Search

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. [ABSTRACT FROM AUTHOR]

AbstractObjectiveMethodsResultsConclusionsThe aim of this study was to create age-stratified norms for the Raven’s Standard Progressive Matrices (SPM) for Sri Lankan adults.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.The age-related changes were more accurately predicted by a curvilinear model (overall R2 = 0.961) than a linear regression model (R2 = 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.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. [ABSTRACT FROM AUTHOR]

Raven's progressive matrix (RPM) is one kind of visual abstract reasoning tasks, which tests the ability of extracting reasoning rules from limited samples and applying them to an unknown setting. It is frequently used in evaluating human intelligence. Recent advances of RPM-like datasets and solution models partially address the challenges of visually understanding the RPM questions and logically reasoning the missing answers. This paper tackles the challenges of the poor generalization performance due to insufficient samples in RPM datasets. To address the problem of insufficient data for precisely conducting relational reasoning in RPMs, we propose an effective scheme, namely candidate answer morphological mixup (CAM-Mix). CAM-Mix serves as a data augmentation strategy by gray-scale image morphological mixup, which regularizes various solution methods and overcomes the model overfitting problem. Compared with existing methods, a more accurate decision boundary could be defined by creating new negative candidate answers semantically similar to the correct answers. Experimental results show that the proposed data augmentation method on state-of-the-art RPM solution models can provide significant and consistent performance improvements on various RPM-like datasets compared with state-of-the-art solution models and other data augmentation strategies. [ABSTRACT FROM AUTHOR]

Background The presence of asthma in children may negatively impact their cognitive function, possibly due to intermittent hypoxia from repeated acute exacerbations, sleep deprivation, and school absenteeism. However, conflicting reports abound on cognitive performance among children with asthma. Objective To assess and compare the cognitive function of asthmatic and non-asthmatic children in Ilorin, Nigeria. Methods This cross-sectional study compared the cognitive performance scores of 66 children with asthma aged 6-17 years, and the corresponding scores of 66 healthy age- and gender-matched children without asthma. Data obtained included sociodemographic, anthropometric, and clinical details. Cognitive function was assessed with the Raven's Progressive Matrices (RPM) psychometric test. Results The male: female ratio was 1.3:1. The median RPM score of 50.0 [interquartile range (IQR) 25.0-75.0] in children with asthma was significantly higher than that of non-asthmatic children [32.5 (IQR 10.0-50.0)]; (P=0.016). A significantly higher proportion of asthmatic subjects (74.2%) were in the high grade RPM score category compared to the non-asthmatic children’s concomitant proportion (57.6%). Asthmatic subjects were twice as likely to belong to the high grade RPM score category than their non-asthmatic counterparts (OR=2.12; 95%CI 1.02 to 4.44; P=0.043). There was no significant association between RPM score grade and clinical severity of asthma in the subject population (P=0.554). Conclusion Children with asthma perform better in cognitive function assessment, using RPM test, compared to their non-asthmatic counterparts in Ilorin, Nigeria.

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.

Background The presence of asthma in children may negatively impact their cognitive function, possibly due to intermittent hypoxia from repeated acute exacerbations, sleep deprivation, and school absenteeism. However, conflicting reports abound on cognitive performance among children with asthma. Objective To assess and compare the cognitive function of asthmatic and non-asthmatic children in Ilorin, Nigeria. Methods This cross-sectional study compared the cognitive performance scores of 66 children with asthma aged 6-17 years, and the corresponding scores of 66 healthy age- and gender-matched children without asthma. Data obtained included sociodemographic, anthropometric, and clinical details. Cognitive function was assessed with the Raven's Progressive Matrices (RPM) psychometric test. Results The male: female ratio was 1.3:1. The median RPM score of 50.0 [interquartile range (IQR) 25.0-75.0] in children with asthma was significantly higher than that of non-asthmatic children [32.5 (IQR 10.0-50.0)]; (P=0.016). A significantly higher proportion of asthmatic subjects (74.2%) were in the high grade RPM score category compared to the non-asthmatic children's concomitant proportion (57.6%). Asthmatic subjects were twice as likely to belong to the high grade RPM score category than their non-asthmatic counterparts (OR=2.12; 95%CI 1.02 to 4.44; P=0.043). There was no significant association between RPM score grade and clinical severity of asthma in the subject population (P=0.554). Conclusion Children with asthma perform better in cognitive function assessment, using RPM test, compared to their non-asthmatic counterparts in Ilorin, Nigeria. [ABSTRACT FROM AUTHOR]

