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Background: While the link between body growth indices in the first three years of life and neurodevelopment is well established, the relationship between these indicators and intelligence levels in later stages is not well understood. This study aimed to investigate the association between various anthropometric indices and Intelligence Quotient in early school-aged children., Methods: This cross-sectional study recruited healthy students aged 7 to 8 years from four primary schools in Tehran, Iran. Measurements included body weight, height, body mass index, arm circumference, waist circumference, and hip circumference. Intelligence Quotient was assessed using the Raven's Standard Progressive Matrices Test, with scores standardized by age according to the Raven's manual. Separate univariate linear regression analyses evaluated the association between each anthropometric index and children's Intelligence Quotient scores. Multivariate linear regression analyses were then performed to adjust for potential confounding factors, including the child's sex, age, and parental education levels., Results: A cohort of 160 children (80 females), with a mean age of 7.5 ± 0.6 years, was included in the study. The participants exhibited the following mean anthropometric values: weight 27.7 ± 6.5 kg, height 1.2 ± 0.1 m, body mass index 17.9 ± 3.4 kg/m 2 , arm circumference 20.7 ± 2.8 cm, waist circumference 56.4 ± 6.4 cm, and hip circumference 68.1 ± 6.6 cm. Their mean Intelligence Quotient score was 106 ± 16.6. In the univariate analysis, children's weight was not significantly associated with Intelligence Quotient score (P-value = 0.153, β = 0.288 [-0.108, 0.684]), while height was negatively associated (P-value = 0.048, β = -31.685 [-63.142, -0.228]) and body mass index showed a positive association (P-value = 0.001, β = 1.265 [0.524, 2.006]). Abdominal circumference was not significantly associated with Intelligence Quotient (P-value = 0.913, β = 0.051 [-0.870, 0.972]), but both waist circumference (P-value = 0.007, β = 0.542 [0.147, 0.937]) and hip circumference (P-value = 0.013, β = 0.484 [0.102, 0.866]) demonstrated significant positive associations with Intelligence Quotient. However, none of the anthropometric indices maintained statistically significant associations with Intelligence Quotient after adjusting for potential confounders (P-values > 0.05)., Conclusions: This study indicated no strong relationship between various body measurements and intelligence levels in school-aged children after adjusting the analyses for confounders. This suggests that intelligence in these children may be largely shaped by genetic and sociodemographic factors, with minimal influence from physical growth patterns., Competing Interests: Declarations. Ethics approval and consent to participate: All stages of this study were performed in studies involving human participants in accordance with the Declaration of Helsinki. Prior to enrollment, the study protocol was approved by the Institutional Review Board (IRB) and ethics committee of the Tehran University of Medical Sciences with the reference number IR.TUMS.1394.1016. Prior to participation, verbal and written informed consent from parents and verbal agreement from students were obtained, following a detailed presentation of the study's purpose and comprehensive explanations regarding the information sought in the questionnaire. Consent for publication: After explaining the purpose of the study, verbal and written informed consent was obtained from the parents of the students for the anonymous publication of their children’s data. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Extended testing time in Raven's Progressive Matrices (RPM) can lead to increased fatigue and reduced motivation, which may impair cognitive task performance. This study explores the application of artificial intelligence (AI) in RPM by combining eye-tracking technology with machine learning (ML) models, aiming to explore new methods for improving the efficiency of RPM testing and to identify the key metrics involved. Using eye-tracking metrics as features, ten ML models were trained, with the XGBoost model demonstrating superior performance. Notably, we further refined the period of interest and reduced the number of metrics, achieving strong performance, with accuracy, precision, and recall all above 0.8, using only 60% of the response time and nine eye-tracking metrics. This study also examines the role of several key metrics in RPM and offers valuable insights for future research.

