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.

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.

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, due 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 EEG was recorded, that individual differences in mid-frontal global theta connectivity during stages of higher-order information-processing explained 65 percent 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 informationprocessing 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.

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, lowpass 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 -.79.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.