Journal Menu
► Journal MenuJournal of Intelligence — Open Access Journal
Journal of Intelligence (ISSN 2079-3200) is an international peer-reviewed open access journal on the study of human intelligence published quarterly online by MDPI.
- Open Access - free for readers, free publication for well-prepared manuscripts submitted in 2018.
- Rapid publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 28 days after submission; acceptance to publication is undertaken in 7.2 days (median values for papers published in this journal in 2017).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Latest Articles
Scoring Alternatives for Mental Speed Tests: Measurement Issues and Validity for Working Memory Capacity and the Attentional Blink Effect
▼
Figures
J. Intell. 2018, 6(4), 47; https://doi.org/10.3390/jintelligence6040047 - 15 October 2018
Abstract
Research suggests that the relation of mental speed with working memory capacity (WMC) depends on complexity and scoring methods of speed tasks and the type of task used to assess capacity limits in working memory. In the present study, we included conventional binding/updating
[...] Read more.
Research suggests that the relation of mental speed with working memory capacity (WMC) depends on complexity and scoring methods of speed tasks and the type of task used to assess capacity limits in working memory. In the present study, we included conventional binding/updating measures of WMC as well as rapid serial visual presentation paradigms. The latter allowed for a computation of the attentional blink (AB) effect that was argued to measure capacity limitations at the encoding stage of working memory. Mental speed was assessed with a set of tasks and scored by diverse methods, including response time (RT) based scores, as well as ex-Gaussian and diffusion model parameterization. Relations of latent factors were investigated using structure equation modeling techniques. RT-based scores of mental speed yielded substantial correlations with WMC but only weak relations with the AB effect, while WMC and the AB magnitude were independent. The strength of the speed-WMC relation was shown to depend on task type. Additionally, the increase in predictive validity across RT quantiles changed across task types, suggesting that the worst performance rule (WPR) depends on task characteristics. In contrast to the latter, relations of speed with the AB effect did not change across RT quantiles. Relations of the model parameters were consistently found for the ex-Gaussian tau parameter and the diffusion model drift rate. However, depending on task type, other parameters showed plausible relations as well. The finding that characteristics of mental speed tasks determined the overall strength of relations with WMC, the occurrence of a WPR effect, and the specific pattern of relations of model parameters, implies that mental speed tasks are not exchangeable measurement tools. In spite of reflecting a general factor of mental speed, different speed tasks possess different requirements, supporting the notion of mental speed as a hierarchical construct.
Full article
Figure 1
Bibliometric Keyword Analysis across Seventeen Years (2000–2016) of Intelligence Articles
►
Figures
J. Intell. 2018, 6(4), 46; https://doi.org/10.3390/jintelligence6040046 - 15 October 2018
Abstract
An article’s keywords are distinct because they represent what authors feel are the most important words in their papers. Combined, they can even shed light on which research topics in a field are popular (or less so). Here we conducted bibliometric keyword analyses
[...] Read more.
An article’s keywords are distinct because they represent what authors feel are the most important words in their papers. Combined, they can even shed light on which research topics in a field are popular (or less so). Here we conducted bibliometric keyword analyses of articles published in the journal, Intelligence (2000–2016). The article set comprised 916 keyword-containing papers. First, we analyzed frequencies to determine which keywords were most/least popular. Second, we analyzed Web of Science (WOS) citation counts for the articles listing each keyword and we ran regression analyses to examine the effect of keyword categories on citation counts. Third, we looked at how citation counts varied across time. For the frequency analysis, “g factor”, “psychometrics/statistics”, and “education” emerged as the keywords with the highest counts. Conversely, the WOS citation analysis showed that papers with the keywords “spatial ability”, “factor analysis”, and “executive function” had the highest mean citation values. We offer tentative explanations for the discrepant results across frequencies and citations. The analysis across time revealed several keywords that increased (or decreased) in frequency over 17 years. We end by discussing how bibliometric keyword analysis can detect research trends in the field, both now and in the past.