Objective: The aim of this study was to create age-stratified norms for the Raven's Standard Progressive Matrices (SPM) for Sri Lankan adults., Methods: A sample of 610 adults (age: 18-72 years; education: 1-19 years), underwent the 60-item version of the SPM under individual supervision of a test administrator. The sample was stratified into 5-year age bands, and the norms are presented as percentile tables and percentile curves., Results: The age-related changes were more accurately predicted by a curvilinear model (overall R 2 = 0.961) than a linear regression model ( R 2 = 0.639). The SPM norms are presented as age-stratified percentile tables, as well as sex-, age- and education-adjusted multiple regression equations. The highest percentiles in the younger end of the age spectrum showed a ceiling effect. In the context of age-stratified US (1993) and British (1992) norms, older individuals in the Sri Lankan sample scored much lower than their Western counterparts. However, the difference narrowed in the younger age bands, showing no difference among the 18-to-22-year age bands in the three countries., Conclusions: This age-by-country interaction can be partly explained by poorer education in the older individuals in the present sample compared to those in the US and UK standardization samples. SPM norms presented in this paper fill a hiatus in assessment of general intellectual ability in Sri Lankan adults. Given that Sri Lanka improves its educational, socioeconomic and health standards faster than the nations who have already reached higher standards, these norms would require re-standardization in the coming decades.

Metacognition, the cognition about cognition, is closely linked to intelligence and therefore understanding the metacognitive processes underlying intelligence test performance, specifically on Raven's Progressive Matrices, could help advance the knowledge about intelligence. The measurement of metacognition, is often done using domain-general offline questionnaires or domain-specific online think-aloud protocols. This study aimed to investigate the relationship between metacognitive awareness and intelligence via the design and use of a novel Meta-Cognitive Awareness Scale - Domain Specific (MCAS-DS) that encourages reflection of task strategy processes. This domain-specific scale was first constructed to measure participants' awareness of their own metacognition linked to Raven's Progressive Matrices (SPM). Following discriminatory index and Exploratory Factor Analysis, a 15-item scale was derived. Exploratory Factor Analysis showed five factors: Awareness of Engagement in Self-Monitoring, Awareness of Own Ability, Awareness of Responding Speed/Time, Awareness of Alternative Solutions and Awareness of Requisite Problem-Solving Resources. The intelligence level of ninety-eight adults was then estimated using Raven's Standard Progressive Matrices. Participants also completed the MCAS-DS, and further items that examined their test-taking behavior and Confidence level. Metacognitive awareness was positively correlated to standardized IQ scores derived from the SPM whilst Over-Confidence derived using the Confidence level measure was negatively correlated to SPM. Despite some limitations, this study shows promise for elucidating the relationship between metacognitive awareness and intelligence using the task-specific scale., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Song, Loyal and Lond.)

Alzheimer's disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey-Osterrieth complex figure and Raven's progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD.

Observations abound about the power of visual imagery in human intelligence, from how Nobel prize-winning physicists make their discoveries to how children understand bedtime stories. These observations raise an important question for cognitive science, which is, what are the computations taking place in someone's mind when they use visual imagery? Answering this question is not easy and will require much continued research across the multiple disciplines of cognitive science. Here, we focus on a related and more circumscribed question from the perspective of artificial intelligence (AI): If you have an intelligent agent that uses visual imagery-based knowledge representations and reasoning operations, then what kinds of problem solving might be possible, and how would such problem solving work? We highlight recent progress in AI toward answering these questions in the domain of visuospatial reasoning, looking at a case study of how imagery-based artificial agents can solve visuospatial intelligence tests. In particular, we first examine several variations of imagery-based knowledge representations and problem-solving strategies that are sufficient for solving problems from the Raven's Progressive Matrices intelligence test. We then look at how artificial agents, instead of being designed manually by AI researchers, might learn portions of their own knowledge and reasoning procedures from experience, including learning visuospatial domain knowledge, learning and generalizing problem-solving strategies, and learning the actual definition of the task in the first place., Competing Interests: The author declares no competing interest.

