The common factor of executive functions measures nothing but speed of information uptake.

There is an ongoing debate about the unity and diversity of executive functions and their relationship with other cognitive abilities such as processing speed, working memory capacity, and intelligence. Specifically, the initially proposed unity and diversity of executive functions is challenged by discussions about (1) the factorial structure of executive functions and (2) unfavorable psychometric properties of measures of executive functions. The present study addressed two methodological limitations of previous work that may explain conflicting results: The inconsistent use of (a) accuracy-based vs. reaction time-based indicators and (b) average performance vs. difference scores. In a sample of 148 participants who completed a battery of executive function tasks, we tried to replicate the three-factor model of the three commonly distinguished executive functions shifting, updating, and inhibition by adopting data-analytical choices of previous work. After addressing the identified methodological limitations using drift-diffusion modeling, we only found one common factor of executive functions that was fully accounted for by individual differences in the speed of information uptake. No variance specific to executive functions remained. Our results suggest that individual differences common to all executive function tasks measure nothing more than individual differences in the speed of information uptake. We therefore suggest refraining from using typical executive function tasks to study substantial research questions, as these tasks are not valid for measuring individual differences in executive functions.

An explorative multiverse study for extracting differences in P3 latencies between young and old adults.

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.

Fluid Intelligence Is (Much) More than Working Memory Capacity: An Experimental Analysis.

Empirical evidence suggests a great positive association between measures of fluid intelligence and working memory capacity, which implied to some researchers that fluid intelligence is little more than working memory. Because this conclusion is mostly based on correlation analysis, a causal relationship between fluid intelligence and working memory has not yet been established. The aim of the present study was therefore to provide an experimental analysis of this relationship. In a first study, 60 participants worked on items of the Advanced Progressive Matrices (APM) while simultaneously engaging in one of four secondary tasks to load specific components of the working memory system. There was a diminishing effect of loading the central executive on the APM performance, which could explain 15% of the variance in the APM score. In a second study, we used the same experimental manipulations but replaced the dependent variable with complex working memory span tasks from three different domains. There was also a diminishing effect of the experimental manipulation on span task performance, which could now explain 40% of the variance. These findings suggest a causal effect of working memory functioning on fluid intelligence test performance, but they also imply that factors other than working memory functioning must contribute to fluid intelligence.

Working memory load affects intelligence test performance by reducing the strength of relational item bindings and impairing the filtering of irrelevant information.

There is a broad consensus that individual differences in working memory capacity (WMC) are strongly related to individual differences in intelligence. However, correlational studies do not allow conclusions about the causal nature of the relationship between WMC and fluid intelligence. While research on the cognitive basis of intelligence typically assumes that simpler lower-level cognitive processes contribute to individual differences in higher-order reasoning processes, a reversed causality or a third variable giving rise to two intrinsically uncorrelated variables may exist. In two studies (n1 = 65, n2 = 113), we investigated the causal nature of the relationship between WMC and intelligence by assessing the experimental effect of working memory load on intelligence test performance. Moreover, we tested if the effect of working memory load on intelligence test performance increased under time constraints, as previous studies have shown that the association between the two constructs increases if intelligence tests are administered with a strict time limit. We show that working memory load impaired intelligence test performance, but that this experimental effect was not affected by time constraints, which suggests that the experimental manipulations of working memory capacity and processing time did not affect the same underlying cognitive process. Using a computational modeling approach, we demonstrated that external memory load affected both the building and maintenance of relational item bindings and the filtering of irrelevant information in working memory. Our results confirm that WMC causally contributes to higher-order reasoning processes. Moreover, they support the hypothesis that working memory capacity in general and the abilities to maintain arbitrary bindings and to disengage from irrelevant information in particular are intrinsically related to intelligence.

How robust is the relationship between neural processing speed and cognitive abilities?

Individual differences in processing speed are consistently related to individual differences in cognitive abilities, but the mechanisms through which a higher processing speed facilitates reasoning remain largely unknown. To identify these mechanisms, researchers have been using latencies of the event-related potential (ERP) to study how the speed of cognitive processes associated with specific ERP components is related to cognitive abilities. Although there is some evidence that latencies of ERP components associated with higher-order cognitive processes are related to intelligence, results are overall quite inconsistent. These inconsistencies likely result from variations in analytic procedures and little consideration of the psychometric properties of ERP latencies in relatively small sample studies. Here we used a multiverse approach to evaluate how different analytical choices regarding references, low-pass filter cutoffs, and latency measures affect the psychometric properties of P2, N2, and P3 latencies and their relations with cognitive abilities in a sample of 148 participants. Latent correlations between neural processing speed and cognitive abilities ranged from -.49 to -.78. ERP latency measures contained about equal parts of measurement error variance and systematic variance, and only about half of the systematic variance was related to cognitive abilities, whereas the other half reflected nuisance factors. We recommend addressing these problematic psychometric properties by recording EEG data from multiple tasks and modeling relations between ERP latencies and covariates in latent variable models. All in all, our results indicate that there is a substantial and robust relationship between neural processing speed and cognitive abilities when those issues are addressed.