Full article
Figure 1
Non-g Factors Predict Educational and Occupational Criteria: More than g
►
Figures
J. Intell. 2018, 6(3), 43; https://doi.org/10.3390/jintelligence6030043 - 7 September 2018
Abstract
In a prior issue of the Journal of Intelligence, I argued that the most important scientific issue in intelligence research was to identify specific abilities with validity beyond g (i.e., variance common to mental tests) (Coyle, T.R. Predictive validity of non-g
[...] Read more.
In a prior issue of the Journal of Intelligence, I argued that the most important scientific issue in intelligence research was to identify specific abilities with validity beyond g (i.e., variance common to mental tests) (Coyle, T.R. Predictive validity of non-g residuals of tests: More than g. Journal of Intelligence 2014, 2, 21–25.). In this Special Issue, I review my research on specific abilities related to non-g factors. The non-g factors include specific math and verbal abilities based on standardized tests (SAT, ACT, PSAT, Armed Services Vocational Aptitude Battery). I focus on two non-g factors: (a) non-g residuals, obtained after removing g from tests, and (b) ability tilt, defined as within-subject differences between math and verbal scores, yielding math tilt (math > verbal) and verbal tilt (verbal > math). In general, math residuals and tilt positively predict STEM criteria (college majors, jobs, GPAs) and negatively predict humanities criteria, whereas verbal residuals and tilt show the opposite pattern. The paper concludes with suggestions for future research, with a focus on theories of non-g factors (e.g., investment theories, Spearman’s Law of Diminishing Returns, Cognitive Differentiation-Integration Effort Model) and a magnification model of non-g factors.
Full article
Figure 1
Bifactor Models for Predicting Criteria by General and Specific Factors: Problems of Nonidentifiability and Alternative Solutions
►
Figures
J. Intell. 2018, 6(3), 42; https://doi.org/10.3390/jintelligence6030042 - 7 September 2018
Abstract
The bifactor model is a widely applied model to analyze general and specific abilities. Extensions of bifactor models additionally include criterion variables. In such extended bifactor models, the general and specific factors can be correlated with criterion variables. Moreover, the influence of general
[...] Read more.
The bifactor model is a widely applied model to analyze general and specific abilities. Extensions of bifactor models additionally include criterion variables. In such extended bifactor models, the general and specific factors can be correlated with criterion variables. Moreover, the influence of general and specific factors on criterion variables can be scrutinized in latent multiple regression models that are built on bifactor measurement models. This study employs an extended bifactor model to predict mathematics and English grades by three facets of intelligence (number series, verbal analogies, and unfolding). We show that, if the observed variables do not differ in their loadings, extended bifactor models are not identified and not applicable. Moreover, we reveal that standard errors of regression weights in extended bifactor models can be very large and, thus, lead to invalid conclusions. A formal proof of the nonidentification is presented. Subsequently, we suggest alternative approaches for predicting criterion variables by general and specific factors. In particular, we illustrate how (1) composite ability factors can be defined in extended first-order factor models and (2) how bifactor(S-1) models can be applied. The differences between first-order factor models and bifactor(S-1) models for predicting criterion variables are discussed in detail and illustrated with the empirical example.
Full article
Figure 1a
How Specific Abilities Might Throw ‘g’ a Curve: An Idea on How to Capitalize on the Predictive Validity of Specific Cognitive Abilities
►
Figures
J. Intell. 2018, 6(3), 41; https://doi.org/10.3390/jintelligence6030041 - 7 September 2018
Abstract
School grades are still used by universities and employers for selection purposes. Thus, identifying determinants of school grades is important. Broadly, two predictor categories can be differentiated from an individual difference perspective: cognitive abilities and personality traits. Over time, evidence accumulated supporting the
[...] Read more.