Distractors might display discriminatory power with respect to the construct of interest (e.g., intelligence), which was shown in recent applications of nested logit models to the short-form of Raven's progressive matrices and other reasoning tests. In this vein, a simulation study was carried out to examine two effect size measures (i.e., a variant of Cohen's ω and the canonical correlation R CC ) for their potential to detect distractors with ability-related discriminatory power. The simulation design was adopted to item selection scenarios relying on rather small sample sizes (e.g., N = 100 or N = 200). Both suggested effect size measures (Cohen's ω only when based on two ability groups) yielded acceptable to conservative type-I-error rates, whereas, the canonical correlation outperformed Cohen's ω in terms of empirical power. The simulation results further suggest that an effect size threshold of 0.30 is more appropriate as compared to more lenient (0.10) or stricter thresholds (0.50). The suggested item-analysis procedure is illustrated with an analysis of twelve Raven's progressive matrices items in a sample of N = 499 participants. Finally, strategies for item selection for cognitive ability tests with the goal of scaling by means of nested logit models are discussed.

The present study investigated the psychometric properties of the Raven's Colored Progressive Matrices (CPM) test in a sample of preschoolers from Brazil ( n = 582; age: mean = 57 months, SD = 7 months; 46% female). We investigated the plausibility of unidimensionality of the items (confirmatory factor analysis) and differential item functioning (DIF) for sex and age (multiple indicators multiple causes method). We tested four unidimensional models and the one with the best-fit index was a reduced form of the Raven's CPM. The DIF analysis was carried out with the reduced form of the test. A few items presented DIF (two for sex and one for age), confirming that the Raven's CPM items are mostly measurement invariant. There was no effect of sex on the general factor, but increasing age was associated with higher values of the g factor. Future research should indicate if the reduced form is suitable for evaluating the general ability of preschoolers.

Background: Previous research has demonstrated an association of scores on a visual test of nonverbal reasoning, Raven's Progressive Matrices (RPM), with scores on open-set sentence recognition in quiet for adult cochlear implant (CI) users as well as for adults with normal hearing (NH) listening to noise-vocoded sentence materials. Moreover, in that study, CI users demonstrated poorer nonverbal reasoning when compared with NH peers. However, it remains unclear what underlying neurocognitive processes contributed to the association of nonverbal reasoning scores with sentence recognition, and to the poorer scores demonstrated by CI users., Objectives: Three hypotheses were tested: (1) nonverbal reasoning abilities of adult CI users and normal-hearing (NH) age-matched peers would be predicted by performance on more basic neurocognitive measures of working memory capacity, information-processing speed, inhibitory control, and concentration; (2) nonverbal reasoning would mediate the effects of more basic neurocognitive functions on sentence recognition in both groups; and (3) group differences in more basic neurocognitive functions would explain the group differences previously demonstrated in nonverbal reasoning., Method: Eighty-three participants (40 CI and 43 NH) underwent testing of sentence recognition using two sets of sentence materials: sentences produced by a single male talker (Harvard sentences) and high-variability sentences produced by multiple talkers (Perceptually Robust English Sentence Test Open-set, PRESTO). Participants also completed testing of nonverbal reasoning using a visual computerized RPM test, and additional neurocognitive assessments were collected using a visual Digit Span test and a Stroop Color-Word task. Multivariate regression analyses were performed to test our hypotheses while treating age as a covariate., Results: In the CI group, information processing speed on the Stroop task predicted RPM performance, and RPM scores mediated the effects of information processing speed on sentence recognition abilities for both Harvard and PRESTO sentences. In contrast, for the NH group, Stroop inhibitory control predicted RPM performance, and a trend was seen towards RPM scores mediating the effects of inhibitory control on sentence recognition, but only for PRESTO sentences. Poorer RPM performance in CI users than NH controls could be partially attributed to slower information processing speed., Conclusions: Neurocognitive functions contributed differentially to nonverbal reasoning performance in CI users as compared with NH peers, and nonverbal reasoning appeared to partially mediate the effects of these different neurocognitive functions on sentence recognition in both groups, at least for PRESTO sentences. Slower information processing speed accounted for poorer nonverbal reasoning scores in CI users. Thus, it may be that prolonged auditory deprivation contributes to cognitive decline through slower information processing., (© 2019 S. Karger AG, Basel.)

