IQ's Corner: October 2021

Friday, October 29, 2021

For IQs Corner readers: The Cognitive-Affective-Motivation Model of Learning (CAMML): Standing on the Shoulders of Giants - Kevin S. McGrew, 2021

I'm pleased to share my new pub with my blog readers.

The Cognitive-Affective-Motivation Model of Learning (CAMML): Standing on the Shoulders of Giants - Kevin S. McGrew, 2021
https://journals.sagepub.com/doi/abs/10.1177/08295735211054270

The Cognitive-Affective-Motivation Model of Learning (CAMML): Standing on the Shoulders of Giants
Kevin S. McGrew

First Published October 25, 2021 Research Article

https://doi.org/10.1177/08295735211054270

Article information

Abstract
The Cognitive-Affective-Motivation Model of Learning (CAMML) is a proposed framework for integrating contemporary motivation, affective (Big 5 personality) and cognitive (CHC theory) constructs in the practice of school psychologists (SPs). The central tenet of this article is that SPs need to integrate motivation alongside affective and cognitive constructs vis-à-vis an updated trilogy-of-the-mind (cognitive, conative, affective) model of intellectual functioning. CAMML builds on Richard Snow's seminal research on academic aptitudes—which are not synonymous with cognitive abilities. Learning aptitude complexes are academic domain-specific cognitive abilities and personal investment mechanisms (motivation and self-regulation) that collectively produce a student's readiness to learn in a specific domain. CAMML incorporates the "crossing the Rubicon" commitment pathway model of motivated self-regulated learning. It is recommended SPs take a fresh look at motivation theory, constructs, and research, embedded in the CAMML aptitude framework, by going back-to-the-future guided by the wisdom of giants from the field of cognition, intelligence, and educational psychology.
Keywords
motivation, self-regulated learning, aptitudes, domain-specific, aptitude complexes, crossing the Rubicon, taxonomies, individual differences, readiness, CHC theory, Big 5, Gf-Gc theory

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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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Relations among phonological processing skills and mathematics in children: A meta-analysis. - PsycNET

Relations among phonological processing skills and mathematics in children: A meta-analysis. - PsycNET
https://psycnet.apa.org/record/2021-98184-001

Citation
Yang, X., Yan, M., Ruan, Y., Ku, S. Y. Y., Lo, J. C. M., Peng, P., & McBride, C. (2021). Relations among phonological processing skills and mathematics in children: A meta-analysis. Journal of Educational Psychology. Advance online publication. https://doi.org/10.1037/edu0000710

Abstract
The present study presents a meta-analysis of the relations between phonological processing abilities and different mathematics subskills. Using a random-effects model with 94 studies (135 unique samples, 826 effect sizes), the present meta-analysis revealed a significant general association between phonological processing and mathematics (average r = .33, p < .001, 95% CI [.30, .36]). Phonological awareness (PA) and rapid automatized naming (RAN) showed stronger correlations with mathematics than phonological memory (PM) did. The correlations among phonological processing abilities and mathematics skills were generally stronger among younger children than among older children. PA and PM manifested larger effect sizes when correlated with mathematics accuracy than with mathematics fluency, whereas RAN yielded larger effect sizes when associated with mathematics fluency than with mathematics accuracy. Metastructural equation modeling results revealed that, after statistically controlling for domain-general abilities (i.e., vocabulary knowledge, executive functioning, and nonverbal intelligence), phonological processing still made a unique contribution to different mathematics subskills (β_s = .20 ∼ .54). These results suggest that children may use phonological processing abilities as one mechanism through which to represent, manipulate, and retrieve mathematics knowledge. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Monday, October 25, 2021

On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting | bioRxiv

On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting | bioRxiv
https://www.biorxiv.org/content/10.1101/2021.10.19.462649v1

Abstract
Human intelligence is one of the main objects of study in cognitive neuroscience. Reviews and meta-analyses have proved to be fundamental to establish and cement neuroscientific theories on intelligence. The prediction of intelligence using in vivo neuroimaging data and machine learning has become a widely accepted and replicated result. Here, we present a systematic review of this growing area of research, based on studies that employ structural, functional, and/or diffusion MRI to predict human intelligence in cognitively normal subjects using machine-learning. We performed a systematic assessment of methodological and reporting quality, using the PROBAST and TRIPOD assessment forms and 30 studies identified through a systematic search. We observed that fMRI is the most employed modality, resting-state functional connectivity (RSFC) is the most studied predictor, and the Human Connectome Project is the most employed dataset. A meta-analysis revealed a significant difference between the performance obtained in the prediction of general and fluid intelligence from fMRI data, confirming that the quality of measurement moderates this association. The expected performance of studies predicting general intelligence from fMRI was estimated to be r = 0.42 (CI_95% = [0.35, 0.50]) while for studies predicting fluid intelligence obtained from a single test, expected performance was estimated as r = 0.15 (CI_95% = [0.13, 0.17]). We further enumerate some virtues and pitfalls we identified in the methods for the assessment of intelligence and machine learning. The lack of treatment of confounder variables, including kinship, and small sample sizes were two common occurrences in the literature which increased risk of bias. Reporting quality was fair across studies, although reporting of results and discussion could be vastly improved. We conclude that the current literature on the prediction of intelligence from neuroimaging data is reaching maturity. Performance has been reliably demonstrated, although extending findings to new populations is indispensable. Current results could be used by future works to foment new theories on the biological basis of intelligence differences.

Sunday, October 03, 2021

‘Race-norming’ kept former NFL players from dementia diagnoses. Their families want answers.

'Race-norming' kept former NFL players from dementia diagnoses. Their families want answers.
https://www.washingtonpost.com/sports/2021/09/29/nfl-concussion-settlement-race-norming/

I believe this refers to demographically adjusted or Heaton neuropsych norms…which have occasionally been used inappropriately in Atkins ID death penalty cases.