School grades are still used by universities and employers for selection purposes. Thus, identifying determinants of school grades is important. Broadly, two predictor categories can be differentiated from an individual difference perspective: cognitive abilities and personality traits. Over time, evidence accumulated supporting the notion of the g-factor as the best single predictor of school grades. Specific abilities were shown to add little incremental validity. The current paper aims at reviving research on which cognitive abilities predict performance. Based on ideas of criterion contamination and deficiency as well as Spearman’s ability differentiation hypothesis, two mechanisms are suggested which both would lead to curvilinear relations between specific abilities and grades. While the data set provided for this special issue does not allow testing these mechanisms directly, we tested the idea of curvilinear relations. In particular, polynomial regressions were used. Machine learning was applied to identify the best fitting models in each of the subjects math, German, and English. In particular, we fitted polynomial models with varying degrees and evaluated their accuracy with a leave-one-out validation approach. The results show that tests of specific abilities slightly outperform the g-factor when curvilinearity is assumed. Possible theoretical explanations are discussed.
Full article
Figure 1
Aligning Predictor-Criterion Bandwidths: Specific Abilities as Predictors of Specific Performance
►
Figures
J. Intell. 2018, 6(3), 40; https://doi.org/10.3390/jintelligence6030040 - 7 September 2018
Abstract
The purpose of the current study is to compare the extent to which general and specific abilities predict academic performances that are also varied in breadth (i.e., general performance and specific performance). The general and specific constructs were assumed to vary only in
[...] Read more.
The purpose of the current study is to compare the extent to which general and specific abilities predict academic performances that are also varied in breadth (i.e., general performance and specific performance). The general and specific constructs were assumed to vary only in breadth, not order, and two data analytic approaches (i.e., structural equation modeling [SEM] and relative weights analysis) consistent with this theoretical assumption were compared. Conclusions regarding the relative importance of general and specific abilities differed based on data analytic approaches. The SEM approach identified general ability as the strongest and only significant predictor of general academic performance, with neither general nor specific abilities predicting any of the specific subject grade residuals. The relative weights analysis identified verbal reasoning as contributing more than general ability, or other specific abilities, to the explained variance in general academic performance. Verbal reasoning also contributed to most of the explained variance in each of the specific subject grades. These results do not provide support for the utility of predictor-criterion alignment, but they do provide evidence that both general and specific abilities can serve as useful predictors of performance.
Full article
Figure 1
The Great Debate: General Ability and Specific Abilities in the Prediction of Important Outcomes
►
Figures
J. Intell. 2018, 6(3), 39; https://doi.org/10.3390/jintelligence6030039 - 7 September 2018
Abstract
The relative value of specific versus general cognitive abilities for the prediction of practical outcomes has been debated since the inception of modern intelligence theorizing and testing. This editorial introduces a special issue dedicated to exploring this ongoing “great debate”. It provides an
[...] Read more.
The relative value of specific versus general cognitive abilities for the prediction of practical outcomes has been debated since the inception of modern intelligence theorizing and testing. This editorial introduces a special issue dedicated to exploring this ongoing “great debate”. It provides an overview of the debate, explains the motivation for the special issue and two types of submissions solicited, and briefly illustrates how differing conceptualizations of cognitive abilities demand different analytic strategies for predicting criteria, and that these different strategies can yield conflicting findings about the real-world importance of general versus specific abilities.
Full article
Figure 1
Multiple Intelligences in Teaching and Education: Lessons Learned from Neuroscience
►
Figures
J. Intell. 2018, 6(3), 38; https://doi.org/10.3390/jintelligence6030038 - 31 August 2018
Abstract
This brief paper summarizes a mixed method review of over 500 neuroscientific reports investigating the proposition that general intelligence (g or IQ) and multiple intelligences (MI) can be integrated based on common and unique neural systems. Extrapolated from this interpretation are five
[...] Read more.
This brief paper summarizes a mixed method review of over 500 neuroscientific reports investigating the proposition that general intelligence (g or IQ) and multiple intelligences (MI) can be integrated based on common and unique neural systems. Extrapolated from this interpretation are five principles that inform teaching and curriculum so that education can be strengths-based and personalized to promote academic achievement. This framework is proposed as a comprehensive model for a system of educational cognitive neuroscience that will serve the fields of neuroscience as well as educators. Five key principles identified are culture matters, every brain is unique—activate strengths, know thyself, embodied cognition/emotional rudder, and make it mean something.
