Individual Brain Charting: A high-resolution brain map of cognitive functions
https://www.humanbrainproject.eu/en/follow-hbp/news/individual-brain-charting-a-high-resolution-brain-map-of-cognitive-functions/
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Understanding the human brain clock & related neuroscience
Thursday, August 30, 2018
Individual Brain Charting: A high-resolution brain map of cognitive functions
Individual Brain Charting: A high-resolution brain map of cognitive functions
https://www.humanbrainproject.eu/en/follow-hbp/news/individual-brain-charting-a-high-resolution-brain-map-of-cognitive-functions/
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Monday, August 27, 2018
Intelligence and Uncertainty: Implications of Hierarchical Predictive Processing for the Neuroscience of Cognitive Ability
Intelligence and Uncertainty: Implications of Hierarchical Predictive Processing for the Neuroscience of Cognitive Ability
https://www.sciencedirect.com/science/article/pii/S0149763418302045
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Kevin McGrew, PhD
Educational Psychologist
Director, Institute for Applied Psychometrics
IAP
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Annals of the New York Academy of Sciences: Vol 1423, No 1
Annals of the New York Academy of Sciences: Vol 1423, No 1
https://nyaspubs.onlinelibrary.wiley.com/toc/17496632/2018/1423/1
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Saturday, August 25, 2018
The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5-21 year olds.
The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5-21 year olds.
https://www.ncbi.nlm.nih.gov/pubmed/30142446
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Sunday, August 19, 2018
A distributed brain network predicts general intelligence from resting-state human neuroimaging data.
A distributed brain network predicts general intelligence from resting-state human neuroimaging data.
https://www.ncbi.nlm.nih.gov/pubmed/30104429
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Kevin McGrew, PhD
Educational Psychologist
Director, Institute for Applied Psychometrics
IAP
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Friday, August 17, 2018
On the biological basis of musicality - Honing - 2018 - Annals of the New York Academy of Sciences
On the biological basis of musicality - Honing - 2018 - Annals of the New York Academy of Sciences
https://nyaspubs.onlinelibrary.wiley.com/doi/10.1111/nyas.13638
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Kevin McGrew, PhD
Educational Psychologist
Director, Institute for Applied Psychometrics
IAP
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Brain Networks Responsible for Naming Objects Identified
Brain Networks Responsible for Naming Objects Identified
https://neurosciencenews.com/object-naming-networks-9704/
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Saturday, August 11, 2018
Beyond IQ: Mining the “no-mans-land” between Intelligence and IQ: Journal of Intelligence special issue
I am pleased to see the Journal of Intelligence addressing the integration of non-cognitive variables (personality; self-beliefs; motivational constructs; often called the “no-mans land” between intelligence and personality— I believe this catchy phrase was first used by Stankov) with intellectual constructs to better understanding human performance. I have had a long-standing interest in such comprehensive models as reflected by my articulation of the Model of Academic Competence and Motivation (MACM) and repeated posting of “beyond IQ” information at my blogs.
Joel Schneider and I briefly touched in this topic in our soon to be published CHC intelligence theory update chapter. Below is the select text and some awesome figures crafted by Joel.
Our simplified conceptual structure of knowledge abilities is presented in Figure 3.10. At the center of overlapping knowledge domains is general knowledge—knowledge and skills considered important for any member of the population to know (e.g., literacy, numeracy, self-care, budgeting, civics, etiquette, and much more). The bulk of each knowledge domain is the province of specialists, but some portion is considered important for all members of society to know. Drawing inspiration from F. L. Schmidt (2011, 2014), we posit that interests and experience drive acquisition of domain-specific knowledge.
In Schmidt's model, individual differences in general knowledge are driven largely by individual differences in fluid intelligence and general interest in learning, also known as typical intellectual engagement (Goff & Ackerman, 1992). In contrast, individual differences in domain-specific knowledge are more driven by domain-specific in-terests, and also by the “tilt” of one's specific abilities (Coyle, Purcell, Snyder, & Richmond, 2014; Pässler, Beinicke, & Hell, 2015). In Figure 3.11, we present a simplified hypothetical synthesis of several ability models in which abilities, interests, and personality traits predict general and specific knowledge (Ackerman, 1996a, 1996b, 2000; Ackerman, Bowen, Beier, & Kanfer, 2001; Ackerman & Heggestad, 1997; Ackerman & Rolfhus, 1999; Fry & Hale, 1996; Goff & Ackerman, 1992; Kail, 2007; Kane et al., 2004; Rolfhus & Ackerman, 1999; Schmidt, 2011, 2014; Schneider et al., 2016; Schneider & Newman, 2015; Woodcock, 1993; Ziegler, Danay, Heene, Asendorpf, & Bühner, 2012).
Click on images to enlarge.
