Applications of the diffusion decision model (DDM) to the study of cognitive individual differences consistently find that the model's drift rate (v) parameter forms a cohesive factor across many tasks and relates to measures of higher-order cognitive functioning, including general cognitive ability and working memory. This parameter is often interpreted as a measure of "processing speed," a traditional psychometric construct thought to reflect an individual's basic speed of information processing across tasks. However, conceptual differences between v and traditional notions of processing speed make this mapping far from straightforward. Racing accumulator models, which provide a more flexible and comprehensive account of behavioral data than the DDM, allow for the speed with which individuals accumulate evidence to be dissociated from the efficiency with which they accumulate task-relevant evidence (versus task-irrelevant evidence). We applied the DDM and a racing accumulator model to three tasks across three independent datasets to gauge the extent to which v parameter findings from the cognitive individual differences literature reflect speed of evidence accumulation (SEA) versus efficiency of evidence accumulation (EEA). Across all tasks, v was more strongly related to EEA than SEA. EEA was consistently related to measures of general cognitive ability, working memory, and executive function whereas SEA explained <1% of the variance in each. These findings suggest individual differences in the DDM's v parameter, and its relations with higher-order cognitive abilities, primarily reflect EEA rather than SEA and challenge the widespread practice of equating v with the traditional "processing speed" construct.

Poor inhibitory control and decision-making are often considered as risks for substance use and other adverse psychiatric outcomes. The Stop-Signal Task (SST) is a widely used protocol, from which inhibitory control is indexed by stop signal reaction time (SSRT). However, heretofore models of SSRT may be too simplistic to capture complex processes underlying task performance. In contrast, the Racing Diffusion Ex-Gaussian ABCD (RDEX-ABCD) model provides a more mechanistic framework, capturing both inhibitory control and task-general decision-making processes during the SST. Here, we applied the RDEX-ABCD model to SST data from the IMAGEN cohort (n > 1000) at ages 19 and 23, and examined model parameters in relation to substance use via Elastic Net regression. Connectome-based predictive modeling was then performed to identify brain networks predicting parameters, and the association between these networks and substance use was examined. We found that parameters indexing inhibitory control had no associations with substance use and were only weakly associated with brain connectivity. In contrast, parameters reflecting general decision-making processes - such as efficiency of evidence accumulation, decision threshold (response caution), probability of go failure - and their associated brain activity were significant predictors of cannabis and cigarette use. These findings suggested that efficiency of evidence accumulation, a neurocognitive mechanism that facilitates adaptive decision making across many contexts, emerged as a robust predictor of substance use vulnerability. Overall, general decision-making mechanisms may act as more reliable indicators of vulnerability to substance use than the conventional inhibitory control measures.

Background and Hypothesis: Individuals with schizophrenia (SZ) show difficulties deciding whether social cues are meant for them, which are implicated in paranoia and social functioning. Recent computational modeling findings suggest that such disruptions may stem from inefficient and biased “evidence accumulation”—the process of gathering information to make a decision. However, it is unclear whether these disruptions are specific to social information processing, particularly self-referential processing, or whether they reflect general processing deficits. Study Design: Mechanisms and clinical correlates of decision-making were examined in 39 SZ and 42 controls across three domains: self-referential social processing (eye contact detection task), non-self-referential social processing (facial gender identification task), and non-social general perceptual processing (visual integration task). Drift Diffusion Models characterized whether inefficient and biased evidence accumulation in SZ was specific to self-referential processing or reflected more general deficits. Study Results: Relative to controls, evidence accumulation in SZ was less sensitive to visual cues during eye contact detection and gender identification—not visual integration. Biased evidence accumulation occurred in SZ only during eye contact detection. In SZ, less sensitive evidence accumulation during eye contact detection and gender identification—not visual integration—was associated with worse paranoia, even after controlling for general processing difficulties. Conclusions: Aberrant self-referential social processing in SZ may be shaped by mechanisms supporting social and self-referential—but not basic visual—information processing. Paranoia was associated with disrupted processing of social—not non-social—information. Therefore, disrupted evidence accumulation in SZ may have differential symptom associations across social and non-social contexts.