Full article
Figure 1
Using Standardized Test Scores to Include General Cognitive Ability in Education Research and Policy
►
Figures
J. Intell. 2018, 6(3), 37; https://doi.org/10.3390/jintelligence6030037 - 2 August 2018
Abstract
In education research and education policy, much attention is paid to schools, curricula, and teachers, but little attention is paid to the characteristics of students. Differences in general cognitive ability (g) are often overlooked as a source of important variance among
[...] Read more.
In education research and education policy, much attention is paid to schools, curricula, and teachers, but little attention is paid to the characteristics of students. Differences in general cognitive ability (g) are often overlooked as a source of important variance among schools and in outcomes among students within schools. Standardized test scores such as the SAT and ACT are reasonably good proxies for g and are available for most incoming college students. Though the idea of g being important in education is quite old, we present contemporary evidence that colleges and universities in the United States vary considerably in the average cognitive ability of their students, which correlates strongly with other methods (including international methods) of ranking colleges. We also show that these g differences are reflected in the extent to which graduates of colleges are represented in various high-status and high-income occupations. Finally, we show how including individual-level measures of cognitive ability can substantially increase the statistical power of experiments designed to measure educational treatment effects. We conclude that education policy researchers should give more consideration to the concept of individual differences in cognitive ability as well as other factors.
Full article
Figure 1
A Misuse of IQ Scores: Using the Dual Discrepancy/Consistency Model for Identifying Specific Learning Disabilities
►
Figures
J. Intell. 2018, 6(3), 36; https://doi.org/10.3390/jintelligence6030036 - 1 August 2018
Abstract
The purpose of this article is to describe the origins of patterns of strengths and weaknesses (PSW) methods for identifying specific learning disabilities (SLD) and to provide a comprehensive review of the assumptions and evidence supporting the most commonly-used PSW method in the
[...] Read more.
The purpose of this article is to describe the origins of patterns of strengths and weaknesses (PSW) methods for identifying specific learning disabilities (SLD) and to provide a comprehensive review of the assumptions and evidence supporting the most commonly-used PSW method in the United States: Dual Discrepancy/Consistency (DD/C). Given their use in determining whether students have access to special education and related services, it is important that any method used to identify SLD have supporting evidence. A review of the DD/C evidence indicates it cannot currently be classified as an evidence-based method for identifying individuals with a SLD. We show that the DD/C method is unsound for three major reasons: (a) it requires test scores have properties that they fundamentally lack, (b) lack of experimental utility evidence supporting its use, and (c) evidence supporting the inability of the method to identify SLD accurately.
Full article
Figure 1
The Enriching Interplay between Openness and Interest: A Theoretical Elaboration of the OFCI Model and a First Empirical Test
►
Figures
J. Intell. 2018, 6(3), 35; https://doi.org/10.3390/jintelligence6030035 - 1 August 2018
Abstract
The Openness-Fluid-Crystallized-Intelligence (OFCI) model posits long-term relations between Openness and cognitive abilities and has been successfully tested with longitudinal data. However, research on the developmental interplay between cognitive abilities and personality exists only sparsely. The current paper focuses on a theoretical development of
[...] Read more.
The Openness-Fluid-Crystallized-Intelligence (OFCI) model posits long-term relations between Openness and cognitive abilities and has been successfully tested with longitudinal data. However, research on the developmental interplay between cognitive abilities and personality exists only sparsely. The current paper focuses on a theoretical development of the OFCI model which suggests micro-level mechanisms underlying the long-term development. Specifically, within-situation relations between Openness, interests, situational perception, cognitive abilities, and emotions are proposed to explain longitudinal relations between Openness and cognitive abilities. Using experience sampling, selected parts of this elaboration were empirically scrutinized in a first test of the proposed ideas. Openness and specific interest both varied substantially across situations and covaried systematically. In interaction with an indicator of fluid intelligence, this covariation was related to an indicator of crystallized intelligence. The paper contributes to theorizing the intertwined development of personality and cognitive abilities, and highlights the importance of within-situation research for explaining long-term development.
