NCME ITEMS e-learning module -- Foundations of IRT

https://ncme.elevate.commpartners.com/products/digital-module-19-foundations-of-irt-estimation

Are individual differences in attention control related to working memory capacity? A latent variable mega-analysis. - PsycNET

And yet more good research implicating attentional control (AC under Gwm in CHC taxonomy) as central to human intelligence.

Are individual differences in attention control related to working memory capacity? A latent variable mega-analysis. - PsycNET 
https://psycnet.apa.org/record/2020-86314-001

Citation
Unsworth, N., Miller, A. L., & Robison, M. K. (2020). Are individual differences in attention control related to working memory capacity? A latent variable mega-analysis. Journal of Experimental Psychology: General.Advance online publication. https://doi.org/10.1037/xge0001000

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Abstract
The current study examined whether there are coherent individual differences in attention control abilities and whether they are related to variation in working memory capacity. Data were pooled from multiple studies over 12 years of data collection. Mega-analyses on the combined data set suggested that most of the attention control measures had adequate reliabilities and were weakly to moderately related to one another. A number of latent variable mega-analyses suggested that the attention control measures loaded onto a broad attention control factor and this factor was consistently related to working memory capacity. Furthermore, working memory capacity was generally related to each individual attention control measure. These results provide important evidence for the notion that there is a coherent attention control factor and this factor is related to working memory capacity consistent with much prior research. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

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Kevin S. McGrew, PhD
Educational & School Psychologist
Director
Institute for Applied Psychometrics (IAP)
https://www.themindhub.com
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More support for the Gs—>Gwm—>—Gf/ Gc developmental cascade model as per CHC taxonomy

 More support for the developmental cascade model

https://www.sciencedirect.com/science/article/abs/pii/S0160289620300817?via%3Dihub

Speed of processing, control of processing, working memory and crystallized and fluid intelligence: Evidence for a developmental cascade 

Anna Tourva, George Spanoudis

 

Keywords: Fluid intelligence Crystallized intelligence Working memory Speed of processing Executive attention Developmental-cascade model 

A B S T R A C T  

The present study investigated the causal relations among age, speed of processing, control of processing, working memory and intelligence, fluid and crystallized. 158 participants aged from 7 to 18 years old completed a large battery of tests measuring latent factors of speed, control of processing and working memory. Intelligence was assessed using the Wechsler Abbreviated Scale of Intelligence. Structural equation modeling was performed to determine whether there is a cognitive-developmental cascade in which age-related increases in processing speed lead to improvements in control of processing that leads to increases in working memory, and whether improved working memory, in turn, leads to increases in both fluid and crystallized intelligence. Several alternative models of a different cascade order of the above factors were also tested. The results of the present study provide evidence of a cognitive-developmental cascade, confirming that this model describes cognitive development during childhood and adolescence.  

Click images to enlarge.

Development and psychometric properties of rubrics for assessing social-emotional skills in youth - ScienceDirect

https://www.sciencedirect.com/science/article/abs/pii/S0191491X20301863 
https://www.sciencedirect.com/science/article/abs/pii/S0191491X20301863?via%3Dihub

Paper from excellent group of scholars doing critical work on "BEYOND IQ" factors.

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Kevin S. McGrew, PhD
Educational & School Psychologist
Director
Institute for Applied Psychometrics (IAP)
https://www.themindhub.com
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Toward a hierarchical model of social cognition: A neuroimaging meta-analysis and integrative review of empathy and theory of mind-file under Gei per CHC taxonomy.