Background Neurodevelopmental models regard impulsivity as a central risk factor for adolescent substance use. However, the practical utility of impulsivity in predicting substance use is complicated by variability among measures that encompass multiple methods and theoretical domains. Prior research has been constrained by cross-sectional designs, small sample sizes, and/or the use of a narrow subset of impulsivity measures.Method Leveraging the ABCD dataset (n = 11,868), we identified and replicated correlations among impulsivity measures and assessed their prospective longitudinal and concurrent predictive utility regarding adolescent substance use outcomes before 15 years old. We then used simulation to inform how associations between impulsivity and substance use vary across sampling strategies (population vs. high-risk cohorts) and sample sizes.Findings Correlations between questionnaire and behavioral measures of impulsivity were small, and questionnaires significantly outperformed behavioral measures in predicting substance use initiation, largely due to the contribution of the CBCL externalizing scale. Predictions of substance use based on impulsivity were statistically detectable but small according to clinical standards (AUCs 0.6-0.76), exhibiting sensitivity to sample size and base rate of substance use, and thus, poor absolute predictive performance. Large samples (n > 1,000) were needed to achieve adequate power for impulsivity measures to predict substance use initiation.Conclusion These results support a significant but small contribution of impulsivity in predicting the onset of early adolescent substance use, indicating that these factors alone are insufficient for clinically deployable prediction. In community samples, large sample sizes are needed for reproducible impulsivity prediction of adolescent substance use.

Computational models propose that cognitive development reflects greater evidence accumulation efficiency, but it remains unclear whether these gains stem from increased accumulation of correct information, reduced accumulation of incorrect information, or both-and if these processes differ across domains. Taking a computational approach, we tested these possibilities in two distinct cognitive systems: scene categorization and visually guided navigation. Ninety-five participants (ages 4-21) completed tasks in each domain. Accuracy and reaction times were fit with the linear ballistic accumulator model to estimate separate accumulation rates for correct and incorrect information. For scene categorization, only the correct accumulation rate increased with age, indicating a selective enhancement in accumulation efficiency. Conversely, for visually guided navigation, both correct and incorrect accumulation rates changed, reflecting broader efficiency gains. Together, these findings formally characterize the unique developmental mechanisms driving scene categorization and visually guided navigation while demonstrating the utility of this modeling approach across any cognitive system.

Characterizing associations between individual differences in brain activity and behavior remains a primary challenge in functional neuroimaging research. A growing literature supports the use of formal computational models to represent the mechanistic processes underlying behavior during cognitive tasks. This study applies one such model to the Adolescent Brain Cognitive Development Study stop-signal task (SST) and quantifies relationships between mechanistic model parameters and task-related brain activation using a machine-learning-based predictive modeling approach. With a large sample of task performance and task-related neuroimaging data from 9- to 11-year-olds (n = 6469), we found that SST formal model parameters showed relatively strong relationships with fMRI task-related activation (average variance explained as high as R2 = 26.86 ± 1.69%) compared with empirically derived performance measures (largest R2 = 20.89 ± 1.41%). Our approach suggests that neuroimaging data are most closely associated with evidence accumulation for the go choice process and with attentional lapses that prevent the initiation of the stop process (“trigger failure”). Increased salience network (i.e., insula and anterior cingulate) activity on correct go trials was associated with worse evidence accumulation, and greater visual cortex activity on error trials was associated with fewer attentional lapses. In addition, through relationships with phenotypic measures of inhibition, impulsivity, and cognition, we provide evidence supporting the formal model's construct validity. We demonstrate the utility of computational cognitive modeling for revealing stronger, and more meaningful, associations between brain function and behavior.

