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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.

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]

In intra-enterprise logistics and automation of manufacturing processes general a rising productivity by high flexibility is required. Existing transportation systems exclusively use two-dimensional track sections, because they can be served with standard drives. Because of these simple structures the transport speed is limited and thereby also the throughput. In this paper now a modular transportation system is presented which could reach higher speeds with a direct drive and the use of centrifugal force compensating curves. Simultaneously the system also can change the altitude. All this succeeds with the integration of three-dimensional track sections. Therefore a two piped guiding system with a long stator linear motor was designed. To combine the linear motor with the three dimensional track special stator elements were developed which allow a bending of the stator to follow the route course. The current work deals with the implementation of a mechanical passive switch, which is operated by the electromagnetic forces of the linear motor. So no additional mechanical actors or a separate electromagnetic system are necessary.

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. Although there is some evidence that latencies of ERP components associated with higher‐order cognitive processes are related to cognitive abilities, results are overall quite inconsistent. We show that these previous inconsistencies may have resulted from variation in analytic procedures and little consideration of the psychometric properties of ERP latencies in relatively small sample studies. Using a multiverse approach, we found evidence for a substantial, replicable, and robust relationship between neural processing speed and cognitive abilities. [ABSTRACT FROM AUTHOR]