 Toward a hierarchical model of social cognition: A neuroimaging meta-analysis and integrative review of empathy and theory of mind. 
https://psycnet.apa.org/fulltext/2020-82377-001.html
Toward a Hierarchical Model of Social Cognition: A Neuroimaging Meta-Analysis and Integrative Review of Empathy and Theory of Mind
Matthias Schurz , Joaquim Radua, Matthias G. Tholen, Lara Maliske, Daniel S. Margulies, Rogier B. Mars, Jerome Sallet, Philipp Kanske
Author Affiliations  
Schurz, M., Radua, J., Tholen, M. G., Maliske, L., Margulies, D. S., Mars, R. B., . . . Kanske, P. (2020). Toward a hierarchical model of social cognition: A neuroimaging meta-analysis and integrative review of empathy and theory of mind. Psychological Bulletin. Advance online publication. http://dx.doi.org/10.1037/bul0000303
Abstract

Along with the increased interest in and volume of social cognition research, there has been higher awareness of a lack of agreement on the concepts and taxonomy used to study social processes. Two central concepts in the field, empathy and Theory of Mind (ToM), have been identified as overlapping umbrella terms for different processes of limited convergence. Here, we review and integrate evidence of brain activation, brain organization, and behavior into a coherent model of social-cognitive processes. We start with a meta-analytic clusteringof neuroimaging data across different social-cognitive tasks. Results show that understanding others' mental states can be described by a multilevel model of hierarchical structure, similar to models in intelligence and personality research. A higher level describes more broad and abstract classes of functioning, whereas a lower one explains how functions are applied to concrete contexts given by particular stimulus and task formats. Specifically, the higher level of our model suggests 3 groups of neurocognitive processes: (a) predominantly cognitive processes, which are engaged when mentalizing requires self-generated cognition decoupled from the physical world; (b) more affectiveprocesses, which are engaged when we witness emotions in others based on shared emotional, motor, and somatosensory representations; (c) combined processes, which engage cognitive and affective functions in parallel. We discuss how these processes are explained by an underlying principal gradient of structural brain organization. Finally, we validate the model by a review of empathy and ToM task interrelations found in behavioral studies.
Public Significance Statement

Empathy and Theory of Mind are important human capacities for understanding others. Here, we present a meta-analysis of neuroimaging data from 4,207 participants, which shows that these abilities can be deconstructed into specific and partially shared neurocognitive subprocesses. Our findings provide systematic, large-scale support for the hypothesis that understanding others' mental states can be described by a multilevel model of hierarchical structure, similar to models in intelligence and personality research.

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Kevin S. McGrew, PhD
Educational & School Psychologist
Director
Institute for Applied Psychometrics (IAP)
https://www.themindhub.com
******************************************

Intelligence and creativity share a common cognitive and neural basis. -File under P-FIT, g, creativity, Glr, Gf, Gc, Gc, brain networks

A most excellent study.  

https://doi.apa.org/doiLanding?doi=10.1037%2Fxge0000958

Frith, E., Elbich, D. B., Christensen, A. P., Rosenberg, M. D., Chen, Q., Kane, M. J., Silvia, P. J., Seli, P., & Beaty, R. E. (2020). Intelligence and creativity share a common cognitive and neural basis. Journal of Experimental Psychology: General. Advance online publication. https://doi.org/10.1037/xge0000958

Are intelligence and creativity distinct abilities, or do they rely on the same cognitive and neural systems? We sought to quantify the extent to which intelligence and creative cognition overlap in brain and behavior by combining machine learning of fMRI data and latent variable modeling of cognitive ability data in a sample of young adults (N = 186) who completed a battery of intelligence and creative thinking tasks. The study had 3 analytic goals: (a) to assess contributions of specific facets of intelligence (e.g., fluid and crystallized intelligence) and general intelligence to creative ability (i.e., divergent thinking originality), (b) to model whole-brain functional connectivity networks that predict intelligence facets and creative ability, and (c) to quantify the degree to which these predictive networks overlap in the brain. Using structural equation modeling, we found moderate to large correlations between intelligence facets and creative ability, as well as a large correlation between general intelligence and creative ability (r = .63). Using connectome-based predictive modeling, we found that functional brain networks that predict intelligence facets overlap to varying degrees with a network that predicts creative ability, particularly within the prefrontal cortex of the executive control network. Notably, a network that predicted general intelligence shared 46% of its functional connections with a network that predicted creative ability—including connections linking executive control and salience/ventral attention networks—suggesting that intelligence and creative thinking rely on similar neural and cognitive systems. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

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Kevin McGrew, PhD

Educational Psychologist

Director, Institute for Applied Psychometrics

IAP

www.themindhub.com

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