Intelligence最新文献

Gignac and Zajenkowski (2020) find that “the degree to which people mispredicted their objectively measured intelligence was equal across the whole spectrum of objectively measured intelligence”. This Comment shows that Gignac and Zajenkowski (2020) finding of homoscedasticity is likely the result of a recoding choice by the experimenters and does not in fact indicate that the Dunning-Kruger Effect is a mere statistical artifact. Specifically, Gignac and Zajenkowski (2020) recoded test subjects' responses to a question regarding self-assessed comparative IQ onto a linear IQ scale when a normal IQ scale would likely have been more appropriate. More generally, researchers studying self-assessed intelligence should be aware of potential measurement problems that may arise when transforming an ordinal scale onto an interval scale.

Flynn argued that the Flynn effect was due to an increasing use of “scientific spectacles” among the general population (Flynn, 2010), yet the Flynn effect itself has been viewed through clouded scientific spectacles. Most research has focused on Flynn's main finding: IQ scores have increased over time. Flynn (1987) presumed the effect was a cohort (generational) effect, yet a variety of within- and between-person processes could give rise to the observed secular changes. Many theories have been put forth as to the fundamental cause of the Flynn effect. Frequently ignored is what a specific cause implies the Flynn effect would look like at different levels of analysis and in the context of different research designs. In this paper we present two ‘lenses’ with which to view a potential causal model of the Flynn effect, in the hopes of closing some of the current gaps emerging from past research. First, we propose an examination of within- versus between-person processes. Relatedly, we propose that the exclusive focus on normed measures of intelligence has hampered understanding of what functional form the Flynn effect might take, particularly during development. Consideration of “raw” intelligence is likely to be fruitful. For our second lens, we consider the framework of age-period-cohort modeling to categorize what kind of effect a given model implies. We examine several causal theories of the Flynn effect through these lenses. Viewed through our lenses, we find that certain causal theories are, perhaps, somewhat incomplete in their specification of all the relevant processes.

Intelligence, as a general cognitive ability, shows a substantial inter-subject variation. Because of its impact on our lives, there is great interest in explaining the neural substrates of these differences. We used a large set of neuroimaging and behavioral data from 805 subjects, provided by the Human Connectome Project, and applied predictive models based on elastic-net regression using functional connectivity and brain morphometric data to predict general intelligence values. Additionally, we explored the impact of brain spatial distribution of the input connectivity data in the regression model using two strategies: brain parcellation and individual components. Features derived from functional connectivity were considerably more correlated with general intelligence than cortical thickness and surface area. Considering the regularization terms in this particular prediction problem, the best performances were obtained when the impact of all the independent variables was considered in the regresion, i.e. null LASSO sparsity term. Using different parcellation schemes affected predictive performances, which indicates spatial heterogeneity in the regression. We were able to explain 17,5% of the general intelligence variance, in the best performance reached, with a brain parcellation of 25 independent components; by other hand, using only cortical morphometric features the performance reduced to 1,6% for both cortical thickness and surface area. While no component, in particular, was responsible for predicting a large portion of the variance, the spatial components with the best results comprehend parietal, frontal and occipital regions, in agreement with the Parieto-Frontal Integration Theory (P-FIT).

The aim of the study is to estimate the most recent trends of intelligence world-wide. We find that the most recent studies report mainly positive Flynn effects in economically less developed countries, but trivial and frequently negative Flynn effects in the economically most advanced countries. This is confirmed by an analysis of 48 countries in the 2000–2018 PISA tests, showing that high pre-existing IQ and school achievement are the best predictors of declining test scores. IQ gaps between countries are still large (e.g., 19 IQ points in PISA between East Asia and South Asia) but are diminishing world-wide. We predict that these trends, observed in adolescents today, will reduce cognitive gaps between the working-age populations of countries and world regions during coming decades. As is predicted by the well-established relationship between intelligence and economic growth, there is already evidence that the ongoing cognitive convergence is paralleled by global economic convergence. These developments raise questions as to how long this cognitive and economic convergence will continue, whether it will eliminate cognitive and economic gaps between countries entirely, and whether a condition with high levels of cognitive ability and economic prosperity is sustainable long-term.