OBJECTIVE:Slower and more variable reaction time is one of the most prominent cognitive signatures in childhood attention-deficit/hyperactivity disorder (ADHD). However, standard use of tasks that involve motor responses to index "speed" potentially confounds fine-motor coordination with central cognitive processing speed. One promising alternative is a vocal articulation task, which provides a measure of speeded performance that is independent of fine-motor coordination. METHOD:The present study applies an ex-Gaussian decomposition to preparatory interval (the time to initiate a vocal response) and speech rate on a speeded articulation task among children aged 8-12 with and without ADHD (N = 119). RESULTS:There was substantial evidence that the tail of the distribution, as indexed by the tau parameter (which is linked to the rate of information accumulation), was larger in children with ADHD and among children with low working memory capacity (regardless of ADHD status). Variance in tau was also greater among children with ADHD, and the greater variance was not fully explained by individual differences in working memory. CONCLUSIONS:Results highlight the importance of adopting analytic methods that can more accurately describe performance. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Background:Understanding attention deficit/hyperactivity disorder (ADHD) medication patterns is crucial for optimizing treatment outcomes. There are limited data on racial, ethnic, gender and socioeconomic treatment differences across longitudinal national samples. Methods:Secondary data analysis of baseline through 3rd-year follow-up (2016-2020) data from the Adolescent Brain Cognitive Development Study (ABCD) (N = 11,875). Data were collected from 21 US sites, to reflect national demographic diversity. Complete case panel included 9708 children aged 9/10 at baseline and 12/13 at 3rd-year follow up. Sociodemographic factors (sex, race, ethnicity, household income), ADHD medication use (stimulants and non-stimulants), and ADHD severity were examined. Results:By the 3rd-year follow-up, 13% of children used ADHD medications. Females were more likely to never have received medications compared to males (92% vs. 82%, odds ratio [OR] 2.34, 95% confidence interval [CI] 2.05-2.67, p < 0.001). Females exhibited lower mean ADHD severity scores, though the difference diminished over time. Asian (95% vs. 87%, OR 2.83, 95% CI 1.41-5.38, p < 0.001) and Hispanic children (90% vs. 86%, OR 1.39, 95% CI 1.17-1.64, p < 0.001) were more likely to have never received medications compared to White and non-Hispanic children. Black children were more likely to discontinue medications (9% vs. 5%, OR 1.84, 95% CI 1.45-2.33, p < 0.001) compared to White children, although this was not significant after adjusting for sociodemographic factors. Children in lower income households were more likely to have clinically significant ADHD but less likely to receive and remain on medications compared to those from higher income households. Conclusions:Significant differences exist in ADHD medication use patterns among US children based on sex, race, ethnicity, and socioeconomic status. Addressing these differences is essential to ensure equitable access to treatment.

Previous cross-sectional studies demonstrated that reduced sleep is associated with widespread changes in the brain’s intrinsic functional architecture. The present study extends this work by clarifying links between sleep and the developing brain during adolescence both longitudinally (across two years) and directionally (does reduced sleep cause connectivity changes or are connectivity changes the cause of reduced sleep?). Our novel approach combines the Adolescent Brain Cognitive Development (ABCD) Study, a longitudinal observational study of 11,878 youth, and a second sample of 76 adult participants scanned after a typical night of sleep and after a sleep deprivation causal manipulation. First, in the ABCD dataset, we identified a robust and generalizable neurosignature of reduced sleep. Second, in an independent sample of ABCD participants, we demonstrate that greater reductions in sleep duration across two years are significantly related to greater expression of this neurosignature. Third, in the sleep deprivation dataset, we show that expression of the ABCD reduced sleep neurosignature is significantly increased within individuals following sleep deprivation, and that neurosignatures of reduced sleep from the two samples exhibit significant spatial correspondence. These results clarify links between sleep and the developing brain and provide novel evidence that changes in sleep produce characteristic brain functional connectivity changes across adolescence.

General cognitive ability (GCA), also called “general intelligence,” is thought to depend on network properties of the brain, which can be quantified through graph theoretic measures such as small worldness and module degree. An extensive set of studies examined links between GCA and graphical properties of resting state connectomes. However, these studies often involved small samples, applied just a few graph theory measures in each study, and yielded inconsistent results, making it challenging to identify the architectural underpinnings of GCA. Here, we address these limitations by systematically investigating univariate and multivariate relationships between GCA and 17 whole-brain and node-level graph theory measures in individuals from the Adolescent Brain Cognitive Development Study (n = 5937). We demonstrate that whole-brain graph theory measures, including small worldness and global efficiency, fail to exhibit meaningful relationships with GCA. In contrast, multiple node-level graphical measures, especially module degree (within-network connectivity), exhibit strong associations with GCA. We establish the robustness of these results by replicating them in a second large sample, the Human Connectome Project (n = 847), and across a variety of modeling choices. This study provides the most comprehensive and definitive account to date of complex interrelationships between GCA and graphical properties of the brain’s intrinsic functional architecture.

