Journal of Intelligence

23 pages, 846 KiB

Open AccessEditor’s ChoiceArticle

In Search of the Executive Cognitive Processes Proposed by Process-Overlap Theory

by Gidon T. Frischkorn and Claudia C. von Bastian

J. Intell. 2021, 9(3), 43; https://doi.org/10.3390/jintelligence9030043 - 19 Aug 2021

Cited by 9 | Viewed by 4503

Process-Overlap Theory (POT) suggests that measures of cognitive abilities sample from sets of independent cognitive processes. These cognitive processes can be separated into domain-general executive processes, sampled by the majority of cognitive ability measures, and domain-specific processes, sampled only by measures within a [...] Read more.

Process-Overlap Theory (POT) suggests that measures of cognitive abilities sample from sets of independent cognitive processes. These cognitive processes can be separated into domain-general executive processes, sampled by the majority of cognitive ability measures, and domain-specific processes, sampled only by measures within a certain domain. According to POT, fluid intelligence measures are related because different tests sample similar domain-general executive cognitive processes to some extent. Re-analyzing data from a study by De Simoni and von Bastian (2018), we assessed domain-general variance from executive processing tasks measuring inhibition, shifting, and efficiency of removal from working memory, as well as examined their relation to a domain-general factor extracted from fluid intelligence measures. The results showed that domain-general factors reflecting general processing speed were moderately and negatively correlated with the domain-general fluid intelligence factor (r = −.17–−.36). However, domain-general factors isolating variance specific to inhibition, shifting, and removal showed only small and inconsistent correlations with the domain-general fluid intelligence factor (r = .02–−.22). These findings suggest that (1) executive processing tasks sample only few domain-general executive processes also sampled by fluid intelligence measures, as well as (2) that domain-general speed of processing contributes more strongly to individual differences in fluid intelligence than do domain-general executive processes. Full article

(This article belongs to the Special Issue g and Its Underlying Executive Processes)

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21 pages, 3060 KiB

Open AccessEditor’s ChoiceArticle

Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners

by Ivan L. Simpson-Kent, Eiko I. Fried, Danyal Akarca, Silvana Mareva, Edward T. Bullmore, the CALM Team and Rogier A. Kievit

J. Intell. 2021, 9(2), 32; https://doi.org/10.3390/jintelligence9020032 - 15 Jun 2021

Cited by 13 | Viewed by 6196

Abstract

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific [...] Read more.

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain–behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5–18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role ‘between’ brain and behavior. We discuss implications and possible avenues for future studies. Full article

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7 pages, 245 KiB

Open AccessEditor’s ChoiceReview

Critical Thinking: A Model of Intelligence for Solving Real-World Problems

by Diane F. Halpern and Dana S. Dunn

J. Intell. 2021, 9(2), 22; https://doi.org/10.3390/jintelligence9020022 - 7 Apr 2021

Cited by 30 | Viewed by 10021

Abstract

Most theories of intelligence do not directly address the question of whether people with high intelligence can successfully solve real world problems. A high IQ is correlated with many important outcomes (e.g., academic prominence, reduced crime), but it does not protect against cognitive [...] Read more.

Most theories of intelligence do not directly address the question of whether people with high intelligence can successfully solve real world problems. A high IQ is correlated with many important outcomes (e.g., academic prominence, reduced crime), but it does not protect against cognitive biases, partisan thinking, reactance, or confirmation bias, among others. There are several newer theories that directly address the question about solving real-world problems. Prominent among them is Sternberg’s adaptive intelligence with “adaptation to the environment” as the central premise, a construct that does not exist on standardized IQ tests. Similarly, some scholars argue that standardized tests of intelligence are not measures of rational thought—the sort of skill/ability that would be needed to address complex real-world problems. Other investigators advocate for critical thinking as a model of intelligence specifically designed for addressing real-world problems. Yes, intelligence (i.e., critical thinking) can be enhanced and used for solving a real-world problem such as COVID-19, which we use as an example of contemporary problems that need a new approach. Full article

(This article belongs to the Special Issue How Intelligence Can Be a Solution to Consequential World Problems)