Whether there is an association between intelligence and face processing ability (i.e., face detection, face perception and face memory) is contentious, with some suggesting a moderate, positive association and others contending there is no meaningful association. The inconsistent results may be due to sample size differences, as well as variability in the quality of intelligence measures administered. The establishment of a moderate, positive correlation between face processing and intelligence would suggest it may be integrated within the Cattell-Horn-Carroll model of intelligence. Additionally, developmental prosopagnosia, a specific impairment of the recognition of facial identity, may be assessable in a manner similar to a learning disability. Consequently, we employed a psychometric meta-analytic approach to estimate the true score correlation between intelligence and face processing ability. Intelligence was positively and significantly correlated with face detection (r’ = 0.20; k = 2, N = 407), face perception (r’ = 0.42, k = 11, N = 2528), and face memory (r’ = 0.26, k = 23, N = 9062). Additionally, intelligence measurement quality moderated positively and significantly the association between intelligence and face memory (β = 0.08). On the basis of both theoretical and empirical considerations, we interpreted the results to suggest that face processing ability may be plausibly conceptualised within the Cattell-Horn-Carroll model of intelligence, in a manner similar to other relatively narrow dimensions of cognitive ability, i.e., associated positively with intelligence, but also distinct (e.g., reading comprehension). Potential clinical implications for the assessment of developmental prosopagnosia are also discussed.

As applied to general intelligence, the Dunning-Kruger effect (DK) is the phenomenon in which individuals at the lower end of the intellectual ability distribution are more likely to overestimate their intelligence. In a recent article in Intelligence it was suggested that the DK is primarily a statistical artifact and, indeed, the application of more appropriate analyses led to a failure to replicate a significant effect. When some of the limitations (namely sample representativeness) were addressed and the more appropriate statistical methods were used in the current study, our analyses illustrated a statistically significant DK effect. However, the magnitude of the effect was minimal; bringing its meaningfulness into question. In conclusion, it is recommended that the conditions that result in a significant DK be further explored.

In STEM fields other than biological sciences (math-intensive STEM), there is a greater ratio of males to females (M:F ratio) than that of the general population. The Ability Distribution Hypothesis suggests that this is mainly due to greater male variance in g. Others hypothesize that this is due to sex differences in occupational interests. There has not been an empirical study to evaluate which kind of differences has greater effects on the M:F ratios in math-intensive STEM fields. To fill the gap, we examine the test scores, application for majors and final admissions in a complete dataset of college entrance. We study the M:F ratios of four math-intensive STEM majors: Economics, Engineering, Computer Science and Physical sciences and Math. In summary, we find that greater male variance exists in total test scores; greater male variance partially explains the female underrepresentation in the upper tails of total test scores; men appear to have stronger interests in Engineering and Computer Science than women, while women appear to have stronger interests in Economics and to a lesser extent Physical sciences and Math than men; compared to sex differences in test scores, sex differences in major-choosing appear to have stronger effects on the M:F ratios of math-intensive STEM majors.

Intelligence and complex problem solving (CPS) correlate closely, but little is known about the mechanism that translates intelligence into successful CPS. Therefore, this study considered the strategic exploration behaviors VOTAT (vary-one-thing-at-a-time) and NOTAT (vary no-thing-at-a-time) as possible mediators. A sample of 495 high-school students worked on nine CPS tasks, six of which with solely direct effects and three with direct and eigendynamic effects. We expected substantial mediation effects if the applied strategic behaviors were optimal to identify the particular underlying effect types (i.e., direct effects: VOTAT; direct and eigendynamic effects: VOTAT and NOTAT). The model for tasks with only direct effects revealed VOTAT and NOTAT to be substantial mediators: Whereas VOTAT showed substantial positive relations to intelligence and CPS performance, NOTAT unexpectedly showed substantial negative relations. Both VOTAT and NOTAT resulted in significant indirect mediation effects. The model for tasks with direct and eigendynamic effects showed substantial positive relations of VOTAT and NOTAT to intelligence and CPS-performance and resulted in significant and positive indirect mediation effects. Moreover, the indirect effects differed between VOTAT and NOTAT and across the two facets of CPS performance. Overall, strategic exploration behaviors are relevant for explaining the g-CPS-relation.

Around one third of people across populations hold beliefs in epistemically unwarranted claims and theories. Why this effect is so strong remains elusive. In three studies (total N = 827), we clarified the relationships of fluid reasoning ability, analytic thinking style (indexed by non-intuitiveness and open-mindedness), and unwarranted beliefs in pseudoscience, paranormal phenomena, and conspiracy theories. Fluid reasoning predicted about 11% of variance in rejection of pseudoscience, but only 4% – in paranormal beliefs, and less than 2.5% – in conspiracist beliefs. By contrast, analytic thinking substantially predicted rejection of all the three kinds of beliefs, explaining 37% variance in pseudoscience and around 20% variance in paranormal and conspiracist beliefs. A novel finding indicated that fluid reasoning and analytic thinking predicted rejection of pseudoscience in an over-additive interaction. Fluid reasoning and analytic thinking explained the common variance shared by unwarranted beliefs, but not the belief-specific variance. Their relationships with unwarranted beliefs were stronger for males than for females. Overall, the three studies suggest that analytic thinking is more important than cognitive ability for adopting epistemically supported world-view.