A neurocognitive psychometrics account of individual differences in attentional control.

Attention control processes play an important role in many substantial psychological theories but are hard to reliably and validly measure on the subject-level. Therefore, associations between individual differences in attentional control and other variables are often inconsistent. Here we propose a novel neurocognitive psychometrics account of attentional control that integrates model parameters from the dual-stage two-phase model (Hübner et al., 2010), a mathematical model of selective attention, with neural correlates of conflict processing (i.e., latencies of the stimulus-locked lateralized readiness potential) in a multilayer structural equation model framework. We analyzed data from 150 participants who completed the Eriksen Flanker task while their EEG was recorded and used the neurocognitive psychometric approach to distinguish between two sequential stages of information-processing-target selection and response selection. Model parameters and neural correlates showed convergent validity and could be meaningfully related to each other. Together, these neurocognitive process parameters jointly explained 37 % of the variance in individual differences in higher-order cognitive abilities that were assessed with a battery of intelligence tests and working memory tasks. Individuals with greater cognitive abilities were not only better at focusing their attention on the target stimulus but also at subsequent response-selection. All in all, our results support the idea that individual differences in attentional control processes are related to individual differences in cognitive abilities. Moreover, they provide hope that the measurement crisis of individual differences in attentional control can be overcome by integrating measurement approaches from related disciplines such as mathematical psychology and cognitive neuroscience. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

How consistent is mind wandering across situations and tasks? A latent state-trait analysis.

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. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Do Individual Effects Reflect Quantitative or Qualitative Differences in Cognition?

Rouder and Haaf (2020) posed the important question if there are some individuals whose behavior is not in accordance with well-established experimental effects and whether these individual differences are quantitative or qualitative in nature. In our commentary, we discuss the distinction between quantitative and qualitative individual differences and between individual and average causal effects and come to the conclusion that this is not a new question, but in fact one that has already been discussed by Gordon W. Allport (1937) and Donald B. Rubin (1974, 1978). Moreover, we critically examine their proposed rule of thumb to collect about 100 trials per experimental condition to reliably measure individual differences in typical experimental effects. Based on simulation results, we suggest to not rely on any general rule of thumb, but to use simulation studies and the convenient quid function provided by the authors to make more informed decisions regarding trial numbers for specific experimental designs.

An Investigation of the Slope Parameters of Reaction Times and P3 Latencies in the Sternberg Memory Scanning Task - A Fixed-Links Model Approach.

The speed of short-term memory scanning is thought to be captured in the slope of the linear function of mean reaction times (RTs) regressed on set size in the Sternberg memory scanning task (SMST). Individual differences in the slope parameter have been hypothesized to correlate with general intelligence (g). However, this correlation can usually not be found. This present study chose a fixed-links model (FLM) approach to re-evaluate the RT slope parameter on a latent level in a sample of 102 participants aged 18 to 61 years who completed the SMST with set sizes 1, 3, and 5. The same was tried for P3 latencies to investigate whether or not both parameters measure the same cognitive processes in the SMST, and to assess the usability of both slopes to predict g. For RTs, a linear increase with set size was found. The slope of mean RTs correlated with g on a manifest level already. The FLM approach could better reveal this relationship with the correlation between the slope and g being substantially higher. For P3 latencies, we found no evidence for a linear increase, but a general increase from the smallest set size to the two larger ones. This indicates that RTs and P3 latencies do not measure the same cognitive processes in the SMST. The FLM proved suitable to investigate the association between the speed of short-term memory scanning and intelligence.

A chronometric model of the relationship between frontal midline theta functional connectivity and human intelligence.