Background:Sleep is critical for healthy brain development and emotional well-being, especially during adolescence, when sleep, behavior, and neurobiology are rapidly evolving. Theoretical reviews and empirical research have historically focused on how sleep influences mental health through its impact on higher-order brain systems. No studies have leveraged data-driven network neuroscience methods to uncover interpretable, brainwide signatures of sleep duration in adolescence, their socioenvironmental origins, and their consequences for cognition and psychopathology. Methods:We implemented graph theory and component-based predictive modeling to examine how a multimodal index of sleep duration (parent-report, youth-report, Fitbit) is associated with intrinsic brain architecture in 3037 youths (ages 11-12 years) from the ABCD (Adolescent Brain Cognitive Development) Study. Results:We demonstrated that network integration/segregation exhibited a strong, generalizable multivariate association with sleep duration (r = 0.23, p < .001). The multivariate signature of shorter sleep predominantly involved increasing disconnection of a lower-order system, the somatomotor network, from other systems. Next, we identified a single component of brain architecture as the dominant contributor of this relationship (r = 0.15), which again exhibited this somatomotor disconnection motif. Finally, greater somatomotor disconnection was associated with lower socioeconomic resources, longer screen times, reduced cognitive/academic performance, and elevated externalizing problems (βs > 0.03, ps ≤ .007). Conclusions:These findings reveal a novel neural signature of shorter sleep in adolescence that is intertwined with environmental risk, cognition, and psychopathology. By robustly elucidating the key involvement of an understudied brain system in sleep, this study can inform theoretical and translational research directions on sleep to promote neurobehavioral development and mental health during the adolescent transition.

Pubertal timing has implications for adolescent substance use, such that early maturers have increased use. Yet, pubertal timing is not widely studied beyond adolescence, making it unclear whether and how adolescent effects persist or if downstream effects emerge after adolescence. This paper investigates the relation between pubertal timing (perceived comparison to same-sex peers) and alcohol use for 75-100 days and examines alcohol belief mediators. Participants (N=183) aged 21-45 years (M age =27.33 [SD age =6.65]) came from two intensive longitudinal studies. Across ~13,000 daily observations, pubertal timing was associated with normative daily alcohol use during adulthood, such that women who matured late and men who matured on-time drank the most. Alcohol beliefs about relaxation and social facilitation influenced the alcohol use behavior of late maturing men less than their peers. Adolescent alcohol use might be slow to emerge in late developers, and the mechanisms underlying use seem to differ across development and by gender.

Adolescence is a period of growth in cognitive performance and functioning. Recently, data-driven measures of brain-age gap, which can index cognitive decline in older populations, have been utilized in adolescent data with mixed findings. Instead of using a data-driven approach, here we assess the maturation status of the brain functional landscape in early adolescence by directly comparing an individual's resting-state functional connectivity (rsFC) to the canonical early-life and adulthood communities. Specifically, we hypothesized that the degree to which a youth's connectome is better captured by adult networks compared to infant/toddler networks is predictive of their cognitive development. To test this hypothesis across individuals and longitudinally, we utilized the Adolescent Brain Cognitive Development (ABCD) Study at baseline (9-10 years; n = 6469) and 2-year-follow-up (Y2: 11-12 years; n = 5060). Adjusted for demographic factors, our anchored rsFC score (AFC) was associated with better task performance both across and within participants. AFC was related to age and aging across youth, and change in AFC statistically mediated the age-related change in task performance. In conclusion, we showed that a model-fitting-free index of the brain at rest that is anchored to both adult and baby connectivity landscapes predicts cognitive performance and development in youth.