Intelligence最新文献

Gignac and Zajenkowski (2020) recommended testing the Dunning-Kruger (DK) hypothesis with a combination of polynomial regression and LOESS regression, as the conventional approach to testing the hypothesis (i.e., quartile split) confounds regression toward the mean and the better-than-average effect. Building upon Gignac and Zajenkowski (2020), an insightful method to estimate the magnitude and prevalence of a DK effect is introduced based on comparing linear and LOESS regression predicted values. Based on simulated data specified to exhibit a plausible DK effect for cognitive abilities, the magnitude of the DK effect was empirically modeled. The effect peaked at a 20-point relative overestimation at an IQ of 55, impacting only 0.14% of the population, and decreased to a 7-point relative overestimation at an IQ of 70, affecting 2.3% of the population. Analysing two large field data samples (N ≈ 3500 each) from participants who completed intelligence subtests in grammar and logical reasoning, the DK effect was found to account for a maximum relative ability overestimation of 7 to 9 percentile points. Notably, this effect was confined to only ≈ 0.2% of the participants (IQ ≈ 55), all of whom scored at chance levels. Finally, low levels of conditional reliability (≈ 0.40) at distribution extremes were found to complicate interpreting results that superficially support the DK hypothesis. It is concluded that, when analyzed using appropriate methods, it is unlikely that the DK effect will ever be demonstrated as an unambiguously meaningful psychological phenomenon affecting an appreciable portion of the population.

A meta-analysis of the criterion-related validity of U.S. military enlistment cognitive ability tests was conducted using hands-on performance tests (HOPTs) as the criterion. In a HOPT, incumbents perform a representative sample of the tasks for a position and are evaluated by trained raters. We found operational validities in the .40s to .50s. Our meta-analytic database allowed us to investigate concerns regarding overcorrections (via use of multivariate corrections with credible applicant covariances) and provides a direct measure of job proficiency.

Working memory (WM) serves not just for remembering facts (“declarative WM”) but also for controlling action and thought (“procedural WM”, pWM) by holding and manipulating task rules and task control parameters such as goals. Yet, the structure of individual differences in pWM is underexplored. The present work compared between two highly similar (in the number of stimulus response rules, stimuli, responses, and scoring method) pWM challenges: Rule WM (rWM) - loading WM with novel arbitrary stimulus response rules, and Task switching between familiar rules. A series of confirmatory bi-factor models fitted to results from two separate highly variable samples (N = 544, 520) support the existence of a common (to Switching and rWM) ability as well as the distinction between rWM and Switching. Latent regression models in which correlated latent variables of Switching, Speed and rWM predicted Reasoning and Anti-saccade performance indicate a different pattern of variance sharing for switching and rWM: Specific rWM and the rWM-Speed variance overlap predicted Reasoning, whereas specific Switch did not. Switch was predictive of Anti-saccade and Reasoning only through its overlap with the rWM (for Reasoning) and with both Speed and rWM (for both outcomes). Together, these results support the conclusion that the ability to meet a switching challenge and the ability to meet a challenge of having many rules to remember (rWM) constitute partly separable sources of individual differences.

Many intelligence tests measure multiple specific cognitive abilities. Practitioners use these specific ability scores, which encompass both specific ability and general intelligence variance, and the resulting intelligence profiles to make counseling and intervention decisions. In the present study, we investigated the temporal stability of eight specific abilities and their profiles over one school year, as well as their incremental validity in the prediction of school grades with German grade 7 to 9 students (N = 326 at T1; N = 311 at T2; N = 257 with IQ data at both times of measurement). The mean rank-order stability was 0.80 and ranged from 0.71 to 0.85. Intelligence profiles replicated significantly above chance levels (Mdn_κ = 0.31). The incremental validity coefficients were mostly small, but the Reasoning score substantially contributed to the prediction of math grades (ΔR² = 0.07–0.09), the Verbal Ability score to the prediction of German grades (ΔR² = 0.05–0.09), and the Crystallized Intelligence score to the prediction of geography grades (ΔR² = 0.03–0.08) beyond the general intelligence score. Our study of specific ability scores indicated moderate to high rank-order stability, fair to moderate profile stability, and substantial incremental validity for some specific ability scores.