This study aims to investigate the connection between the degree of visual impairment and educational task performance and management in the e-learning environment with an emphasis on writing and visual recognition disorders. The study was conducted in 2019 in Beijing city, Anhui province, Jiangsu province, Guizhou province, Zhejiang province (China) among 125 students of online English courses. The age of participants corresponded to the sixth grade (9.0 ± 2.5 years). The e-learning course included three 10- lessons blocks, after which students underwent individual online training with the integrated application of the Gottschaldt's, Poppelreuter-Ghent's, and Raven's tests. The level of visual impairment inversely depends on the tasks' accomplishment -- the more severe the vision loss, the worse the learning outcomes. Pearson's correlation coefficients between the educational performance and mild visual impairment were 0.79 (Gottschaldt's Hidden Figure test), 0.81 (Raven's Progressive Matrices test), and 0.83 (Poppelreuter- Ghent's Overlapping Figures test). [ABSTRACT FROM AUTHOR]

Raven’s Progressive Matrices measure logical reasoning and are often included in large multi-topic surveys in low and middle-income countries. The matrices are image-based items that do not require formal knowledge of language or math to complete. As such, they are attractive items to measure logical reasoning in international development contexts. Many of these large field surveys include short item Raven’s sets because space is too limited to fit a full suite. However, short sets can result in restricted variation in terms of test scores. In this paper, we use a nominal response model (NRM) form of item response theory (IRT) to uncover hidden variation in right and wrong answers using short-item Raven’s tests from two large field surveys in Malawi and Zambia. We also analyze relationships between a set of other variables, comparing performance of different versions of the logical reasoning scores as both independent and dependent variables, checking the validity of the new scores. The new NRM-estimated logical reasoning scores follow a more normal distribution in both samples. Validity checks suggest that when relationships are less strong, NRM-estimated scores can capture more nuance than summed scores or even 2 parameter logistic IRT-estimated scores. NRM can uncover differences that are not apparent when using simple summed scores.

Raven's Progressive Matrices (RPM) have been widely used as standard intelligence tests for human participants. Humans solve RPM problems in a hierarchical manner, perceiving conceptual features at different levels and inferring the latent rules governing the matrix using cognitive maps. Although the latest AI algorithms can surpass human performance, little effort has been made to build a model that solves RPM problems in a human-like hierarchical manner. We built a human-like hierarchical neuro-symbolic model to solve RPM problems. The proposed model consists of a semantic-VAE (sVAE) perceptual module and a cognitive map reasoning back-end (CMRB). The supervised sVAE extracts the hierarchical visual features of RPMs by perceiving the structural organization of RPMs through a convolutional neural network and disentangles objects into semantically understandable features. Based on these semantic features, the CMRB predicts the semantic features of objects in the missing field using cognitive maps generated by supervised learning or manually designed. The answer image was generated by sVAE using the semantic features predicted by CMRB. The proposed model achieved state-of-the-art performance on three benchmarks datasets—RAVEN, I-RAVEN, and RAVEN-fair—generalizes well to RPMs containing objects with untrained feature dimensions, mimics human cognitive processes when solving RPM problems, achieves interpretability of their hierarchical processes, and can also be applied to some real-world situations that require abstract visual reasoning. [ABSTRACT FROM AUTHOR]

