Intelligence最新文献

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.

Research with cognitive training for older adults has largely shown that benefits are confined to the skills that are directly practiced with little or no generalization (or “transfer”) to other skills. However, investigations typically rely on pre-post designs in which the effects of training on non-practiced skills can only be revealed in the initial encounter with the novel task after training. The principle of mutualism suggests that growth in one cognitive skill may potentiate plasticity in related skills, such that transfer may only emerge with practice on the novel skill. We introduce a successive enrichment paradigm in which learning on a target skill (here, working memory (WM)) is examined as a function of earlier training experiences. Older adults were randomly assigned to one of four groups who trained on different combinations of tasks before training on a verbal WM task. Practice with any combination of WM tasks accelerated learning of the target task relative to a verbal decision speed control. Furthermore, those who first practiced multiple WM span tasks that were different from the target task showed larger pre- to posttest gain on the target WM task relative to those with prior exposure to only one different WM task or even the exact same WM task as the target. However, these effects only emerged with practice on the novel task. These data provide support for the mutualism principle — a conceptualization of transfer that can explain the emergence of the positive manifold of cognitive abilities, and offers promise for new pathways to promote late-life cognitive health.

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.

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.

In a recent study (N = 1075), Dunkel et al. (2023) concluded that maternal supportiveness is important for children's general intelligence. Maternal supportiveness was measured at ages 14, 24, and 36 months while children's intelligence was measured at ages 14, 24, and 36 months and at 4 and 10 years. The effects of maternal supportiveness at time T (β = 0.12), of maternal supportiveness at time T + 1 (β = 0.08), and of the child's intelligence at T + 1 (β = 0.49) on the child's intelligence at T + 2, were all positive and statistically significant when adjusting for one another. However, it is known that such adjusted cross-lagged effects may be biased due to residual confounding and regression to the mean. In the present study, we fitted various models, including latent change score models, on data simulating the data used by Dunkel et al. We found discrepant effects. For example, a positive effect of supportiveness on subsequent increase in children's intelligence (β = 0.04) was accounted for by maternal intelligence (β = 0.01 after adjustment). Another effect indicated that low supportiveness may compensate for having a mother with low intelligence and allow children to achieve the same intelligence as children to more intelligent and supportive mothers (β = 0.34). These divergent findings suggested that it may be premature to assume an increasing effect of maternal supportiveness on children's intelligence. It is important for researchers to bear in mind that correlations, also in superficially more advanced forms like cross-lagged effects, do not prove causality.

This study investigates neural mechanisms of divergent and convergent thinking in the verbal knowledge domain while taking into account activation related to working memory (WM). Divergent thinking was assessed using the Alternate Uses Task (AUT) and convergent thinking using the Compound Remote Associates task (RAT). We analyzed upper alpha band (10–12 Hz) oscillatory activity, in which we accounted for the temporal dynamics of both thinking processes by investigating three different time points during each trial for both tasks. We subtracted WM-related oscillatory activity measured by a serial recall task within the same knowledge domain and by using highly similar stimulus material as in both divergent and convergent thinking tasks. Our results show a strong upper alpha synchronization during divergent relative to convergent thinking, most pronounced at fronto-parietal electrodes. Moreover, we observed highest synchronization towards the middle (in contrast to the beginning and end) of each trial during both thinking processes. The results of the present study extend previous findings in the visuo-spatial knowledge domain, using a highly similar analytical approach to investigate divergent and convergent thinking. Together, these findings provide theoretical implications on how divergent and convergent thinking interact beyond WM across different knowledge domains by emphasizing their complex interplay.

Network models have become a popular alternative to factor models for analysing the phenotypic relationships among cognitive abilities. Studies have begun to compare these models directly to one another using cognitive ability data, although such a comparison has so far not extended to genetics. Our aim with this study was therefore to compare factor and network models of cognitive abilities first at a phenotypic level and then at a genetic level. We analyzed data from the Twins Early Development Study that were collected using 14 cognitive ability measures from 11,290 twins in the UK aged 12 years old. We conducted phenotypic and genetic analyses in which numerous factor and network models were tested, including a novel network twin model. Factor and network models both provided useful representations of the phenotypic and genetic relationships among cognitive abilities. Surprisingly, several relationships among cognitive abilities within the genetic networks were negative, which suggests that these cognitive abilities might share some genetic variants with inverse effects, although more research is currently needed to confirm this. Implications for future genomic research are discussed.

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.

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.

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.