Purpose: Expertise is the product of training. Few studies have used functional connectivity or conventional diffusometric methods to identify neural underpinnings of chess expertise. Diffusometric variables of white matter might reflect these adaptive changes, along with changes in structural connectivity, which is a sensitive measure of microstructural changes. Method: Diffusometric variables of 29 professional chess players and 29 age-sex matched controls were extracted for white matter regions based on John Hopkin's Mori white matter atlas and partially correlated against professional training time and level of chess proficiency. Diffusion MRI connectometry was implemented to identify changes in structural connectivity in professional players compared to novices. Result: Compared to novices, higher planar anisotropy (CP) was observed in inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF) and cingulate gyrus, in professional chess players, which correlated with higher RPM score in this group. Higher fractional anisotropy (FA) was observed in ILF, uncinate fasciculus (UF) and hippocampus and correlated with better scores in Raven's progressive matrices (RPM) score and longer duration of chess training in professional players. Consistently, radial diffusivity in bilateral IFOF, bilateral ILF and bilateral SLF was inversely correlated with level of training in professional players. DMRI connectometry analysis identified increased connectivity in bilateral UF, bilateral IFOF, bilateral cingulum, and corpus callosum in chess player's compared to controls. Conclusion: Structural connectivity of major associational subcortical white matter fibers are increased in professional chess players. FA and CP of ILF, SLF and UF directly correlates with duration of professional training and RPM score, in professional chess players.

The acuity of reasoning on Raven's Progressive Matrices is strongly influenced by strategic determinants. Building on metamemory studies that highlight the influence of study-time allocation on memory development, we investigated children's allocation of study time while solving these matrices. A total of 170 children aged 6-12 years completed a computerized short-form version of the standard matrices featuring items selected to represent a broad range of difficulties. Beyond analyzing changes in mean latencies and performances with age, we used generalized additive mixed models to explore within-participant variability in response times as a function of both item complexity and overall individual efficiency. Results revealed that individual differences in performances were significantly associated with children's adaptive modulation of response times. Mediation analysis further indicated that response-time modulation contributed to age-related changes in performance. Taking account of study-time allocation in reasoning tasks may open up new avenues for the study of reasoning development and the assessment of intellectual functioning., Competing Interests: The authors declare no conflict of interest.

Background: Visuo-spatial and problem-solving abilities are commonly impaired in patients with Alzheimer's disease (AD). Conversely, subjects with amnestic mild cognitive impairment (aMCI) do not exhibit overt involvement of cognitive domains other than memory. Consequently, a detection of an impairment at the Raven's colored progressive matrices (RCPM) could be useful to discriminate aMCI from AD and to mark the progression from one condition to another., Aim of the Study: To describe the pattern of errors at RCPM in subjects suffering from AD as compared with that of aMCI., Methods: Fifteen patients with AD, 15 subjects with aMCI and 31 Healthy Controls (HC) received the RCPM. The errors were classified as: (1) difference (D); (2) inadequate individuation (II); (3) repetition of the pattern (RP); (4) incomplete correlation (IC)., Results: No difference approached significance between aMCI subjects and HC. AD patients always exhibited a higher number of errors as compared with HC. AD patients showed higher number of errors as compared with aMCI only on RP and IC errors., Conclusions: The results suggest that the visuo-spatial and problem-solving impairment that characterize AD, and probably subtend the progression from aMCI to dementia, do not affect to the same extent all cognitive dimensions explored by RCPM.

