Intelligent Insights on Intelligence Theories and Tests
ABSTRACT
In this paper, we introduce the reader to the field of cognitive network science, that is, the application of network science methods to study human cognition and knowledge structures. Cognitive networks are representations of associative knowledge between concepts in a cognitive system apt at acquiring, storing, processing and producing language, that is, the mental lexicon. In a cognitive network, nodes represent concepts with links expressing relations, such as semantic, syntactic, phonological and visual connections, for example, “canine” and “dog” (nodes) linked by “being synonyms” (link). Hence, cognitive networks represent associative knowledge in mathematical, measurable and quantifiable ways. Can such structure be used to gain insights over cognitive phenomena? We explore this research question by reviewing recent, pioneering key applications and limitations of cog-nitive networks across visual, auditory, and semantic language processing tasks, either in healthy or clinical populations. We also review applications of cognitive networks modeling language acquisition, reconstructing text content and assessing creativity or personality traits in individuals. Our paper also gently introduces the reader to mathematical notations, definitions and measures about single-layer and multiplex networks as well as hypergraphs. Last but not least, across phonological, semantic and syntactic networks, we guide the reader through relevant psychological frameworks, datasets and software packages that might all aid current and future cognitive network scientists.
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Abstract
A diagnosis of intellectual disability is a momentous event that can determine eligibility for special services and supportive sources of income, and in the criminal arena, it can be a matter of life and death. For criminal defendants who might otherwise face capital punishment, it is a matter of life and death. Individuals evaluated for intellectual disability often have been given multiple intelligence tests, sometimes with results falling on both sides of the diagnostic threshold. In all cases, the diagnostic decision must be based on a rigorous examination of the totality of evidence in the context of systematic clinical judgment. When multiple IQ results are relevant and comparable, they can be combined into a properly computed composite score to assist the clinician charged with diagnostic responsibility in determining if Prong 1, deficits in intellectual functioning, of the three-prong criteria necessary for an intellectual disability diagnosis has been met. Best psychometrically grounded methods for these calculations are presented along with a discussion of inappropriate approaches for accurately combining multiple scores. To make these methods accessible to professionals outside the discipline of psychology, all calculations are fully explained in the context of foundational concepts.
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Abstract
Objective This study examined the relationship between executive functions (EF) and mathematical skills through-out development using a meta-analysis of longitudinal studies.
Method This study included (a) longitudinal studies that (b) reported correlations between EF measures (assessed at Time 1) and mathematics outcomes (assessed at Time 2) in (c) typically developing samples ranging in age from birth to 18 years. Studies were excluded if they were (a) not written in English or Portuguese, (b) aggregated data from typical and atypical populations, or (c) combined data from children and adolescents without distinction. A systematic search was conducted in October 2021 and later updated in 2025 using PsycINFO, SciELO, and PubMed. The risk of publication bias was assessed using funnel plot analysis and Egger's test. A random-effects meta-analysis was performed.
Results Twenty-nine studies involving children and adolescents (n = 104,295; M_age at Time 1 = 5.4 years; M_age at Time 2 = 8.4 years) were included. The overall correlation between EF and mathematics was moderate and statisti-cally significant (r = 0.30, 95% CI [0.24, 0.36]). Among EF components, working memory showed the strongest asso-ciation with mathematical performance (r = 0.43, 95% CI [0.35, 0.50]), followed by cognitive flexibility (r = 0.34, 95% CI [0.27, 0.42]) and inhibitory control (r = 0.21, 95% CI [0.13, 0.29]). Age and study quality did not significantly moderate the relationship between EF and mathematics.
Conclusion The findings suggest that EF, particularly working memory, is a meaningful predictor of mathematical performance across development. These results underscore the importance of early EF assessment in informing interventions designed to prevent math learning difficulties. Despite the low risk of publication bias, the high heterogeneity observed in most analyses suggests the influence of additional moderating variables that warrant further investigation.
Keywords Executive function, Math, Meta-analysis, Longitudinal
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Very important article (open source..click here to read/download) regarding cognitive functioning and working memory capacity and attentional control. For at least 15 years I’ve been monitoring research on the attentional-control working memory complex system (AC-Gwm)…(click here for numerous posts regarding the important of AC-Gwm). I’m convinced that the AC-Gwm complex system is one of the core cognitive efficiency systems that helps us understand general intellectual functioning. It has been found to be important in cognitive functioning and also in various forms of psychopathology.
