or avoid stimuli in the environment. In recent years, it has been used successfully to help explain a variety of health problems (e.g., addictions and phobias). Unfortunately, more recent AAT studies have failed to replicate earlier promising findings. One explanation for these replication failures could be that the AAT does not reliably measure approach-avoidance tendencies. Here, we first review existing literature on the reliability of various versions of the AAT. Next, we examine the AAT's reliability in a large and diverse sample (N = 1077; 248 of whom completed all sessions). Using a smartphone-based, mobile AAT, we measured participants' approach-avoidance tendencies eight times over a period of seven months (one measurement per month) in two distinct stimulus sets (happy/sad expressions and disgusting/neutral stimuli). The mobile AAT's split-half reliability was adequate for face stimuli (r = .85), but low for disgust stimuli (r = .72). Its test-retest reliability based on a single measurement was poor for either stimulus set (all ICC1s < .3). Its test-retest reliability based on the average of all eight measurements was moderately good for face stimuli (ICCk = .73), but low for disgust stimuli (ICCk = .5). Results suggest that single-measurement AATs could be influenced by unexplained temporal fluctuations of approach-avoidance tendencies. These fluctuations could be examined in future studies. Until then, this work suggests that future research using the AAT should rely on multiple rather than single measurements.

Adaptive Design Optimization as a Promising Tool for Reliable and Efficient Computational Fingerprinting

A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multidimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction, especially in clinical populations. Computational modeling may address this challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-the-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent work suggests that Bayesian adaptive design optimization (ADO) is a promising way to address these challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual's characteristics. In this review, we first describe the ADO methodology and then summarize recent work demonstrating that ADO increases the reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and proposing development of ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.

Improving the Reliability of Cognitive Task Measures: A Narrative Review

Cognitive tasks are capable of providing researchers with crucial insights into the relationship between cognitive processing and psychiatric phenomena. However, many recent studies have found that task measures exhibit poor reliability, which hampers their usefulness for individual differences research. Here, we provide a narrative review of approaches to improve the reliability of cognitive task measures. Specifically, we introduce a taxonomy of experiment design and analysis strategies for improving task reliability. Where appropriate, we highlight studies that are exemplary for improving the reliability of specific task measures. We hope that this article can serve as a helpful guide for experimenters who wish to design a new task, or improve an existing one, to achieve sufficient reliability for use in individual differences research.

From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research

Advances in computational statistics and corresponding shifts in funding initiatives over the past few decades have led to a proliferation of neuroscientific measures being developed in the context of mental health research. Although such measures have undoubtedly deepened our understanding of neural mechanisms underlying cognitive, affective, and behavioral processes associated with various mental health conditions, the clinical utility of such measures remains underwhelming. Recent commentaries point toward the poor reliability of neuroscientific measures to partially explain this lack of clinical translation. Here, we provide a concise theoretical overview of how unreliability impedes clinical translation of neuroscientific measures; discuss how various modeling principles, including those from hierarchical and structural equation modeling frameworks, can help to improve reliability; and demonstrate how to combine principles of hierarchical and structural modeling within the generative modeling framework to achieve more reliable, generalizable measures of brain-behavior relationships for use in mental health research.

Better together: novel methods for measuring and modeling development of executive function diversity while accounting for unity

INTRODUCTION

Executive functions (EFs) are linked to positive outcomes across the lifespan. Yet, methodological challenges have prevented precise understanding of the developmental trajectory of their organization.

METHODS

We introduce novel methods to address challenges for both measuring and modeling EFs using an accelerated longitudinal design with a large, diverse sample of students in middle childhood (N = 1,286; ages 8 to 14). We used eight adaptive assessments hypothesized to measure three EFs, working memory, context monitoring, and interference resolution. We deployed adaptive assessments to equate EF challenge across ages and a data-driven, network analytic approach to reveal the evolving diversity of EFs while simultaneously accounting for their unity.

RESULTS AND DISCUSSION

Using this methodological paradigm shift brought new precision and clarity to the development of these EFs, showing these eight tasks are organized into three stable components by age 10, but refinement of composition of these components continues through at least age 14.

The reliability of the serial reaction time task: meta-analysis of test-retest correlations

The Serial Reaction Time task, one of the most widely used tasks to index procedural memory, has been increasingly employed in individual differences research examining the role of procedural memory in language and other cognitive abilities. Yet, despite consistently producing robust procedural learning effects at the group level (i.e. faster responses to sequenced/probable trials versus random/improbable trials), these effects have recently been found to have poor reliability. In this meta-analysis (N = 7), comprising 719 participants (M = 20.81, s.d. = 7.13), we confirm this 'reliability paradox'. The overall retest reliability of the robust procedural learning effect elicited by the SRTT was found to be well below acceptable psychometric standards (r < 0.40). However, split-half reliability within a session is better, with an overall estimate of 0.66. There were no significant effects of sampling (participants' age), methodology (e.g. number of trials, sequence type) and analytical decisions (whether all trials were included when computing the procedural learning scores; using different indexes of procedural learning). Thus, despite producing robust effects at the group-level, until we have a better understanding of the factors that improve the reliability of this task using the SRTT for individual differences research should be done with caution.