Wechsler Intelligence Scale for Children (WISC) is widely used to estimate autistic intelligence (Joseph in The neuropsychology of autism. Oxford University Press, Oxford, 2011; Goldstein et al. in Assessment of autism spectrum disorders. Guilford Press, New York, 2008; Mottron in J Autism Dev Disord 34(1):19-27, 2004). However, previous studies suggest that while WISC-III and Raven's Progressive Matrices (RPM) provide similar estimates of non-autistic intelligence, autistic children perform significantly better on RPM (Dawson et al. in Psychol Sci 18(8):657-662, doi: 10.1111/j.1467-9280.2007.01954.x , 2007). The latest WISC version introduces substantial changes in subtests and index scores; thus, we asked whether WISC-IV still underestimates autistic intelligence. Twenty-five autistic and 22 typical children completed WISC-IV and RPM. Autistic children's RPM scores were significantly higher than their WISC-IV FSIQ, but there was no significant difference in typical children. Further, autistic children showed a distinctively uneven WISC-IV index profile, with a "peak" in the new Perceptual Reasoning Index. In spite of major changes, WISC-IV FSIQ continues to underestimate autistic intelligence.

Previous work has suggested that the Raven's Progressive Matrices (RPM) are better suited for capturing the nature of intelligence for individuals with autism spectrum disorder (ASD) than the Wechsler scales. The RPM measures 'fluid intelligence', an area for which it has been argued that persons with ASD have a relative strength. Given that measures of intelligence are used for establishing clinical diagnoses, for making educational decisions, and for group-matching in research studies, continued examination of this contention is warranted. In the current study, verbal children with ASD performed moderately better on the RPM than on the Wechsler scales; children without ASD received higher percentile scores on the Wechsler than on the RPM. Adults with and without ASD received higher percentile scores on the Wechsler than the RPM. Results suggest that the RPM and Wechsler scales measure different aspects of cognitive abilities in verbal individuals with ASD. For the verbal children and adults with ASD in the current study, the RPM and Wechsler scales have unique contributions that must be considered in context when establishing a baseline of cognitive function. The results of this investigation highlight the importance of thoughtfully selecting appropriate measures of intelligence consistent with clinical, educational, and research purposes, especially for verbal children and adults with ASD.

The relationship between fluid intelligence and working memory is of fundamental importance to understanding how capacity-limited structures such as working memory interact with inference abilities to determine intelligent behavior. Recent evidence has suggested that the relationship between a fluid abilities test, Raven's Progressive Matrices, and working memory capacity (WMC) may be invariant across difficulty levels of the Raven's items. We show that this invariance can only be observed if the overall correlation between Raven's and WMC is low. Simulations of Raven's performance revealed that as the overall correlation between Raven's and WMC increases, the item-wise point bi-serial correlations involving WMC are no longer constant but increase considerably with item difficulty. The simulation results were confirmed by two studies that used a composite measure of WMC, which yielded a higher correlation between WMC and Raven's than reported in previous studies. As expected, with the higher overall correlation, there was a significant positive relationship between Raven's item difficulty and the extent of the item-wise correlation with WMC.

The Allen Brain Atlas shows that the semaphorin 5A (SEMA5A) gene, which encodes an important protein for neurogenesis and neuronal apoptosis, is predominantly expressed in the human hippocampus. Structural and functional neuroimaging studies have further shown that the hippocampus plays an important role in the performance on Raven's Progressive Matrices (RPM), a measure of reasoning ability and general fluid intelligence. Thus far, however, no study has examined the relationships between the SEMA5A gene polymorphism, hippocampal volume, and RPM performance. The current study collected both structural MRI, genetic, and behavioral data in 329 healthy Chinese adults, and examined associations between SEMA5A variants, hippocampal volume, and performance on RAPM (the advanced form of RPM). After controlling for intracranial volume (ICV), sex, and age, SEMA5A genetic polymorphism at the SNP rs42352 had the strongest association with hippocampal volume (p=0.00000552 and 0.000103 for right and left hippocampal volumes, respectively), with TT homozygotes having higher hippocampal volume than the other genotypes. Furthermore, there was a high correlation between right hippocampal volume and RAPM performance (r=0.42, p=0.0000509) for SEMA5A rs42352 TT homozygotes. This study provides the first evidence for the involvement of the SEMA5A gene in hippocampal structure and their interaction on RAPM performance. Future studies of the hippocampus-RPM associations should consider genetic factors as potential moderators., (Copyright © 2013 Elsevier Inc. All rights reserved.)