Introduction: Intelligence in children with autism has been underestimated for long. Previous studies have reported an uneven performance on intelligence tests. Better performance on Raven's Progressive Matrices (RPM) as compared to Wechsler Intelligence Scale for Children and Adults (WISC-III) has been found in some studies. RPM is quick and easy to administer. Hence there is a need to understand if RPM is a comparable test to tap the intelligence in children with autism. Aims and Objectives: To find the percentile IQ scores of children with autism and controls using RPM and WISC and to compare the performance of the two groups using the IQ tests. Materials and Methods: A cross-sectional study including 15 children with autism and normal controls was done. The children were subjected to WISC-III and RPM. Results: The study shows that normal children perform better on intelligence tests than children with autism. Both tests can be used to measure intelligence in children with autism fairly owing to their correlation. However, RPM showed better scores than WISC-III for children with autism. Conclusions: Matrices can be used as an alternative and an even better test to measure intelligence in children with autism just as the standard WISC-III. Further studies in a larger population of children with autism are warranted to validate our findings. [ABSTRACT FROM AUTHOR]

Over 200 million children worldwide suffer from malnutrition and, as a result, are underdeveloped both physically and mentally. Meat has more bioavailable essential micronutrients than plants and is the best source of nutrient-rich foods for children aged 6 to 23 mo, according to the World Health Organization. By consuming meat, which contributes essential bioavailable micronutrients to diets, children in particular can reduce undernutrition and the associated growth and cognitiveimpairment. This review aims to elucidate the effect of meat consumption on cognitive development by systematically reviewing and synthesizing results from available studies. Of 241 pertinent studies initially retrieved from the literature, only 9 met the inclusion criteria, and these included 28 cognition variables covering data from 10,617 children aged 3 mo to 17 y. Twelve(42.8%) of the variables showed improvements in cognition with increased meat consumption, 6 (21.4%) showed no effect,and 10 (35.7%; including 8 from HIV-positive children) showed a decrease in cognition with increased meat consumption.Across all variables, the study reveals some evidence of an association between meat consumption and improved cognition,which becomes more compelling when data from the HIV-positive children are excluded. More research on the effect of meat consumption on cognitive development from randomized controlled studies is needed.

The main goal of this study is to examine the differences between school-aged children with different chronotypes who are only children or have a sibling in the household, regarding their sleeping habits and performance in intelligence tasks. The main measures used were Chronotype Questionnaire for Children and Raven's Coloured Progressive Matrices. This study analysed 46 Portuguese children (Mean=8.67; SD=1.034, range 7-11 years of age) without sleep or educational/psychological disturbances, attending the first grade. The differences regarding chronotype and whether they were only children or not were examined. Then the performance of the sample in the Raven’s Progressive Matrices tests was evaluated. The Mann-Whitney U test showed that ‘only’ children had higher fluid intelligence scores only in the afternoon. The chronotype did not differentiate this population and it is concluded that the type of household is the main moderator: Not being an only child was, in general, the best predictor in the test. Results suggest that different sleeping times (e.g., bedtime and wake-up time) and different chronotypes did not have different effects. However, having siblings proved to be an advantage regarding the child’s performance. Re-education of sleeping practices, especially examining the influence of co-sleeping in families, is essential for the intellectual development of children. [ABSTRACT FROM AUTHOR]

Emerging evidence suggests that early bilingual experience constrains the development of attentional processes in infants, and that some of these early bilingual adaptations could last into adulthood. However, it is not known whether the early adaptations in the attentional domain alter more general cognitive abilities. If they do, then we would expect that bilingual adults who learned their second language early in life would score more highly across cognitive tasks than bilingual adults who learned their second language later in life. To test this hypothesis, 170 adult participants were administered a well-established (non-verbal) measure of fluid intelligence: Raven's Advanced Progressive Matrices (RAPM). Fluid intelligence (the ability to solve novel reasoning problems, independent of acquired knowledge) is highly correlated with numerous cognitive abilities across development. Performance on the RAPM was greater in bilinguals than monolinguals, and greater in 'early bilinguals' (adults who learned their second language between 0–6 years) than 'late bilinguals' (adults who learned their second language after age 6 years). The groups did not significantly differ on a proxy of socioeconomic status. These results suggest that the difference in fluid intelligence between bilinguals and monolinguals is not a consequence of bilingualism per se, but of early adaptive processes. However, the finding may depend on how bilingualism is operationalized, and thus needs to be replicated with a larger sample and more detailed measures. [ABSTRACT FROM AUTHOR]