From Computation to Clinic

Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation-and on the best strategies to overcome these barriers-is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses).

ReX: an integrative tool for quantifying and optimizing measurement reliability for the study of individual differences

Characterizing multifaceted individual differences in brain function using neuroimaging is central to biomarker discovery in neuroscience. We provide an integrative toolbox, Reliability eXplorer (ReX), to facilitate the examination of individual variation and reliability as well as the effective direction for optimization of measuring individual differences in biomarker discovery. We also illustrate gradient flows, a two-dimensional field map-based approach to identifying and representing the most effective direction for optimization when measuring individual differences, which is implemented in ReX.

The association of impulsivity with effects of the ChooseWell 365 workplace nudge intervention on diet and weight

Impulsivity is associated with unhealthy food choices. Nudge interventions in the food environment may be particularly helpful for individuals with high impulsivity. To examine if trait, choice, and action impulsivity were associated with the effectiveness of a workplace-based nudge intervention to improve diet and weight. This was a planned secondary analysis of 487 participants of ChooseWell 365, a randomized controlled trial that tested a 12-month nudge intervention to improve cafeteria purchases among hospital employees. Trait impulsivity was measured with the Barratt Impulsiveness Scale. Choice and action impulsivity were assessed with delay discounting and response inhibition tasks, respectively. Tertiles were generated for each measure. Multivariable regression models examined the association of impulsivity with cafeteria purchases [Healthy Purchasing Score (HPS)] over 12 months, dietary intake [Healthy Eating Index-2015 (HEI) score], and body mass index (BMI) measured at 12 months. Interaction terms tested differences in intervention effect by level of impulsivity. Participants with higher trait (p = .02) and choice (p < .001) impulsivity had lower baseline HPS than those with lower impulsivity. Employees of all impulsivity levels increased healthy eating, but higher trait impulsivity was associated with smaller increase in HPS over 12 months (p = .03). In the highest action impulsivity tertile, 12-month BMI increased less for intervention vs. control participants (0.3 vs. 0.5 kg/m2; p-interaction = .04). There were no interaction effects for trait or choice impulsivity. A workplace nudge intervention improved food choices among employees of all impulsivity levels and attenuated weight gain in those with higher action impulsivity.

Closed-Loop Deep Brain Stimulation for Psychiatric Disorders

ABSTRACT

Deep brain stimulation (DBS) is a well-established approach to treating medication-refractory neurological disorders and holds promise for treating psychiatric disorders. Despite strong open-label results in extremely refractory patients, DBS has struggled to meet endpoints in randomized controlled trials. A major challenge is stimulation "dosing"-DBS systems have many adjustable parameters, and clinicians receive little feedback on whether they have chosen the correct parameters for an individual patient. Multiple groups have proposed closed loop technologies as a solution. These systems sense electrical activity, identify markers of an (un)desired state, then automatically deliver or adjust stimulation to alter that electrical state. Closed loop DBS has been successfully deployed in movement disorders and epilepsy. The availability of that technology, as well as advances in opportunities for invasive research with neurosurgical patients, has yielded multiple pilot demonstrations in psychiatric illness. Those demonstrations split into two schools of thought, one rooted in well-established diagnoses and symptom scales, the other in the more experimental Research Domain Criteria (RDoC) framework. Both are promising, and both are limited by the boundaries of current stimulation technology. They are in turn driving advances in implantable recording hardware, signal processing, and stimulation paradigms. The combination of these advances is likely to change both our understanding of psychiatric neurobiology and our treatment toolbox, though the timeframe may be limited by the realities of implantable device development.

Testing locus coeruleus-norepinephrine accounts of working memory, attention control, and fluid intelligence

The current set of studies examined the relationship among working memory capacity, attention control, fluid intelligence, and pupillary correlates of tonic arousal regulation and phasic responsiveness in a combined sample of more than 1,000 participants in two different age ranges (young adults and adolescents). Each study was designed to test predictions made by two recent theories regarding the role of the locus coeruleus-norepinephrine (LC-NE) system in determining individual differences in cognitive ability. The first theory, proposed by Unsworth and Robison (2017a), posits two important individual differences: the moment-to-moment regulation of tonic arousal, and the phasic responsiveness of the system to goal-relevant stimuli. The second theory, proposed by Tsukahara and Engle (2021a), argues that people with higher cognitive abilities have greater functional connectivity between the LC-NE system and cortical networks at rest. These two theories are not mutually exclusive, but they make different predictions. Overall, we found no evidence consistent with a resting-state theory. However, phasic responsiveness was consistently correlated with working memory capacity, attention control, and fluid intelligence, supporting a prediction made by Unsworth and Robison (2017a). Tonic arousal regulation was not correlated with working memory or fluid intelligence and was inconsistently correlated with attention control, which offers only partial support for Unsworth and Robison's (2017a) second prediction.