- Posted using BlogPress from my iPad
Joel Schneider and I briefly touched in this topic in our soon to be published CHC intelligence theory update chapter. Below is the select text and some awesome figures crafted by Joel.
Our simplified conceptual structure of knowledge abilities is presented in Figure 3.10. At the center of overlapping knowledge domains is general knowledge—knowledge and skills considered important for any member of the population to know (e.g., literacy, numeracy, self-care, budgeting, civics, etiquette, and much more). The bulk of each knowledge domain is the province of specialists, but some portion is considered important for all members of society to know. Drawing inspiration from F. L. Schmidt (2011, 2014), we posit that interests and experience drive acquisition of domain-specific knowledge.
In Schmidt's model, individual differences in general knowledge are driven largely by individual differences in fluid intelligence and general interest in learning, also known as typical intellectual engagement (Goff & Ackerman, 1992). In contrast, individual differences in domain-specific knowledge are more driven by domain-specific in-terests, and also by the “tilt” of one's specific abilities (Coyle, Purcell, Snyder, & Richmond, 2014; Pässler, Beinicke, & Hell, 2015). In Figure 3.11, we present a simplified hypothetical synthesis of several ability models in which abilities, interests, and personality traits predict general and specific knowledge (Ackerman, 1996a, 1996b, 2000; Ackerman, Bowen, Beier, & Kanfer, 2001; Ackerman & Heggestad, 1997; Ackerman & Rolfhus, 1999; Fry & Hale, 1996; Goff & Ackerman, 1992; Kail, 2007; Kane et al., 2004; Rolfhus & Ackerman, 1999; Schmidt, 2011, 2014; Schneider et al., 2016; Schneider & Newman, 2015; Woodcock, 1993; Ziegler, Danay, Heene, Asendorpf, & Bühner, 2012).
Click on images to enlarge.
- Posted using BlogPress from my iPad
Friday, August 10, 2018
Study: Brain training games could be used to assess cognitive abilities, replace the Mini-Mental State Exam (MMSE)
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Study: Brain training games could be used to assess cognitive abilities, replace the Mini-Mental State Exam (MMSE)
// SharpBrains
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The Use of Mobile Games to Assess Cognitive Function of Elderly with and without Cognitive Impairment (Journal of Alzheimer's Disease):
Abstract: In the past few years numerous mobile games have been developed to train the brain. There is a lack of information about the relation between the scores obtained in these games and the cognitive abilities of the patients. The aim of this study was to determine whether or not mobile games can be used to assess cognitive abilities of elderly. Twenty healthy young adults, 29 old patients with cognitive impairments (Mini-Mental State Exam (MMSE) [20- 24]) and 27-aged controls participated in this study. Scores obtained in 7 mobile games were correlated with MMSE and the Addenbrooke's Cognitive Evaluation revised (ACE-R). Statistically significant differences were found for all games between patients with cognitive impairments and the aged controls. Correlations between the average scores of the games and the MMSE and ACE-R are significant (R = 0.72 [p < 0.001] and R = 0.81 [p < 0.001], respectively). Scores of cognitive mobile games could be used as an alternative to MMSE and ACE-R to evaluate cognitive function of aged people with and without cognitive impairment at least when MMSE is higher than 20/30.
Study in context:
- What are cognitive abilities and how to boost them?
- Brainnovations Winner Jan Samzelius on why monitoring Typing Cadence may help detect early Parkinson's and Alzheimer's Disease
- Firms Race to Find New Ways to Scan Brain Health
- Solving the Brain Fitness Puzzle Is the Key to Self-Empowered Aging
- Five reasons the future of brain enhancement is digital, pervasive and (hopefully) bright
- Save the Date for 2018 SharpBrains Virtual Summit: Investing in Brain Health for All (December 4–6th)
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Kevin McGrew, PhD
Educational Psychologist
Director, Institute for Applied Psychometrics
IAP
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Thursday, August 09, 2018
Saturday, August 04, 2018
Children's academic attainment is linked to the global organization of the white matter connectome - Bathelt - - Developmental Science
Children's academic attainment is linked to the global organization of the white matter connectome - Bathelt - - Developmental Science
https://onlinelibrary.wiley.com/doi/full/10.1111/desc.12662
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Kevin McGrew, PhD
Educational Psychologist
Director, Institute for Applied Psychometrics
IAP
******************************************************
Thursday, August 02, 2018
Is auditory perceptual timing a core deficit of developmental coordination disorder? - Trainor - 2018 - Annals of the New York Academy of Sciences
Is auditory perceptual timing a core deficit of developmental coordination disorder? - Trainor - 2018 - Annals of the New York Academy of Sciences
https://nyaspubs.onlinelibrary.wiley.com/doi/full/10.1111/nyas.13701
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