Individual Differences In Intelligence Research Articles

The Convergence of Intelligence and Longevity

Humans are the most intelligent mammals, and the most long-lived. Intelligence and longevity are characteristics of our species and they probably evolved together. They are also individual traits; individual differences in intelligence, health and longevity are directly correlated. People who are cognitively well-endowed are more likely to possess a number of favorable physical attributes, including disease-resistance and longevity. The convergence of intelligence and longevity during evolution was inevitable. Something as extraordinary as the human brain can only have evolved in an organism whose physiological systems are highly reliable, efficient and coherent. The human brain takes about 30 years to achieve maturity, although a case may be made for 50 or 60 years. A brain that requires such a long period of development needs a strong supporting cast in the soma. The resilience and longevity of brain is reflected by the same qualities in soma, and several ideas have been put forth to explain the association. We propose that the genetic elements that engineered our efficient and durable brain have exercised similar effects on our somatic systems. Almost all of the genes that generate and maintain the human brain are also active in the periphery, endowing our tissues with the same efficiency and durability. The speed, flexibility and efficiency of neural networks are homologous with similar attributes that govern the behavior of gene networks for intelligence and longevity.

Exploring the relationship between visual inspection time task and intelligence in young children

Inspection time task (IT) indexes individual differences in perceptual discrimination speed and it is a reliable predictor of psychometric intelligence However, the reasons underlying the relationship between IT and intelligence are not clear, because few studies investigated factors shared by both of them. This study examined how performance on a modified version of the inspection time task relates to individual differences in attentional control and how this relation is affected by age. A total of 157 children from 7 through 18 years were tested in a visual inspection time task, a Go/no-go reaction time task, a letter-matching task, and the Wechsler Abbreviated Scale of Intelligence (WASI). Diffusion modeling showed that IT captures top-down sensory and attentional processes underlying the IT-IQ relation and that individual differences in drift rate of ECTs predict individual differences in intelligence. Therefore, IT and attention make unique contributions to the prediction of IQ variability.

Processing speed and executive attention as causes of intelligence.

Individual differences in processing speed and executive attention have both been proposed as explanations for individual differences in cognitive ability, particularly general and fluid intelligence (Engle et al., 1999; Kail & Salthouse, 1994). Both constructs have long intellectual histories in scientific psychology. This article attempts to describe the historical development of these constructs, particularly as they pertain to intelligence. It also aims to determine the degree to which speed and executive attention are theoretical competitors in explaining individual differences in intelligence. We suggest that attention is the more fundamental mechanism in explaining variation in human intelligence. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Few temporally distributed brain connectivity states predict human cognitive abilities

Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10 min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual's network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.

Working memory load affects intelligence test performance by reducing the strength of relational item bindings and impairing the filtering of irrelevant information

There is a broad consensus that individual differences in working memory capacity (WMC) are strongly related to individual differences in intelligence. However, correlational studies do not allow conclusions about the causal nature of the relationship between WMC and fluid intelligence. While research on the cognitive basis of intelligence typically assumes that simpler lower-level cognitive processes contribute to individual differences in higher-order reasoning processes, a reversed causality or a third variable giving rise to two intrinsically uncorrelated variables may exist. In two studies (n1 = 65, n2 = 113), we investigated the causal nature of the relationship between WMC and intelligence by assessing the experimental effect of working memory load on intelligence test performance. Moreover, we tested if the effect of working memory load on intelligence test performance increased under time constraints, as previous studies have shown that the association between the two constructs increases if intelligence tests are administered with a strict time limit. We show that working memory load impaired intelligence test performance, but that this experimental effect was not affected by time constraints, which suggests that the experimental manipulations of working memory capacity and processing time did not affect the same underlying cognitive process. Using a computational modeling approach, we demonstrated that external memory load affected both the building and maintenance of relational item bindings and the filtering of irrelevant information in working memory. Our results confirm that WMC causally contributes to higher-order reasoning processes. Moreover, they support the hypothesis that working memory capacity in general and the abilities to maintain arbitrary bindings and to disengage from irrelevant information in particular are intrinsically related to intelligence.