15 pages, 1207 KiB

Open AccessEditor’s ChoiceArticle

The Relationship between Theory of Mind and Intelligence: A Formative g Approach

by Ester Navarro, Sara Anne Goring and Andrew R. A. Conway

J. Intell. 2021, 9(1), 11; https://doi.org/10.3390/jintelligence9010011 - 19 Feb 2021

Cited by 10 | Viewed by 4607

Abstract

Theory of Mind (ToM) is the ability understand that other people’s mental states may be different from one’s own. Psychometric models have shown that individual differences in ToM can largely be attributed to general intelligence (g) (Coyle et al. 2018). Most [...] Read more.

Theory of Mind (ToM) is the ability understand that other people’s mental states may be different from one’s own. Psychometric models have shown that individual differences in ToM can largely be attributed to general intelligence (g) (Coyle et al. 2018). Most psychometric models specify g as a reflective latent variable, which is interpreted as a general ability that plays a causal role in a broad range of cognitive tasks, including ToM tasks. However, an alternative approach is to specify g as a formative latent variable, that is, an overall index of cognitive ability that does not represent a psychological attribute (Kovacs and Conway 2016). Here we consider a formative g approach to the relationship between ToM and intelligence. First, we conducted an SEM with reflective g to test the hypothesis that ToM is largely accounted for by a general ability. Next, we conducted a model with formative g to determine whether the relationship between ToM and intelligence is influenced by domain-specific tasks. Finally, we conducted a redundancy analysis to examine the contribution of each g variable. Results suggest that the relationship between ToM and intelligence in this study was influenced by language-based tasks, rather than solely a general ability. Full article

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27 pages, 976 KiB

Open AccessEditor’s ChoiceArticle

Intelligence and Creativity: Mapping Constructs on the Space-Time Continuum

by Giovanni Emanuele Corazza and Todd Lubart

J. Intell. 2021, 9(1), 1; https://doi.org/10.3390/jintelligence9010001 - 30 Dec 2020

Cited by 40 | Viewed by 10378

Abstract

This theoretical article proposes a unified framework of analysis for the constructs of intelligence and creativity. General definitions for intelligence and creativity are provided, allowing fair comparisons between the two context-embedded constructs. A novel taxonomy is introduced to classify the contexts in which [...] Read more.

This theoretical article proposes a unified framework of analysis for the constructs of intelligence and creativity. General definitions for intelligence and creativity are provided, allowing fair comparisons between the two context-embedded constructs. A novel taxonomy is introduced to classify the contexts in which intelligent and/or creative behavior can be embedded, in terms of the tightness vs. looseness of the relevant conceptual space S and available time T. These two dimensions are used to form what is identified as the space-time continuum, containing four quadrants: tight space and tight time, loose space and tight time, tight space and loose time, loose space and loose time. The intelligence and creativity constructs can be mapped onto the four quadrants and found to overlap more or less, depending on the context characteristics. Measurement methodologies adapted to the four different quadrants are discussed. The article concludes with a discussion about future research directions based on the proposed theoretical framework, in terms of theories and hypotheses on intelligence and creativity, of eminent personalities and personality traits, as well as its consequences for developmental, educational, and professional environments. Full article

(This article belongs to the Special Issue Intelligence and Creativity)

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24 pages, 1188 KiB

Open AccessEditor’s ChoiceArticle

Effect Sizes, Power, and Biases in Intelligence Research: A Meta-Meta-Analysis

by Michèle B. Nuijten, Marcel A. L. M. van Assen, Hilde E. M. Augusteijn, Elise A. V. Crompvoets and Jelte M. Wicherts

J. Intell. 2020, 8(4), 36; https://doi.org/10.3390/jintelligence8040036 - 2 Oct 2020

Cited by 21 | Viewed by 8473

Abstract

In this meta-study, we analyzed 2442 effect sizes from 131 meta-analyses in intelligence research, published from 1984 to 2014, to estimate the average effect size, median power, and evidence for bias. We found that the average effect size in intelligence research was a [...] Read more.

