Scoring Alternatives for Mental Speed Tests: Measurement Issues and Validity for Working Memory Capacity and the Attentional Blink Effect

Task Switching: On the Relation of Cognitive Flexibility with Cognitive Capacity

The task-switching paradigm is deemed a measure of cognitive flexibility. Previous research has demonstrated that individual differences in task-switch costs are moderately inversely related to cognitive ability. However, current theories emphasize multiple component processes of task switching, such as task-set preparation and task-set inertia. The relations of task-switching processes with cognitive ability were investigated in the current study. Participants completed a task-switching paradigm with geometric forms and a visuospatial working memory capacity (WMC) task. The task-switch effect was decomposed with the diffusion model. Effects of task-switching and response congruency were estimated as latent differences using structural equation modeling. Their magnitudes and relations with visuospatial WMC were investigated. Effects in the means of parameter estimates replicated previous findings, namely increased non-decision time in task-switch trials. Further, task switches and response incongruency had independent effects on drift rates, reflecting their differential effects on task readiness. Findings obtained with the figural tasks employed in this study revealed that WMC was inversely related to the task-switch effect in non-decision time. Relations with drift rates were inconsistent. Finally, WMC was moderately inversely related to response caution. These findings suggest that more able participants either needed less time for task-set preparation or that they invested less time for task-set preparation.

Is Flexibility More than Fluency and Originality?

Flexibility (i.e., the number of categorically different ideas), fluency (i.e., the answer quantity), and originality (i.e., the quality of ideas) are essential aspects of the ability to think divergently. Theoretically, fluency and ideational flexibility tasks are akin to one another. However, flexibility was also considered to be uniquely related to working memory capacity due to the task requirements involved in generating diverse answers (e.g., self-monitoring, suppression, and category generation). Given that the role of working memory is strengthened in flexibility tasks relative to fluency and originality tasks, flexibility should be more strongly related with working memory. Additionally, mental speed should show a similar pattern of results because mental speed has been previously related to task complexity. Based on a sample of N = 409 adults (M_age = 24.01 years), we found in latent variable models that fluency/originality strongly predicts flexibility and accounts for 61% of its variance. Creative flexibility was unrelated to working memory and mental speed after controlling for fluency/originality. Additionally, the residual of a latent flexibility factor was unrelated to self-reported creative activities. We concluded that flexibility, as measured here, can be deemed primarily a method factor that did not show value over and above fluency/originality as assessed in traditional fluency and originality tasks. We discussed perspectives for disentangling trait and method variance in flexibility tasks.

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.

Binding Costs in Processing Efficiency as Determinants of Cognitive Ability

Performance in elementary cognitive tasks is moderately correlated with fluid intelligence and working memory capacity. These correlations are higher for more complex tasks, presumably due to increased demands on working memory capacity. In accordance with the binding hypothesis, which states that working memory capacity reflects the limit of a person's ability to establish and maintain temporary bindings (e.g., relations between items or relations between items and their context), we manipulated binding requirements (i.e., 2, 4, and 6 relations) in three choice reaction time paradigms (i.e., two comparison tasks, two change detection tasks, and two substitution tasks) measuring mental speed. Response time distributions of 115 participants were analyzed with the diffusion model. Higher binding requirements resulted in generally reduced efficiency of information processing, as indicated by lower drift rates. Additionally, we fitted bi-factor confirmatory factor analysis to the elementary cognitive tasks to separate basal speed and binding requirements of the employed tasks to quantify their specific contributions to working memory capacity, as measured by Recall-1-Back tasks. A latent factor capturing individual differences in binding was incrementally predictive of working memory capacity, over and above a general factor capturing speed. These results indicate that the relation between reaction time tasks and working memory capacity hinges on the complexity of the reaction time tasks. We conclude that binding requirements and, therefore, demands on working memory capacity offer a satisfactory account of task complexity that accounts for a large portion of individual differences in ability.

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.

Mene Mene Tekel Upharsin: Clerical Speed and Elementary Cognitive Speed are Different by Virtue of Test Mode Only

Current taxonomies of intelligence comprise two factors of mental speed, clerical speed (Gs), and elementary cognitive speed (Gt). Both originated from different research traditions and are conceptualized as dissociable constructs in current taxonomies. However, previous research suggests that tasks of one category can be transferred into the other category by simply changing the mode of administration, i.e., in form of a paper-and-pencil test or in from of a computer-based elementary cognitive task. However, cross-mode correlations for specific tasks are usually only moderate. In the present study, mental speed was assessed as a broad construct across different tasks and stimulus materials. This allowed modeling mental speed as a hierarchical construct for paper-and-pencil as well as for computer-based tests. Cross-mode correlations of the respective general factors were moderate (r = 0.64), while the cross-mode correlations of task-specific components depended on task type (r = 0.12 to r = 0.71). Only the g factors of mental speed, but not the task-specific components, were found to be related with working memory capacity as a marker of cognitive ability. The speed general factor modeled across computer-based tests was more highly correlated with working memory capacity (r = 0.66) than the general factor modeled across paper-and-pencil tests (r = 0.46). These findings corroborate a crucial role of the assessment method and imply that validity of speed tests is affected by the choice of the test format.