Full article
Figure 1
Cognitive Models in Intelligence Research: Advantages and Recommendations for Their Application
►
Figures
J. Intell. 2018, 6(3), 34; https://doi.org/10.3390/jintelligence6030034 - 17 July 2018
Abstract
Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions
[...] Read more.
Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions aimed at the enhancement of intelligence on mediating process parameters. Moreover, cognitive models provide an explicit theoretical formalization of theories regarding specific cognitive processes that may help in overcoming ambiguities in the interpretation of fuzzy verbal theories. In this paper, we give an overview of the advantages of cognitive modeling in intelligence research and present models in the domains of processing speed, working memory, and selective attention that may be of particular interest for intelligence research. Moreover, we provide guidelines for the application of cognitive models in intelligence research, including data collection, the evaluation of model fit, and statistical analyses.
Full article
Figure 1
Ability Tests Measure Personality, Personality Tests Measure Ability: Disentangling Construct and Method in Evaluating the Relationship between Personality and Ability
►
Figures
J. Intell. 2018, 6(3), 32; https://doi.org/10.3390/jintelligence6030032 - 10 July 2018
Abstract
Although personality and cognitive ability are separate (sets of) constructs, we argue and demonstrate in this article that their effects are difficult to tease apart, because personality affects the performance on cognitive tests and cognitive ability affects the item responses on personality assessments.
[...] Read more.
Although personality and cognitive ability are separate (sets of) constructs, we argue and demonstrate in this article that their effects are difficult to tease apart, because personality affects the performance on cognitive tests and cognitive ability affects the item responses on personality assessments. Cognitive ability is typically measured with tests of items with correct answers; personality is typically measured with rating-scale self-reports. Oftentimes conclusions regarding the personality–ability relationship have as much to do with measurement methods as with the construct similarities and differences. In this article, we review key issues that touch on the relationship between cognitive ability and personality. These include the construct-method distinction, sources of test score variance, the maximal vs. typical performance distinction, and the special role for motivation in low-stakes testing. We review a general response model for cognitive and personality tests that recognizes those sources of test score variance. We then review the approaches for measuring personality through performance (objective personality tests, grit game, coding speed, economic preferences, and confidence), test and survey behavior (survey effort, response time, and item position effects), and real-world behavior (study time, registration latency, behavior residue, and social media). We also discuss ability effects on personality tests, indicated by age and cognitive ability effects, anchoring vignette rating errors, and instructions to ‘fake good’. We conclude with a discussion of the implications for our understanding of personality and ability differences, and suggestions for integrating the fields.
Full article
Figure 1
The Dissociation between Adult Intelligence and Personality with Respect to Maltreatment Episodes and Externalizing Behaviors Occurring in Childhood
►
Figures
J. Intell. 2018, 6(3), 31; https://doi.org/10.3390/jintelligence6030031 - 9 July 2018
Abstract
Here we analyze the simultaneous relationships among five variables. Two refer to childhood (episodes of various forms of maltreatment and externalizing behaviors), whereas three refer to early adulthood (intelligence, personality, and socialization difficulties). The 120 individuals considered for the present report were invited
[...] Read more.
Here we analyze the simultaneous relationships among five variables. Two refer to childhood (episodes of various forms of maltreatment and externalizing behaviors), whereas three refer to early adulthood (intelligence, personality, and socialization difficulties). The 120 individuals considered for the present report were invited from the 650 schoolchildren participating in the Longitudinal Study of Intelligence and Personality (Minas Gerais, Brazil). The complete sample was recruited in 2002 (T1; mean age = 10.0; standard deviation (SD) = 2.2) and 120 were tested again in 2014-17 (T2; mean age = 23.5; SD = 2.2). Externalizing behaviors were registered at T1, whereas the remaining variables were obtained at T2. These were the main results: (1) externalizing behaviors predict future social effectiveness (as estimated by the general factor of personality derived from the NEO Personality Inventory-Revised (NEO-PI-R) and socialization difficulties computed from the socialization scale (SOC)) and future intelligence performance (as assessed by a set of fluid and crystallized tests); (2) episodes of self-reported childhood maltreatment predict social effectiveness, but not intelligence; (3) maltreatment and externalizing behaviors are unrelated; and (4) social effectiveness (personality) and intelligence are unrelated. Therefore, the findings support the dissociation between adult intelligence and personality with respect to maltreatment episodes and externalizing behaviors occurring in childhood. Implications of these findings for social policies aimed at preventing adult socially ineffective personalities are underscored.