Abstract reasoning is one of the defining characteristics of human intelligence and can be estimated by visual IQ tests such as Raven's Progressive Matrices. In this paper, we propose using a hierarchical Transformer encoder with structured representation that employs a novel neural network architecture to improve both perception and reasoning in a visual IQ test. For perception, we used object detection models to extract the structured features. For reasoning, we used the Transformer encoder in a hierarchical manner that fits the structure of Raven's Progressive Matrices. Experimental results on the RAVEN dataset, which is one of the major large-scale datasets on Raven's Progressive Matrices, showed that our proposed architecture achieved an overall accuracy of 99.62%, which is an improvement of more than 8% points over CoPINet, the present-day, state-of-the-art neural network model.

This study emerged from a state initiative to improve teaching and lift school outcomes in Melbourne, Australia. At the invitation of participating schools, the data presented here were produced for follow-up sessions in a professional development program using the 'Developmental Management Approach'. Fieldnotes of teacher talk intended to give students opportunities to choose to behave prosocially were collected in this accidental action research. The observational summary from the fieldnotes was used in discussions with participating schools. Implications of the power of classroom hinting by teachers and its potential influence on students' prosocial engagement and moral development are discussed using Raven's typology. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

Copyright of Panamerican Journal of Neuropsychology / Cuadernos de Neuropsicología is the property of Cuadernos de Neuropsicologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

The world population is rapidly aging, bringing together the necessity to better understand the advancing age. This characterization may be used to aid early diagnosis and to guide individually-tailored interventions. While some event-related potential (ERP) components, such as the P300 and late positive complex (LPC), have been associated with fluid intelligence (Gf) in young population; little is known whether these associations hold for older people. Therefore, the main goal of this study was to assess whether these ERP components are associated with Gf in the elderly. Fifty-seven older adults performed a continuous performance task (CPT) and a visual oddball paradigm while EEG was recorded. Participants were divided into two groups, according to their performance in the Raven's Advanced Progressive Matrices test: high-performance (HP) and low-performance (LP). Results showed that the HP group, compared to the LP group, had higher LPC amplitudes in the CPT and shorter P300 latencies in the oddball task, highlighting the role of ERP components as a potential electrophysiological proxy of Gf abilities in the elderly. [ABSTRACT FROM AUTHOR]

The Sandia Matrices are a free alternative to the Raven's Progressive Matrices (RPMs). This study offers a psychometric review of Sandia Matrices items focused on two of the most commonly investigated issues regarding the RPMs: (a) dimensionality and (b) sex differences. Model-data fit of three alternative factor structures are compared using confirmatory multidimensional item response theory (IRT) analyses, and measurement equivalence analyses are conducted to evaluate potential sex bias. Although results are somewhat inconclusive regarding factor structure, results do not show evidence of bias or mean differences by sex. Finally, although the Sandia Matrices software can generate infinite items, editing and validating items may be infeasible for many researchers. To aide implementation of the Sandia Matrices, we provide scoring materials for two brief static tests and a computer adaptive test. Implications and suggestions for future research using the Sandia Matrices are discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Measuring and assessing the cognitive load associated with different tasks is crucial for many applications, from the design of instructional materials to monitoring the mental well-being of aircraft pilots. The goal of this paper is to utilize EEG to infer the cognitive workload of subjects during intelligence tests. We chose the well established advanced progressive matrices test, an ideal framework because it presents problems at increasing levels of difficulty and has been rigorously validated in past experiments. We train classic machine learning models using basic EEG measures as well as measures of network connectivity and signal complexity. Our findings demonstrate that cognitive load can be well predicted using these features, even for a low number of channels. We show that by creating an individually tuned neural network for each subject, we can improve prediction compared to a generic model and that such models are robust to decreasing the number of available channels as well. [ABSTRACT FROM AUTHOR]