In a recent meta-analysis, financial literacy – understanding basic financial terms and concepts – was significantly correlated with general intelligence (r ≈ 0.62). However, this correlation may be underestimated, as few studies employed robust measures of both intelligence (more than one subtest) and financial literacy (>12 items). Additionally, cognitive reflection has not been investigated for its unique capacity at predicting financial literacy, an important consideration, as cognitive reflection has been suggested to be a measure of financial cognition. Beyond cognitive factors, non-cognitive predictors like need for cognition and attitude toward numbers (comfort with numerical concepts) may also influence financial literacy. In our study of 688 young adults (17–40 years), we administered four intelligence subtests (including a measure of cognitive reflection), a financial literacy test, and questionnaires assessing need for cognition and attitude toward numbers - the first to investigate all of these dimensions simultaneously. Results revealed a stronger latent correlation (0.76) between general intelligence and financial literacy than previously reported. Cognitive reflection was found to yield some incremental predictive variance in predicting financial literacy beyond general intelligence. Finally, attitude toward numbers and need for cognition both correlated positively with both general intelligence and financial literacy, however, only attitude toward numbers uniquely predicted financial literacy in a structural equation model. These findings suggest that financial literacy shares closer to 50–60% of its variance with general intelligence. They also highlight the potential value of fostering a positive numerical attitude alongside financial knowledge in educational interventions.

A dimensionality reduction method was used to determine the task-timing-related functional brain networks underlying the Raven's Standard Progressive Matrices (RSPM), a non-verbal estimate of fluid intelligence (Gf). We identified five macro-scale task-based blood‑oxygen-level-dependent (BOLD)-signal brain networks and interpreted their network-level task-induced BOLD changes to provide functional interpretations separately for each network. This led to new observations about the brain networks underlying the RSPM: (1) the multiple demand network (MDN) for solution searching peaked early in the trial (∼9 s peak), followed by response (RESP) for response selection (∼12 s), and re-evaluation (RE-EV) for solution checking (∼18 s peak), (2) high activity in the MDN was correlated with high activity in the later-peaking RE-EV network, proposed to underpin cooperative solution searching (MDN) and checking (RE-EV) processes, and (3) high activity in the MDN in all conditions was associated with low accuracy in the hard RSPM condition, suggesting that those with lower performance on hard problems allocate more resources into solution-searching across all conditions. These findings corroborate the MDN's significance in Gf solution searching, and add the RE-EV network as playing an important checking role, providing overlap with the proposed abstraction/elaboration and hypothesis testing phases of the Parieto-Frontal Integration Theory (P-FIT). Therefore, this set of results not only supports past theoretical work on the brain networks underlying Gf and the RSPM task, but extends it by providing more complete anatomical, temporal, and functional information based on a set of brain task-based networks which replicate over many tasks.

The study examines how intensive hockey training is linked with spatial ability and academic performance. Participants were hockey players from top junior teams (N = 225, mean age = 14.25, all boys) and their unselected peers (N = 278, mean age = 15.47, all boys). Compared to the unselected group, hockey players showed lower results in 10 small-scale spatial tests (Cohen's d ranging from 0.42 to 1.04), Raven's Progressive Matrices (d = 0.41), and 12 school subjects (d for the sum of grades = 1.17). The differences in spatial ability remained significant after controlling for Raven's (d varying from 0.26 to 1.03). The absence of spatial advantage in athletes suggests that effects of sports on cognition are complex: spatial ability facet-specific, sport-specific, professional and intensity level-specific. Moreover, these effects might be confounded by differences in academic engagement, investment of effort and psychological and physiological effects of intensive sports engagement.

Jackson Jr. and Winston (2021) claim there is no taboo against research on race and intelligence. We argue, to the contrary, that there is a taboo against such research. We begin by addressing Jackson Jr. and Winston's arguments concerning expert surveys, as well as their criticisms of our published database of controversies. We then provide additional evidence of the taboo, and explain why it would be expected given the features of academic social science in Western countries. We also point out that Jackson Jr. and Winston's paper exemplifies the very taboo they claim does not exist.

The rise of large-scale collaborative panel studies has generated a need for fast, reliable, and valid assessments of cognitive abilities. In these studies, a detailed characterization of participants' cognitive abilities is often unnecessary, leading to the selection of tests based on convenience, duration, and feasibility. This often results in the use of abbreviated measures or proxies, potentially compromising their reliability and validity. Here we evaluate the mini-q (Baudson & Preckel, 2016), a three-minute speeded reasoning test, as a brief assessment of general cognitive abilities. The mini-q exhibited excellent reliability (0.96–0.99) and a substantial correlation with general cognitive abilities measured with a comprehensive test battery (r = 0.57; age-corrected r = 0.50), supporting its potential as a brief screening of cognitive abilities. Working memory capacity accounted for the majority (54%) of the association between test performance and general cognitive abilities, whereas individual differences in processing speed did not contribute to this relationship. Our results support the notion that the mini-q can be used as a brief, reliable, and valid assessment of general cognitive abilities. We therefore developed a computer-based version, ensuring its adaptability for large-scale panel studies. The paper- and computer-based versions demonstrated scalar measurement invariance and can therefore be used interchangeably. We provide norm data for young (18 to 30 years) and middle-aged (31 to 60 years) adults and provide recommendations for incorporating the mini-q in panel studies. Additionally, we address potential challenges stemming from language diversity, wide age ranges, and online testing in such studies.