A multi-faceted role of dual-state dopamine signaling in working memory, attentional control, and intelligence.

Genetic evidence strongly suggests that individual differences in intelligence will not be reducible to a single dominant cause. However, some of those variations/changes may be traced to tractable, cohesive mechanisms. One such mechanism may be the balance of dopamine D1 (D1R) and D2 (D2R) receptors, which regulate intrinsic currents and synaptic transmission in frontal cortical regions. Here, we review evidence from human, animal, and computational studies that suggest that this balance (in density, activity state, and/or availability) is critical to the implementation of executive functions such as attention and working memory, both of which are principal contributors to variations in intelligence. D1 receptors dominate neural responding during stable periods of short-term memory maintenance (requiring attentional focus), while D2 receptors play a more specific role during periods of instability such as changing environmental or memory states (requiring attentional disengagement). Here we bridge these observations with known properties of human intelligence. Starting from theories of intelligence that place executive functions (e.g., working memory and attentional control) at its center, we propose that dual-state dopamine signaling might be a causal contributor to at least some of the variation in intelligence across individuals and its change by experiences/training. Although it is unlikely that such a mechanism can account for more than a modest portion of the total variance in intelligence, our proposal is consistent with an array of available evidence and has a high degree of explanatory value. We suggest future directions and specific empirical tests that can further elucidate these relationships.

Multimodal Brain Signal Complexity Predicts Human Intelligence.

Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven's Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.

Intelligence Process vs. Content and Academic Performance: A Trip through a House of Mirrors.

The main purpose of modern intelligence tests has been to predict individual differences in academic performance, first of children, then adolescents, and later extending to adults. From the earliest Binet-Simon scales to current times, most one-on-one omnibus intelligence assessments include both process subtests (e.g., memory, reasoning) and content subtests (e.g., vocabulary, information). As somewhat parallel developments, intelligence theorists have argued about the primacy of the process components or the content components reflecting intelligence, with many modern researchers proposing that process constructs like working memory are the fundamental determinant of individual differences in intelligence. To address whether there is an adequate basis for re-configuring intelligence assessments from content or mixed content and process measures to all-process measures, the question to be answered in this paper is whether intellectual process assessments are more or less valid predictors of academic success, in comparison to content measures. A brief review of the history of intelligence assessment is provided with respect to these issues, and a number of problems and limitations of process measures is discussed. In the final analysis, there is insufficient justification for using process-only measures to the exclusion of content measures, and the limited data available point to the idea that content-dominated measures are more highly predictive of academic success than are process measures.

Process-oriented intelligence research: A review from the cognitive perspective

Despite over a century of research on intelligence, the cognitive processes underlying intelligent behavior are still unclear. In this review, we summarize empirical results investigating the contribution of cognitive processes associated with working memory capacity, processing speed, and executive processes to intelligence differences. Specifically, we (a) evaluate how cognitive processes associated with the three different cognitive domains have been measured, and (b) how these processes are related to individual differences in intelligence. Consistently, this review illustrates that isolating single cognitive processes using average performance in cognitive tasks is hardly possible. Instead, formal models that implement theories of cognitive processes underlying performance in different cognitive tasks may provide more adequate indicators of single cognitive processes. Therefore, we outlined which models for working memory capacity, processing speed, and executive processes may provide more specific insights into cognitive processes associated with individual differences in intelligence. Finally, we discuss implications of a process-oriented intelligence research using cognitive measurement models for psychometric theories of intelligence and argue that a model-based approach might overcome validity problems of traditional intelligence theories.