Full article
Figure 1
The Relation of Personality and Intelligence—What Can the Brunswik Symmetry Principle Tell Us?
►
Figures
J. Intell. 2018, 6(3), 30; https://doi.org/10.3390/jintelligence6030030 - 3 July 2018
Abstract
Personality and intelligence are defined as hierarchical constructs, ranging from broad g-factors to (domain-)specific constructs. The present study investigated whether different combinations of hierarchical levels lead to different personality-intelligence correlations. Based on the integrative data analysis approach, we combined a total of
[...] Read more.
Personality and intelligence are defined as hierarchical constructs, ranging from broad g-factors to (domain-)specific constructs. The present study investigated whether different combinations of hierarchical levels lead to different personality-intelligence correlations. Based on the integrative data analysis approach, we combined a total of five data sets. The focus of the first study (N = 682) was an elaborated measurement of personality (NEO-PI-R), which was applied with a relatively short intelligence test (Intelligence Structure Test 2000 R). In the second study (N = 413), a comprehensive measurement of intelligence (Berlin Intelligence Structure test) was used with a shorter personality questionnaire (NEO-FFI). In line with the Brunswik symmetry principle, the findings emphasize that personality-intelligence correlations varied greatly across the hierarchical levels of constructs considered in the analysis. On average, Openness showed the largest relation with intelligence. We recommend for future studies to investigate personality-intelligence relations at more fine-grained levels based on elaborated measurements of both personality and intelligence.
Full article
Figure 1
Relationships between Personality and Cognitive Ability: A Facet-Level Analysis
►
Figures
J. Intell. 2018, 6(2), 28; https://doi.org/10.3390/jintelligence6020028 - 18 May 2018
Abstract
A growing body of research supports the notion that cognitive abilities and personality are systematically related. However, this research has focused largely on global personality dimensions and single—often equally global—markers of cognitive ability. The present study offers a more fine-grained perspective. Specifically, it
[...] Read more.
A growing body of research supports the notion that cognitive abilities and personality are systematically related. However, this research has focused largely on global personality dimensions and single—often equally global—markers of cognitive ability. The present study offers a more fine-grained perspective. Specifically, it is one of the first studies to comprehensively investigate the associations between both fluid and crystallized intelligence with Big Five personality domains as well as their facets. Based on a heterogeneous sample of the adult population in Germany (N = 365), our study yielded three key findings. First, personality was more strongly related to crystallized intelligence than to fluid intelligence. This applied both to the total variance explained and to the effect sizes of most of the Big Five domains and facets. Second, facets explained a larger share of variance in both crystallized and fluid intelligence than did domains. Third, the associations of different facets of the same domain with cognitive ability differed, often quite markedly. These differential associations may substantially reduce—or even suppress—the domain-level associations. Our findings clearly attest to the added value of a facet-level perspective on the personality–cognitive ability interface. We discuss how such a fine-grained perspective can further theoretical understanding and enhance prediction.
Full article
Figure 1
News
Special Issues
Special Issue in
J. Intell.
Intelligence in Education
Guest Editor: Tomoe KanayaDeadline: 1 December 2018
Special Issue in
J. Intell.
What Does the SAT Measure?
Guest Editor: Brenda HannonDeadline: 28 February 2019
Special Issue in
J. Intell.
Cognitive and Non-Cognitive Processing in Future-Focused Learning (FFL)
Guest Editors: Jihyun Lee, Lazar StankovDeadline: 31 March 2019