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Drift-Diffusion Models (DDMs) are a widely-used class of models that assume an accumulation of evidence during a quick decision. These models are often used as measurement models to assess individual differences in cognitive processes, such as an individual's evidence accumulation rate and response caution. An additional underlying assumption of these models is that there is internal noise in the evidence accumulation process. However, fitting DDMs to experimental choice-response time data alone cannot yield estimates of an individual's evidence accumulation rate, caution, and internal noise at the same time. This is due to an intrinsic joint-unidentifiability of these parameters when fitting DDMs to behavioral data. We introduce methods of estimating these parameters at the same time with additional data types. The methods to estimate model parameters rely on Bayesian inference and simulation-based Bayesian inference. We show why these methods are useful without making strong assumptions.

The worst performance rule (WPR) describes the phenomenon that individuals' slowest responses in a task are often more predictive of their intelligence than their fastest or average responses. To explain this phenomenon, it was previously suggested that occasional lapses of attention during task completion might be associated with particularly slow reaction times. Because less intelligent individuals should experience lapses of attention more frequently, reaction time distribution should be more heavily skewed for them than for more intelligent people. Consequently, the correlation between intelligence and reaction times should increase from the lowest to the highest quantile of the response time distribution. This attentional lapses account has some intuitive appeal, but has not yet been tested empirically. Using a hierarchical modeling approach, we investigated whether the WPR pattern would disappear when including different behavioral, self-report, and neural measurements of attentional lapses as predictors. In a sample of N = 85, we found that attentional lapses accounted for the WPR, but effect sizes of single covariates were mostly small to very small. We replicated these results in a reanalysis of a much larger previously published data set. Our findings render empirical support to the attentional lapses account of the WPR.

Several studies have demonstrated that individual differences in processing speed fully mediate the association between age and intelligence, whereas the association between processing speed and intelligence cannot be explained by age differences. Because measures of processing speed reflect a plethora of cognitive and motivational processes, it cannot be determined which specific processes give rise to this mediation effect. This makes it hard to decide whether these processes should be conceived of as a cause or an indicator of cognitive aging. In the present study, we addressed this question by using a neurocognitive psychometrics approach to decompose the association between age differences and fluid intelligence. Reanalyzing data from two previously published datasets containing 223 participants between 18 and 61 years, we investigated whether individual differences in diffusion model parameters and in ERP latencies associated with higher-order attentional processing explained the association between age differences and fluid intelligence. We demonstrate that individual differences in the speed of non-decisional processes such as encoding, response preparation, and response execution, and individual differences in latencies of ERP components associated with higher-order cognitive processes explained the negative association between age differences and fluid intelligence. Because both parameters jointly accounted for the association between age differences and fluid intelligence, age-related differences in both parameters may reflect age-related differences in anterior brain regions associated with response planning that are prone to be affected by age-related changes. Conversely, age differences did not account for the association between processing speed and fluid intelligence. Our results suggest that the relationship between age differences and fluid intelligence is multifactorially determined.

In comparison to young adults, middle-aged and old people show lower scores in intelligence tests and slower response times in elementary cognitive tasks. Whether these well-documented findings can both be attributed to a general cognitive slow-down across the life-span has become subject to debate in the last years. The drift diffusion model can disentangle three main process components of binary decisions, namely the speed of information processing, the conservatism of the decision criterion and the non-decision time (i.e., time needed for processes such as encoding and motor response execution). All three components provide possible explanations for the association between response times and age. We present data from a broad study using 18 different response time tasks from three different content domains (figural, numeric, verbal). Our sample included people between 18 to 62 years of age, thus allowing us to study age differences across young-adulthood and mid-adulthood. Older adults generally showed longer non-decision times and more conservative decision criteria. For speed of information processing, we found a more complex pattern that differed between tasks. We estimated mediation models to investigate whether age differences in diffusion model parameters account for the negative relation between age and intelligence, across different intelligence process domains (processing capacity, memory, psychometric speed) and different intelligence content domains (figural, numeric, verbal). In most cases, age differences in intelligence were accounted for by age differences in non-decision time. Content domain-general, but not content domain-specific aspects of non-decision time were related to age. We discuss the implications of these findings on how cognitive decline and age differences in mental speed might be related.