In this meta-study, we analyzed 2442 effect sizes from 131 meta-analyses in intelligence research, published from 1984 to 2014, to estimate the average effect size, median power, and evidence for bias. We found that the average effect size in intelligence research was a Pearson’s correlation of 0.26, and the median sample size was 60. Furthermore, across primary studies, we found a median power of 11.9% to detect a small effect, 54.5% to detect a medium effect, and 93.9% to detect a large effect. We documented differences in average effect size and median estimated power between different types of intelligence studies (correlational studies, studies of group differences, experiments, toxicology, and behavior genetics). On average, across all meta-analyses (but not in every meta-analysis), we found evidence for small-study effects, potentially indicating publication bias and overestimated effects. We found no differences in small-study effects between different study types. We also found no convincing evidence for the decline effect, US effect, or citation bias across meta-analyses. We concluded that intelligence research does show signs of low power and publication bias, but that these problems seem less severe than in many other scientific fields. Full article

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10 pages, 1158 KiB

Open AccessEditor’s ChoiceCommentary

How to Compare Psychometric Factor and Network Models

by Kees-Jan Kan, Hannelies de Jonge, Han L. J. van der Maas, Stephen Z. Levine and Sacha Epskamp

J. Intell. 2020, 8(4), 35; https://doi.org/10.3390/jintelligence8040035 - 2 Oct 2020

Cited by 52 | Viewed by 10911

Abstract

In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he encouraged us to [...] Read more.

In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he encouraged us to write, shortly before his death. We first discuss the statistical procedure McFarland used, which involved structural equation modeling (SEM) in standard SEM software. Next, we evaluate the penta-factor model of intelligence. We conclude that (1) standard SEM software is not suitable for the comparison of psychometric networks with latent variable models, and (2) the penta-factor model of intelligence is only of limited value, as it is nonidentified. We conclude with a reanalysis of the Wechlser Adult Intelligence Scale data McFarland discussed and illustrate how network and latent variable models can be compared using the recently developed R package Psychonetrics. Of substantive theoretical interest, the results support a network interpretation of general intelligence. A novel empirical finding is that networks of intelligence replicate over standardization samples. Full article

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23 pages, 1499 KiB

Open AccessEditor’s ChoiceArticle

The Worst Performance Rule, or the Not-Best Performance Rule? Latent-Variable Analyses of Working Memory Capacity, Mind-Wandering Propensity, and Reaction Time

by Matthew S. Welhaf, Bridget A. Smeekens, Matt E. Meier, Paul J. Silvia, Thomas R. Kwapil and Michael J. Kane

J. Intell. 2020, 8(2), 25; https://doi.org/10.3390/jintelligence8020025 - 2 Jun 2020

Cited by 9 | Viewed by 5382

Abstract

The worst performance rule (WPR) is a robust empirical finding reflecting that people’s worst task performance shows numerically stronger correlations with cognitive ability than their average or best performance. However, recent meta-analytic work has proposed this be renamed the “not-best performance” rule because [...] Read more.

The worst performance rule (WPR) is a robust empirical finding reflecting that people’s worst task performance shows numerically stronger correlations with cognitive ability than their average or best performance. However, recent meta-analytic work has proposed this be renamed the “not-best performance” rule because mean and worst performance seem to predict cognitive ability to similar degrees, with both predicting ability better than best performance. We re-analyzed data from a previously published latent-variable study to test for worst vs. not-best performance across a variety of reaction time tasks in relation to two cognitive ability constructs: working memory capacity (WMC) and propensity for task-unrelated thought (TUT). Using two methods of assessing worst performance—ranked-binning and ex-Gaussian-modeling approaches—we found evidence for both the worst and not-best performance rules. WMC followed the not-best performance rule (correlating equivalently with mean and longest response times (RTs)) but TUT propensity followed the worst performance rule (correlating more strongly with longest RTs). Additionally, we created a mini-multiverse following different outlier exclusion rules to test the robustness of our findings; our findings remained stable across the different multiverse iterations. We provisionally conclude that the worst performance rule may only arise in relation to cognitive abilities closely linked to (failures of) sustained attention. Full article

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