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Gv-IM: Has the time finally come for Gv visual imagery tests?
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In our 2018 CHC chapter Joel Schneider and I made our long standing affection for visual imagery (Gv-IM per CHC taxonomy) very clear. See text excerpt below. (double click on images to enlarge)
Today I ran across a potential free PDF book (via Research Gate) that may make IM tests viable. There is hope.
from IQ's Corner https://ift.tt/3xrh9b0
via IFTTT https://ift.tt/3aBERXW
On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting | bioRxiv https://ift.tt/3jBCPvt 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 (RSF...
A Review of Key Likert Scale Development Advances: 1995–2019 https://ift.tt/3bMqEYX ****************************************** Kevin S. McGrew, PhD Educational & School Psychologist Director Institute for Applied Psychometrics (IAP) https://www.themindhub.com ****************************************** from IQ's Corner https://ift.tt/3fc4iSK via IFTTT https://ift.tt/3vekQPs
Estimates of brain age for gray matter and white matter in younger and older adults: insights into human intelligence - ScienceDirect https://ift.tt/38ZczGd Abstract Aging entails a multifaceted complex of changes in macro- and micro-structural properties of human brain gray matter (GM) and white matter (WM) tissues, as well as in intellectual abilities. To better capture tissue-specific brain aging, we combined volumetric and diffusivity properties to derive subject-specific age scores for each tissue. We compared age-related variance between WM and GM age scores in younger and older adults and tested whether tissue-specific age scores could explain different effects of aging on fluid (G f ) and crystalized (G c ) intelligence in younger and older adults. Chronological age was strongly associated with GM ( R 2 = 0.73) and WM ( R 2 = 0.57) age scores. The GM age score accounted for significantly more variance in chronological age in younger relative to older ...
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