Mind wandering is often defined as the phenomenon of one's attention drifting away from the current activity toward inner thoughts and feelings. In the laboratory, mind wandering is most frequently assessed with thought reports that are collected while people perform some ongoing activity. It is not clear, however, inasmuch the resulting mind-wandering reports are reflective of person-consistent mind-wandering tendencies and/or situation-driven fluctuations in mind-wandering behavior. To shed light on this question, we tested how consistent mind-wandering reports are across different measurement occasions and tasks to investigate to which extent they indicate individual differences in mind wandering. Results from a latent state-trait analysis showed that mind-wandering reports are occasion-consistent to some extent and also somewhat task-specific. Theoretical implications of these findings are that mind wandering in the laboratory is less affected by situational factors than often assumed, but that individual differences in mind wandering partly depend on the currently ongoing task. Future research should investigate the origins of task-specificity effects on mind wandering and researchers should further incorporate the idea of task-specificity in future theory building.

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between‐person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures—which is an important precondition for robust individual differences as well as longitudinal research—is not yet sufficiently studied. We examined reliability (split‐half correlations) and test–retest correlations for task‐free (resting‐state) BOLD fMRI as well as split‐half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split‐half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test–retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time‐resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region‐specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well‐suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. Practitioner Points: Variability and complexity measures of BOLD activation show good split‐half reliability and moderate test–retest reliability.Measures of variability of global functional connectivity over time can robustly quantify neural dynamics.Length of fMRI data has only a minor effect on reliability. [ABSTRACT FROM AUTHOR]

Childhood adversity can lead to cognitive deficits or enhancements, depending on many factors. Though progress has been made, two challenges prevent us from integrating and better understanding these patterns. First, studies commonly use and interpret raw performance differences, such as response times, which conflate different stages of cognitive processing. Second, most studies either isolate or aggregate abilities, obscuring the degree to which individual differences reflect task‐general (shared) or task‐specific (unique) processes. We addressed these challenges using Drift Diffusion Modeling (DDM) and structural equation modeling (SEM). Leveraging a large, representative sample of 9–10 year‐olds from the Adolescent Brain Cognitive Development (ABCD) study, we examined how two forms of adversity—material deprivation and household threat—were associated with performance on tasks measuring processing speed, inhibition, attention shifting, and mental rotation. Using DDM, we decomposed performance on each task into three distinct stages of processing: speed of information uptake, response caution, and stimulus encoding/response execution. Using SEM, we isolated task‐general and task‐specific variances in each processing stage and estimated their associations with the two forms of adversity. Youth with more exposure to household threat (but not material deprivation) showed slower task‐general processing speed, but showed intact task‐specific abilities. In addition, youth with more exposure to household threat tended to respond more cautiously in general. These findings suggest that traditional assessments might overestimate the extent to which childhood adversity reduces specific abilities. By combining DDM and SEM approaches, we can develop a more nuanced understanding of how adversity affects different aspects of youth's cognitive performance. Research Highlight: To understand how childhood adversity shapes cognitive abilities, the field needs analytical approaches that can jointly document and explain patterns of lowered and enhanced performance.Using Drift Diffusion Modeling and Structural Equation Modeling, we analyzed associations between adversity and processing speed, inhibition, attention shifting, and mental rotation.Household threat, but not material deprivation, was mostly associated with slower task‐general processing speed and more response caution. In contrast, task‐specific abilities were largely intact.Researchers might overestimate the impact of childhood adversity on specific abilities and underestimate the impact on general processing speed and response caution using traditional measures. [ABSTRACT FROM AUTHOR]

It is well established that P3 latencies increase with age. Investigating these age‐related differences requires numerous methodological decisions, resulting in pipelines of great variation. The aim of the present work was to investigate the effects of different analytical pipelines on the age‐related differences in P3 latencies in real data. Therefore, we conducted an explorative multiverse study and varied the low‐pass filter (4 Hz, 8 Hz, 16 Hz, 32 Hz, and no filter), the latency type (area vs. peak), the level of event‐related potential analysis (single participant vs. jackknifing), and the extraction method (manual vs. automated). Thirty young (18–21 years) and 30 old (50–60 years) participants completed three tasks (Nback task, Switching task, Flanker task), while an EEG was recorded. The results show that different analysis strategies can have a tremendous impact on the detection and magnitude of the age effect, with effect sizes ranging from 0% to 88% explained variance. Likewise, regarding the psychometric properties of P3 latencies, we found that the reliabilities fluctuated between rtt =.20 and 1.00, while the homogeneities ranged from rh = −.12 to.90. Based on predefined criteria, we found that the most effective pipelines relied on a manual extraction based on a single participant's data. For peak latencies, manual extraction performed well for all filters except for 4 Hz, while for area latencies, filters above 8 Hz produced desirable results. Furthermore, our findings add to the evidence that jackknifing combined with peak latencies can lead to inconclusive results. The results of our multiverse study show that different analysis strategies can have a tremendous impact on the detection and the magnitude of age‐related differences in P3 latency in cognitive tasks as well as on the psychometric properties of the data. Practical recommendations are derived from the findings. [ABSTRACT FROM AUTHOR]