Using resting thalamic connectivity to identify the relationship between Eysenck personality traits and intelligence in healthy adults

Personality refers to a set of relatively stable psychological characteristics of individuals and has been associated with intelligence. It is well known that the thalamus plays an important role in cognitive processes and personality traits, but the relationship between personality traits, thalamic function, and intelligence has rarely been directly explored. Hence, we investigated the relationship between Eysenck personality traits, resting-state functional connectivity (rsFC) of the thalamus, and intelligence in a large sample of healthy adults (N = 176). We found that the trait of psychoticism was negatively associated with intelligence. The high intelligence group showed significantly lower psychoticism and demonstrated enhanced thalamic connectivity to the amygdala, inferior parietal lobules, pallidum, medial superior/middle frontal gyrus, and precuneus. Furthermore, a mediation analysis indicated that the FC between the left thalamus and left amygdala significantly mediated the correlation between psychoticism and full IQ (FIQ). These findings suggest that intelligent people may be less prone to psychoticism. Meanwhile, thalamic rsFC may reflect individual differences in intelligence and play a key role in the relationship between personality traits and intellectual abilities.

Sir Francis Galton: A Historiographical Reassessment of British Psychology’s Eugenic Past, 1860–1940

Many people find it difficult to accept British psychology’s eugenic history. Much of the literature is unhelpful in its abstention from comprehensive and lucid accounts of the Galtonian eugenic origins of much published work on individual differences in intelligence and personality deemed as inherited, the multifaceted nature of eugenics itself, or the complexity of this legacy in the early 20th century. Despite some well researched accounts of highly specific aspects of eugenic influence, the wider picture often remains elusive. Yet as the implications of eugenics for studies of race, class, and the histories of institutions and academic disciplines are increasingly interrogated, this is ever more inappropriate. Its eugenic past forms part of the challenging history of British psychology. Though a single paper cannot address these issues in their entirety, this thematic reappraisal of Galton and his legacy in the early 20th century provides a crucial initial step.

Process differences as a function of test modifications: Construct validity of Raven's advanced progressive matrices under standard, abbreviated and/or speeded conditions – A meta-analysis

Historically, there has been substantial disagreement about the importance of speed vs. level in determining individual differences in intelligence – a disagreement that persists across various different modern assessment measures of intellectual abilities. The current investigation considers whether changes to the administration constraints (time limitations or speededness, and total test length) of the Raven's Advanced Progressive Matrices test – which has been identified as a measure highly saturated with general intelligence – results in differences to the underlying ability determinants of test performance. A review of empirical studies was conducted, where versions of Raven's Advanced Progressive Matrices Tests were administered under various time constraints and item lengths. Meta-analytic techniques were used to determine whether introducing speed constraints or shortening the length of the test changes the construct validity of the tests (as indicated by differences in convergent and discriminant correlations with other ability traits). The meta-analysis combined results from 142 studies composed of a total of 26,848 participants. Substantial differences were found for correlations of Raven's Advanced Progressive Matrices and Spatial Visualization (as large as ρ̂ = 0.26), Memory (as large as ρ̂ = 0.08), and Perceptual Speed (as large as ρ̂ = 0.34) abilities under speeded conditions and shorter test lengths. Examinees may draw on different strategies for test performance, that in turn, draw on different combinations of abilities, when the test is abbreviated or significant time constraints are introduced. Implications for using this test under different conditions are discussed.

Between-Group Mean Differences in Intelligence in the United States Are &amp;gt;0% Genetically Caused: Five Converging Lines of Evidence

Abstract The past 30 years of research in intelligence has produced a wealth of knowledge about the causes and consequences of differences in intelligence between individuals, and today mainstream opinion is that individual differences in intelligence are caused by both genetic and environmental influences. Much more contentious is the discussion over the cause of mean intelligence differences between racial or ethnic groups. In contrast to the general consensus that interindividual differences are both genetic and environmental in origin, some claim that mean intelligence differences between racial groups are completely environmental in origin, whereas others postulate a mix of genetic and environmental causes. In this article I discuss 5 lines of research that provide evidence that mean differences in intelligence between racial and ethnic groups are partially genetic. These lines of evidence are findings in support of Spearman’s hypothesis, consistent results from tests of measurement invariance across American racial groups, the mathematical relationship that exists for between-group and within-group sources of heritability, genomic data derived from genome-wide association studies of intelligence and polygenic scores applied to diverse samples, and admixture studies. I also discuss future potential lines of evidence regarding the causes of average group differences across racial groups. However, the data are not fully conclusive, and the exact degree to which genes influence intergroup mean differences in intelligence is not known. This discussion applies only to native English speakers born in the United States and not necessarily to any other human populations.