Individual differences in cognitive control have been suggested to act as a domain-general bottleneck constraining performance in a variety of cognitive ability measures, including but not limited to fluid intelligence, working memory capacity, and processing speed. However, owing to psychometric problems associated with the measurement of individual differences in cognitive control, it has been challenging to empirically test the assumption that individual differences in cognitive control underlie individual differences in cognitive abilities. In the present study, we addressed these issues by analyzing the chronometry of intelligence-related differences in midfrontal global theta connectivity, which has been shown to reflect cognitive control functions. We demonstrate in a sample of 98 adults, who completed a cognitive control task while their electroencephalogram was recorded, that individual differences in midfrontal global theta connectivity during stages of higher-order information-processing explained 65% of the variance in fluid intelligence. In comparison, task-evoked theta connectivity during earlier stages of information processing was not related to fluid intelligence. These results suggest that more intelligent individuals benefit from an adaptive modulation of theta-band synchronization during the time-course of information processing. Moreover, they emphasize the role of interregional goal-directed information-processing for cognitive control processes in human intelligence and support theoretical accounts of intelligence, which propose that individual differences in cognitive control processes give rise to individual differences in cognitive abilities. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Do Attentional Lapses Account for the Worst Performance Rule?

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.

The Evidence for Geary's Theory on the Role of Mitochondrial Functioning in Human Intelligence Is Not Entirely Convincing.

Geary (2018, 2019) suggested that heritable and environmentally caused differences in mitochondrial functioning affect the integrity and efficiency of neurons and supporting glia cells and may thus contribute to individual differences in higher-order cognitive functioning and physical health. In our comment, we want to pose three questions aimed at different aspects of Geary's theory that critically evaluate his theory in the light of evidence from neurocognitive, cognitive enhancement, and behavioral genetics research. We question (1) if Geary's theory explains why certain cognitive processes show a stronger age-related decline than others; (2) if intervention studies in healthy younger adults support the claim that variation in mitochondrial functioning underlies variation in human intelligence; and (3) if predictions arising from the matrilineal heredity of mitochondrial DNA are supported by behavioral genetics research. We come to the conclusion that there are likely many more biological and social factors contributing to variation in human intelligence than mitochondrial functioning.

Diffusion modeling and intelligence: Drift rates show both domain-general and domain-specific relations with intelligence.

Several previous studies reported relationships between speed of information processing as measured with the drift parameter of the diffusion model (Ratcliff, 1978) and general intelligence. Most of these studies utilized only few tasks and none of them used more complex tasks. In contrast, our study (N = 125) was based on a large battery of 18 different response time tasks that varied both in content (numeric, figural, and verbal) and complexity (fast tasks with mean RTs of ca. 600 ms vs. more complex tasks with mean RTs of ca. 3,000 ms). Structural equation models indicated a strong relationship between a domain-general drift factor and general intelligence. Beyond that, domain-specific speed of information processing factors were closely related to the respective domain scores of the intelligence test. Furthermore, speed of information processing in the more complex tasks explained additional variance in general intelligence. In addition to these theoretically relevant findings, our study also makes methodological contributions showing that there are meaningful interindividual differences in content specific drift rates and that not only fast tasks, but also more complex tasks can be modeled with the diffusion model. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Inter-trial alpha power indicates mind wandering.

Mind wandering during ongoing tasks can impede task performance and increase the risk of failure in the laboratory as well as in daily-life tasks and work environments. Neurocognitive measures like the electroencephalography (EEG) offer the opportunity to assess mind wandering non-invasively without interfering with the primary task. However, the literature on electrophysiological correlates of mind wandering is rather inconsistent. The present study aims toward clarifying this picture by breaking down the temporal dynamics of mind wandering encounters using a cluster-based permutation approach. Participants performed a switching task during which mind wandering was occasionally assessed via thought probes applied after trial completion at random time points. In line with previous studies, response accuracy was reduced during mind wandering. Moreover, alpha power during the inter-trial interval was a significantly increased on those trials on which participants reported that they had been mind-wandering. This spatially widely distributed effect is theoretically well in line with recent findings linking an increased alpha power to an internally oriented state of attention. Measurements of alpha power may, therefore, be used to detect mind wandering online during critical tasks in traffic and industry in order to prevent failures.

Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence.

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.

Is general intelligence little more than the speed of higher-order processing?

Individual differences in the speed of information processing have been hypothesized to give rise to individual differences in general intelligence. Consistent with this hypothesis, reaction times (RTs) and latencies of event-related potential have been shown to be moderately associated with intelligence. These associations have been explained either in terms of individual differences in some brain-wide property such as myelination, the speed of neural oscillations, or white-matter tract integrity, or in terms of individual differences in specific processes such as the signal-to-noise ratio in evidence accumulation, executive control, or the cholinergic system. Here we show in a sample of 122 participants, who completed a battery of RT tasks at 2 laboratory sessions while an EEG was recorded, that more intelligent individuals have a higher speed of higher-order information processing that explains about 80% of the variance in general intelligence. Our results do not support the notion that individuals with higher levels of general intelligence show advantages in some brain-wide property. Instead, they suggest that more intelligent individuals benefit from a more efficient transmission of information from frontal attention and working memory processes to temporal-parietal processes of memory storage. (PsycINFO Database Record

Resilience Factors in Women with Severe Early-Life Maltreatment.