The aim of the current paper is to present a discussion regarding intelligence in new generations and to compare their performance with that of previous ones on the basis of the results obtained in Raven's Progressive Matrices norms updating for 19-to-30 year-old people in La Plata, Argentina. This is a compelling discussion after having found an arrest on the Flynn effect. The sample consisted of 947 male-female participants. Based on age, they were split in four intervals: ages 19-20, 21-22, 23-24 and 25-30 years old. The results obtained in this study were compared with the norms from 1964 and 2000 regarding the same age and city as well. A plateau effect and a slight decrease on the average scores in the test were found between the 2000 norms and the current study. In accordance with recent research from other countries, these results show a deceleration and even an arrest of the direct scores needed to reach an average score in each age-group. However, unlike such research we propose that the found effect is not a top but a plateau one. The contributions to the debate on its causes are based on generational dimension-linked perspectives, and on the so-called generation Y or the ' 'Millennials' characteristics in particular. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Raven's Progressive Matrices (RPMs) are frequently used in evaluating human's visual reasoning ability. Researchers have made considerable efforts in developing systems to automatically solve the RPM problem, often through a black-box end-to-end convolutional neural network for both visual recognition and logical reasoning tasks. Based on the intrinsic natures of RPM problem, we propose a Two-stage Rule-Induction Visual Reasoner (TRIVR), which consists of a perception module and a reasoning module, to tackle the challenges of real-world visual recognition and subsequent logical reasoning tasks, respectively. For the reasoning module, we further propose a "2+1" formulation that models human's thinking in solving RPMs and significantly reduces the model complexity. It derives a reasoning rule from each RPM sample, which is not feasible for existing methods. As a result, the proposed reasoning module is capable of yielding a set of reasoning rules modeling human in solving the RPM problems. To validate the proposed method on real-world applications, an RPM-like Video Prediction (RVP) dataset is constructed, where visual reasoning is conducted on RPMs constructed using real-world video frames. Experimental results on various RPM-like datasets demonstrate that the proposed TRIVR achieves a significant and consistent performance gain compared with state-of-the-art models. • A two-stage rule-induction visual reasoner is proposed to solve RPMs. • The proposed method generates one reasoning rule for each sample question. • The proposed method yields reasoning rules modeling human in solving RPMs. • A real-world RPM-like video prediction dataset is developed for evaluation. [ABSTRACT FROM AUTHOR]

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 RCC) 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.

Most research on the relation between psychopathy and intelligence has been conducted with incarcerated male samples. However, psychopathic traits can be found among non-incarcerated individuals, male or female, possessing high intellectual capacities. The construct of psychopathy has been comparatively understudied in women. We hypothesized a positive correlation between interpersonal psychopathic traits and intelligence among females, whereby those non-criminal females having high scores on these traits would be more intelligent than those having low scores on these traits. We carried out a correlational analysis and group comparisons on a sample of 121 non-criminal females. Variables that were measured include the Psychopathic Personality Inventory (PPI) total score and its two subscales; the Levenson Primary and Secondary Psychopathy (LPSP) total score and its two subscales; Psychoticism (P) and intelligence measured by Raven’s Standard Progressive Matrices (SPM). A significant positive correlation (r = .33, p

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.