THERE IS A VAST AMOUNT OF POTENTIAL MAPPINGS BETWEEN BEHAVIORS AND INTENTIONS IN COMMUNICATION: a behavior can indicate a multitude of different intentions, and the same intention can be communicated with a variety of behaviors. Humans routinely solve these many-to-many referential problems when producing utterances for an Addressee. This ability might rely on social cognitive skills, for instance, the ability to manipulate unobservable summary variables to disambiguate ambiguous behavior of other agents ("mentalizing") and the drive to invest resources into changing and understanding the mental state of other agents ("communicative motivation"). Alternatively, the ambiguities of verbal communicative interactions might be solved by general-purpose cognitive abilities that process cues that are incidentally associated with the communicative interaction. In this study, we assess these possibilities by testing which cognitive traits account for communicative success during a verbal referential task. Cognitive traits were assessed with psychometric scores quantifying motivation, mentalizing abilities, and general-purpose cognitive abilities, taxing abstract visuo-spatial abilities. Communicative abilities of participants were assessed by using an on-line interactive task that required a speaker to verbally convey a concept to an Addressee. The communicative success of the utterances was quantified by measuring how frequently a number of Evaluators would infer the correct concept. Speakers with high motivational and general-purpose cognitive abilities generated utterances that were more easily interpreted. These findings extend to the domain of verbal communication the notion that motivational and cognitive factors influence the human ability to rapidly converge on shared communicative innovations.

Background: In this study, we sought to evaluate the influence of sociodemographic factors, ie, age, sex, socioeconomic status, maternal education, and human immunodeficiency virus (HIV) status, on cognitive performance in school-aged HIV-infected Nigerian children., Methods: Sixty-nine HIV-positive children aged 6-15 years were matched with 69 HIV-negative control children for age and sex. The children were subdivided for the purpose of analysis into two cognitive developmental stages using Piaget's staging, ie, the concrete operational stage (6-11 years) and the formal operational stage (12-15 years). All participants underwent cognitive assessment using Raven's Standard Progressive Matrices (RPM). Sociodemographic data for the study participants, ie, age, sex, socioeconomic status, and level of maternal education, were obtained using a study proforma. Logistic regression analyses were used to determine associations of HIV status and sociodemographic characteristics with RPM cognitive scores., Results: The overall mean RPM score for the HIV-positive children was 18.2 ± 9.8 (range 8.0-47.0) which was significantly lower than the score of 27.2 ± 13.8 (range 8.0-52.0) for the HIV-negative children (P < 0.001). On RPM grading, 56.5% of the HIV-positive children had cognitive performance at below average to intellectually defective range. Below average RPM scores were found to be significantly associated with younger age (6-11 years), positive HIV status, lower socioeconomic status, and low level of maternal education., Conclusion: Younger age, poor socioeconomic status, and low level of maternal education were factors apart from HIV infection that were significantly associated with low cognitive function in school-aged HIV-infected Nigerian children.

Inductive reasoning is a fundamental and complex aspect of human intelligence. In particular, how do subjects, given a set of particular examples, generate general descriptions of the rules governing that set? We present a biologically plausible method for accomplishing this task and implement it in a spiking neuron model. We demonstrate the success of this model by applying it to the problem domain of Raven's Progressive Matrices, a widely used tool in the field of intelligence testing. The model is able to generate the rules necessary to correctly solve Raven's items, as well as recreate many of the experimental effects observed in human subjects., (Copyright © 2011 Cognitive Science Society, Inc.)