INTRODUCTION: Early-life maltreatment (ELM) has long-lasting negative consequences and is the most important general risk factor for mental disorders. Nevertheless, a number of maltreated children grow up to become healthy adults and have therefore been called 'resilient'. The aim of the current study is to investigate 'resilience factors' in the context of severe ELM. METHOD: The study was part of the large multicenter project Understanding and Breaking the Intergenerational Cycle of Abuse (UBICA). A total of 89 women were examined, 33 with ELM and at least one lifetime mental disorder (nonresilient), 19 with ELM but without lifetime mental disorders (resilient), and 37 without ELM and without lifetime mental disorders (controls). ELM and other circumstances before the age of 18 years were assessed with the Childhood Experience of Care and Abuse (CECA) Interview. Additional relevant person and situation factors were measured with the Structured Clinical Interview for Mental Disorders (SCID-I), International Personality Disorder Examination (IPDE), Difficulties in Emotion Regulation Scale (DERS), Vulnerable Attachment Style Questionnaire (VASQ), Barratt Impulsiveness Scale (BIS), NEO Five-Factor Inventory (NEO-FFI), and Multiple-Choice Vocabulary Intelligence Test (MWT-B). Factor analyses and paired t tests were performed to identify those variables which differentiate best between the three groups. In addition, a discriminant analysis was conducted to detect the accuracy of assigning women to their specific group. RESULTS: The factor analyses revealed 10 resilience factors based on which we could correctly assign 80% of the women to their group in the discriminant analysis. t tests of factor scores showed that resilient and nonresilient maltreated women mainly differed in current individual attributes (e.g. impulsivity, attachment style), while resilient and nonresilient maltreated women differed from controls in both their current individual attributes and their view of their situation as a child. CONCLUSION: The 4 variables neuroticism, extraversion, vulnerable attachment, and perceived loneliness during childhood were identified as most important in differentiating all three examined groups. Therefore, prevention and intervention programs focusing on the individual's development of secure attachment and social competence may be of particular importance in the context of ELM.

Age-related differences in the P3 amplitude in change blindness.

Observers often miss visual changes in the environment when they co-occur with other visual disruptions. This phenomenon is called change blindness. Previous research has shown that change blindness increases with age. The aim of the current study was to explore the role of post-perceptual stimulus processing in age differences. Therefore, the P3 component of the event-related potential was measured while younger, middle-aged, and older participants performed a change detection task under different task demands. Older adults detected fewer changes than younger adults, even when the task was very easy. Detected changes elicited greater P3 amplitudes than undetected changes in younger adults. This effect was reduced or even absent for middle-aged and older participants, irrespective of task demands. Because this P3 effect is supposed to reflect participants' confidence in change detection, less confidence in own responses may explain the decline of change detection performance in normal aging.

Simon effects in change detection and change blindness.

Responses to centrally presented target stimuli are faster when they are accompanied by a task-irrelevant lateral accessory stimulus that corresponds spatially with the response hand (accessory variant of the Simon effect). In four experiments, we tested whether this effect depends on the awareness of the accessory stimulus. In a change blindness task, participants were asked to respond to a central letter that was accompanied by a lateral background change on some trials. Change blindness describes the phenomenon that even large changes may remain unnoticed when they occur simultaneously with another visual disruption, e.g., a blank screen. In a series of four experiments, a significant Simon effect was observed both when the accessory stimulus reached awareness and when it remained unnoticed. These results indicate that, based on the spatial location of an accessory stimulus, a spatial code is generated. This code interferes with the response code on the response-selection stage.

Stability of heart rate variability indices reflecting parasympathetic activity.

Heart rate variability (HRV) is a measure of autonomic influences on heart rate that has frequently been used as a transsituationally consistent biomarker for cardiovascular health and emotional or cognitive functions. The psychometric properties of HRV however remain unclear. In the present study, we examined the reliability and temporal stability of parasympathetic HRV measures and estimated the portion of variance explained by transsituationally consistent trait variance and by effects of the situation and person-situation interaction with structural equation modeling. The results show good reliability of indices reflecting central parasympathetic control over heart rate and that about 40% of the variance of a single HRV measurement can be explained by effects of the situation and person-situation interaction. An aggregation across at least two measurements may be recommended when using HRV as a transsituationally consistent biomarker or trait.