Modeling urgency in the lab: Exploring the associations between self-reported urgency and behavioral responses to negative outcomes in laboratory gambling

Impulsivity is a multifaceted construct that relates to different behaviors in everyday life and has been associated with many psychopathological disorders and behavioral problems, such as problematic gambling behavior. One questionnaire to measure these several facets on a trait level is the UPPS-P Impulsive Behavior Scale. Specifically, the UPPS-P investigates five distinct facets: (a) negative urgency, (b) lack of premeditation, (c) lack of perseverance, (d) sensation seeking, and (e) positive urgency. Negative urgency at a trait level in particular seems to be associated with the development of psychopathological disorders. To date, there are no established state measures of negative urgency. However, it was recently proposed that speeding after losses might be a suitable measure. Thus, in this study, we explored the possible relationship between a state measure of negative urgency modeled through a behavioral gambling task and a trait measure of negative urgency through the UPPS-P questionnaire. We used correlational and network analyses in an aggregated database of eight samples (total N = 1216) to explore the potential relationships between post-loss speeding on the behavioral gambling task and UPPS-P scores (by combining trait vs. item-based analyses). We found that the degree of speeding after losses (post-loss speeding) did not correlate with the trait measure of impulsivity in general and negative urgency specifically, either at the latent trait level or on an item-based level. This null finding indicates that our state measure of post-loss speeding and negative urgency on a trait level does not seem to capture the same underlying constructs. Implications for personality research are discussed.

Evaluating individual differences in rewarded Stroop performance: reliability and associations with self-report measures

In three separate experiments, we examined the reliability of and relationships between self-report measures and behavioral response time measures of reward sensitivity. Using a rewarded-Stroop task we showed that reward-associated, but task-irrelevant, information interfered with task performance (MIRA) in all three experiments, but individual differences in MIRA were unreliable both within-session and over a period of approximately 4 weeks, providing clear evidence that it is not a good individual differences measure. In contrast, when the task-relevant information was rewarded, individual differences in performance benefits were remarkably reliable, even when examining performance one year later, and with a different version of a rewarded Stroop task. Despite the high reliability of the behavioral measure of reward responsiveness, behavioral reward responsiveness was not associated with self-reported reward responsiveness scores using validated questionnaires but was associated with greater self-reported self-control. Results are discussed in terms of what is actually being measured in the rewarded Stroop task.

Pitfalls of self-reported measures of self-control: Surprising insights from extreme debtors

OBJECTIVE

We took a rare opportunity to examine whether extreme debtors have inflated assessment of their self-control capacity, potentially rendering self-reported measures ineffective as prediction tools for debt risks.

METHOD

The self-control profiles of extreme debtors (n = 1442), whose credit card debt amounted to more than 12 months of their income, were compared with samples of the general population (n = 505) and students from an elite university (n = 1011) on a self-reported measure, a behavioral intention measure, and an executive function measure.

RESULTS

Extreme debtors reported the highest self-control on the self-reported measure but scored the lowest on the behavioral intention measure and executive function. This pattern was robust across different subsections of demographics and diverse reasons of indebtedness.

CONCLUSION

Our findings suggest that self-reported measures may be more susceptible to psychological distortions than behavioral measures, a pitfall that past research has overlooked. Furthermore, we reveal a double whammy faced by extreme debtors: they have a low capacity to regulate their behavior but hold an illusory perception of high capacity, which may expose them to further debt accumulation. The insights from our study underscore the utility of examining extreme segments of the population for advancing psychological knowledge.

Same same, but different: A psychometric examination of three frequently used experimental tasks for cognitive bias assessment in a sample of healthy young adults

Cognitive bias research draws upon the notion that altered information processing is key for understanding psychological functioning and well-being. However, little attention has been paid to the question of whether the frequently used experimental paradigms hold adequate psychometric properties. The present study examined the psychometric properties of three widely used cognitive bias tasks: the Approach-Avoidance Task (AAT), the visual dot-probe-task, and the Implicit Association Test (IAT). Approach, attentional, and association biases towards valenced stimuli were repeatedly measured at five different time points in a sample of 79 healthy young adults. Two different devices were used for assessment: a personal computer (PC) and a touchscreen-based tablet. Reliability estimates included internal consistency and temporal stability. Validity was inferred from convergence across different behavioral tasks and correlations between bias scores and self-reported psychological traits. Reliability ranged widely amongst tasks, assessment devices, and measurement time points. While the dot-probe-task appeared to be completely unreliable, bias scores obtained from the PC-based version of the AAT and both (PC and touchscreen) versions of the IAT showed moderate reliability. Almost no associations were found across information processing tasks or between implicit and explicit measures. Cognitive bias research should adopt a standard practice to routinely estimate and report psychometric properties of experimental paradigms, investigate feasible ways to develop more reliable tools, and use tasks that are suitable to answer the precise research question asked.