Intelligence最新文献

Individual differences in general retrieval ability in semantic and autobiographical fluency tasks were examined in the current study. Participants performed multiple fluency tasks requiring retrieval from semantic memory, autobiographical semantic memory, and autobiographical memory. Participants also completed multiple measures of working memory and vocabulary. Confirmatory factor analysis suggested that three distinct, but correlated fluency factors (semantic memory, autobiographical semantic memory, and autobiographical memory) best accounted for the data. These factors loaded onto a higher-order general retrieval factor. Working memory was correlated with the semantic, autobiographical semantic, and the higher-order general retrieval factor. Vocabulary correlated positively with semantic memory, but negatively autobiographical semantic memory, and not significantly with the higher-order factor. These results suggest there is a general retrieval ability that cuts across semantic and autobiographical fluency tasks and this general retrieval factor is correlated with working memory, but not necessarily with vocabulary. These results provide important information on the nature of individual differences in general retrieval ability.

Children's differences in cognitive development stem from the complex interplay of genetic and environmental factors. Identifying gene-environment interactions in cognitive development is key for effectively targeting interventions that improve children's life chances. The advent of polygenic scores, which aggregate DNA variants to index a person's genetic propensities for phenotypic development, has created unprecedented opportunities for pinpointing gene-environment interactions. Yet, the issue of statistical power – the probability of detecting a true effect – prevails, and no replicable gene-environment interactions in child cognitive development have been reported. In this review article, we recapitulate three approaches to studying gene-environment interactions, including twin studies, candidate gene models, and polygenic score methods. We then discuss the issue of statistical power in gene-environment interaction research and conclude that larger samples are key to ushering a new era of replicable gene-environment interaction findings.

We compare the validity of personnel selection measures and novel tests of attention control for explaining individual differences in synthetic work performance, which required participants to monitor and complete multiple ongoing tasks. In Study 1, an online sample of young adults (N = 474, aged 18–35) based in the United States completed three-minute tests of attention control and two tests that primarily measure acquired knowledge, the Wonderlic and the Armed Forces Qualification Test (AFQT). Structural equation modeling revealed that acquired knowledge tests did not predict simulated work performance beyond attention control, whereas attention control did predict simulated work performance controlling for other measures. In Study 2, an in-lab sample of young adults from Georgia Tech and the greater Atlanta community (N = 321, aged 18–35) completed tests of attention control, processing speed, working memory capacity, and versions of two U.S. Military selection tests, one assessing acquired knowledge (the AFQT) and one assessing psychomotor ability (the Performance-Based Measures assessment from the Aviation Selection Test Battery). Structural equation modeling revealed that attention control fully mediated the relationship between the Performance Based Measures and simulated work performance, but the AFQT and processing speed retained unique prediction. We also explore possible gender differences. Collectively, these results suggest that tests of attention control may be a useful supplement to existing personnel selection measures when complex cognitive tasks are the criterion variable of interest.

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.

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.

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.

Achieving a widely accepted definition of human intelligence has been challenging, a situation mirrored by the diverse definitions of artificial intelligence in computer science. By critically examining published definitions, highlighting both consistencies and inconsistencies, this paper proposes a refined nomenclature that harmonizes conceptualizations across the two disciplines. Abstract and operational definitions for human and artificial intelligence are proposed that emphasize maximal capacity for completing novel goals successfully through respective perceptual-cognitive and computational processes. Additionally, support for considering intelligence, both human and artificial, as consistent with a multidimensional model of capabilities is provided. The implications of current practices in artificial intelligence training and testing are also described, as they can be expected to lead to artificial achievement or expertise rather than artificial intelligence. Paralleling psychometrics, ‘AI metrics’ is suggested as a needed computer science discipline that acknowledges the importance of test reliability and validity, as well as standardized measurement procedures in artificial system evaluations. Drawing parallels with human general intelligence, artificial general intelligence (AGI) is described as a reflection of the shared variance in artificial system performances. We conclude that current evidence more greatly supports the observation of artificial achievement and expertise over artificial intelligence. However, interdisciplinary collaborations, based on common understandings of the nature of intelligence, as well as sound measurement practices, could facilitate scientific innovations that help bridge the gap between artificial and human-like intelligence.

Intelligence is correlated with a range of left-wing and liberal political beliefs. This may suggest intelligence directly alters our political views. Alternatively, the association may be confounded or mediated by socioeconomic and environmental factors. We studied the effect of intelligence within a sample of over 300 biological and adoptive families, using both measured IQ and polygenic scores for cognitive performance and educational attainment. We found both IQ and polygenic scores significantly predicted all six of our political scales. Polygenic scores predicted social liberalism and lower authoritarianism, within-families. Intelligence was able to significantly predict social liberalism and lower authoritarianism, within families, even after controlling for socioeconomic variables. Our findings may provide the strongest causal inference to date of intelligence directly affecting political beliefs.

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.

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.