Academic success depends on numerous factors: intelligence, motivation, developmental level of basic and more complex cognitive functions, personality traits, etc. In special education, Acadia test of developmental abilities is most frequently used to assess the abilities necessary for acquiring academic knowledge and skills, with the aim to detect the children who have, or might have, learning disabilities. In Serbia, psychometric characteristics of the test have not been determined so far. Thus, the aim of this paper is to determine the relation between the achievements on this test and intelligence in a sample of younger school children who attend regular schools in Belgrade. The sample consisted of 784 children of both genders (51% of girls and 49% of boys), aged between 8 and 12 (AM=9.71; SD=0.65). Acadia test, which consists of 13 subtests for assessing attention and short-term memory, perceptive, visual-constructive, conceptual, and speech-language abilities, was used in this research. Intelligence was assessed by means of the standard version of Raven’s progressive matrices (AM=37.03; SD=10.51), and the examinees were organized in four groups on the basis of percentile ranks of the raw score (3 sections). By analyzing the results, statistically significant positive correlation was determined between all the subtests of Acadia test and intelligence, which ranges from 0.27 to 0.63. Age has a low (r=0.08-0.26), but also statistically significant correlation with the achievements on the subtests of Acadia test (p0.05). By applying multivariate analysis of covariance, with controlling the influence of age, it was determined that intelligence is a significant factor of achievement on Acadia test (Wilks’ λ=0.502; F(39)=14.926; p≤0.000, ηp2 =0.205), and on all the subtests separately (p≤0.000), accounting for 7 to 35% of the results variability.

This study investigates the correlation of self-report accuracy with academic performance. The sample was composed of 289 undergraduate students (96 senior and 193 junior) enrolled in two engineering classes. Age ranged between 22 and 24 years, with a slight over representation of male students (53%). Academic performance was calculated based on students' final grades in each class. The tendency to report inaccurate information was measured at the end of the Raven Progressive Matrices Test, by asking students to report their exact finishing times. We controlled for gender, age, personality traits, intelligence, and past academic performance. We also included measures of centrality in their friendship, advice and trust networks. Correlation and multiple regression analyses results indicate that lower achieving students were significantly less accurate in self-reporting data. We also found that being more central in the advice network was correlated with higher performance (r = .20, p < .001). The results are aligned with existing literature emphasizing the individual and relational factors associated with academic performance and, pending future studies, may be utilized to include a new metric of self-report accuracy that is not dependent on academic records. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We aimed to achieve a better understanding of the cognitive processes of fluid reasoning (or fluid intelligence; Gf), the ability to reason in novel conditions. While fluid reasoning has often been considered a unitary construct, multiple cognitive processes are expected to affect fluid reasoning performance. Yet, the contribution of various cognitive processes in fluid reasoning performance remains under-explored. We hypothesized that individual differences in fluid intelligence can be viewed as a composite of individual differences in performance in various processes of Gf. Change detection, rule verification, and rule generation were the three processes-of-interest that were additively recruited in a novel visuospatial reasoning task. We observed decreases in accuracy and increases in response time as the processing requirements increased across task conditions. Hierarchical multiple linear regression analyses showed that individual differences in the likelihood of success and speed of each of these processes, accounted for different aspects of individual differences in accuracy and response time in fluid reasoning performance, as measured by Raven's Progressive Matrices. Change detection was a significant contributor to performance in problems with higher visuospatial demand, however, rule verification and rule generation consistently contributed to performance for all problem types. Our findings support the position that individual differences in fluid intelligence emerge as a composite of performance on separable cognitive operations, with rule processing being important for differentiating performance on high difficulty problems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Intelligence has increasingly been becoming a desirable human trait, so she scientists from different fields devote it a lot of attention. We were interested in the interconnectedness of intelligence, musical activities and family environment in ninth graders. Correlations were determined with the differences in student achievement in measuring educative components of general intellectual abilities with standard progressive matrices (SPM). The survey results confirm that intelligence, musical activities and musical family environment significantly correlated with each other. The study included 177 randomly selected ninth graders from six basic schools in the region of Primorska.