How much intelligence is there in artificial intelligence? A 2020 update

Schank (1980) wrote an editorial for Intelligence on “How much intelligence is there in artificial intelligence?”. In this paper, we revisit this question. We start with a short overview of modern AI and showcase some of the AI breakthroughs in the four decades since Schank’s paper. We follow with a description of the main techniques these AI breakthroughs were based upon, such as deep learning and reinforcement learning; two techniques that have deep roots in psychology. Next, we discuss how psychologically plausible AI is and could become given the modern breakthroughs in AI’s ability to learn. We then access the main question of how intelligent AI systems actually are. For example, are there AI systems that can solve human intelligence tests? We conclude that Shank's observation, that intelligence is all about generalization and that AI is not particularly good at this, has, so far, withstood the test of time. Finally, we consider what AI insights could mean for the study of individual differences in intelligence. We close with how AI can further Intelligence research and vice versa, and look forward to fruitful interactions in the future.

General Intelligence and Socioeconomic Status as Strong Predictors of Student Performance in Latin American Schools: Evidence From PISA Items

Numerous technical—scientific reports have demonstrated that student performance variability is linked to several factors, especially socioeconomic factors. For a century, differential psychology has shown that students’ socioeconomic level has little or no relevance in the explanation of student performance variation when the intellectual factor is considered. Here we present a study on a student samples (N = 1264) aged 13 to 16 yrs, enrolled in 32 schools from five Latin American countries (Argentina, Brazil, Chile, Colombia, and Peru). A short version of the PISA test (composed by 16 items) and five cognitive measures were administered, in addition to a socioeconomic questionnaire. Multilevel analysis (marginal models) indicated that general intelligence (g-factor) and socioeconomic school status were robust predictors, and the students’ socioeconomic status very little accounted for the variation in the PISA test. This study concludes that education policy must incorporate individual differences in intelligence, beyond socioeconomic variables, as an important predictor variable in student performance studies.

Using DNA to predict intelligence

The DNA revolution made it possible to use DNA to predict intelligence. We argue that this advance will transform intelligence research and society. Our paper has three objectives. First, we review how the DNA revolution has transformed the ability to predict individual differences in intelligence. Thousands of DNA variants have been identified that – aggregated into genome-wide polygenic scores (GPS) – account for more than 10% of the variance in phenotypic intelligence. The intelligence GPS is now one of the most powerful predictors in the behavioral sciences. Second, we consider the impact of GPS on intelligence research. The intelligence GPS can be added as a genetic predictor of intelligence to any study without the need to assess phenotypic intelligence. This feature will help export intelligence to many new areas of science. Also , the intelligence GPS will help to address complex questions in intelligence research, in particular how the gene-environment interplay affects the development of individual differences in intelligence. Third, we consider the societal impact of the intelligence GPS, focusing on DNA testing at birth, DNA testing before birth (e.g., embryo selection), and DNA testing before conception (e.g., DNA dating). The intelligence GPS represents a major scientific advance, and, like all scientific advances, it can be used for bad as well as good. We stress the need to maximize the considerable benefits and minimize the risks of our new ability to use DNA to predict intelligence.

Age-related nuances in knowledge assessment

Although crystallized intelligence (gc) is a prominent factor in contemporary theories of individual differences in intelligence, its structure and optimal measurement are elusive. Analogously to the personality trait hierarchy, we propose the following hierarchy of declarative fact knowledge as a key component of gc: a general fact knowledge factor at the apex, followed by broad knowledge areas (e.g., natural sciences, social sciences, humanities), knowledge domains (e.g., chemistry, law, art), and nuances. In most scientific contexts we are predominantly concerned with aggregate levels, but we argue that the sampling of knowledge items strongly affects distinctions at higher levels of the hierarchy. We illustrate the magnitude of item-level heterogeneity by predicting chronological age differences through knowledge differences at different levels of the hierarchy. Analyses were based on an online sample of 1629 participants between age 18 and 70 who completed 120 broadly sampled declarative knowledge items across twelve domains. The results of linear and elastic net regressions, respectively, demonstrated that the majority of the age variance was located at the item level, and the strength of the prediction decreased with increasing aggregation. Knowledge nuances seem to tap important variance that is not covered by aggregate scores (e.g., sum or factor scores) and that is useful in the prediction of age. In turn, these effects extend our understanding how knowledge is acquired and imparted. On a more general stance, to gain new insights into the nature of knowledge, its optimal measurement and psychometric representation, item and person sampling issues should be considered.

Predicting nonverbal intelligence level from resting-state connectivity: a neural networks approach

This article is devoted to the development of a model of an artificial neural network for predicting the level of nonverbal intelligence according to the EEG of the brain. Cognitive functioning relies on the synchronization between different brain structures. However, it is still unclear how individual differences in intelligence are related to the global characteristics of information transmission in brain networks. Resting-state functional connectivity studies show the association of patterns of interactions between brain regions from people and different levels of nonverbal intelligence. In this study, we present a process of development of a neural network model used to predict the level of nonverbal intelligence based on EEG data of the brain. We have developed a fully-connected neural network to predict the level of nonverbal intelligence.

The Effects of Intelligence and Personality on Performance in Simulated Interrogation Scenarios

The paper explores the relationships between individual differences in intelligence and personality and the ability to extract critical information (and identify missing but required information) from a suspect’s brief sheet (i.e. model formulation) and develop a suitable line of questioning (i.e. approach strategizing) in interrogation scenarios. We hypothesised that cognitive flexibility, emotion management, low need for closure and rapport would all be predictors of these abilities. Two hundred and seventy-four participants of different backgrounds were exposed to two interrogation scenarios to assess model formation and approach strategizing abilities, as well as intelligence and personality tests. Benchmarks for performance were measured against two experienced interrogators and two psychologists’ calibrated performance. In terms of overall performance, only rapport and cognitive flexibility were significant positive predictors. Whereas only rapport was a positive predictor of approach strategizing, both rapport and cognitive flexibility were positive predictors of model formation. In conclusion, the data from the early stage of our project suggests that the examined factors should be carefully considered when training and selecting optimal interrogators. Though previous research has identified a number of individual differences in intelligence and personality that are important in demanding law-enforcement contexts, ours is the first to explore them with respect to effective interrogator performance.

Correlates of intelligence via resting-state functional connectivity of the amygdala in healthy adults

Intelligence is a form of advanced cognition that includes reasoning, problem solving, pattern recognition, and establishing relationships among items. The amygdala plays an important role in cognitive processing, but the relationship between amygdalar function and intelligence has rarely been explored directly. Here, we investigated the relationship between resting-state functional connectivity (RSFC) of the amygdala and intelligence test performance in a large sample of healthy adults (N = 197). We found that two pairs of RSFCs were significantly increased in the high IQ group compared with that of the general IQ group. One of these RSFCs consisted of the right amygdala and the right superior parietal lobule, whereas the other RSFC consisted of the right amygdala and the left middle cingulum. In addition, we found that the brain regions in which the strength of RSFC significantly correlated with full IQ (FIQ) were mainly distributed in the parietal and limbic lobes. What’s more, a further mediation analysis indicated that the functional connectivity of the right amygdala and the right superior parietal lobule significantly mediated the correlation between comprehension and object assembly, whereas the functional connectivity of the right amygdala and the left middle cingulum mediated the association between similarities and digit symbol. These findings suggest that amygdalar RSFC may reflect individual differences in intelligence and mediate specific relationships among different intellectual abilities.