Research Byte: #Cognitive augmentation: #AI enhanced tools for supporting individuals with cognitive #disabilities - #schoolpsychology #SPED #cognitive

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Journal web page found here.

Let’s hear it for #conative (#noncognitive) variables in understanding learning—#CAMML #aptitude #traitcomplexes #cognitive #affective #motivation #schoolpsychology

 Variation in the intensity and consistency of attention during learning: The role of conative factors

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Abstract

The present study examined whether conative factors (e.g., self-efficacy, self-set goal difficulty, and task-specific motivation) are reliable predictors of learning and memory abilities and whether any observed relationships could be explained by two related, yet distinct aspects of attention. Specifically, the present study examined whether the relationship between conative factors and overall learning performance is explained by attentional intensity (the amount of attention allocated to a task) and attentional consistency (the consistency with which attention is allocated to said task). In two studies (N’s > 160), participants completed a paired associate’s (PA) cued recall task while pupil diameter was simultaneously recorded to provide an index of the intensity of attention. Measures of working memory, general episodic long-term memory, task-specific motivation, and memory self-efficacy were also included. Study 2 adopted a similar procedure but embedded thought probes into the encoding phase of each list to provide an index of the consistency of attention. Study 2 also added measures of self-set goal difficulty and effective strategy use. Results suggested that all conative factors were related to intensity and consistency in challenging learning contexts. Furthermore, intensity, consistency, and the variance shared between self-efficacy and self-set goal difficulty (r = 0.86) each explained substantial unique variance in learning when controlling for the influence of other important predictors. Overall, results suggest conative factors are important for understanding individual differences in learning and memory abilities, and part of the reason why these factors are associated with improved learning outcomes is due to intensity and consistency.

Comment:  I’ve always believed that conative (non-cognitive) individual difference variables should receive just as much attention as cognitive variables in understanding learning.  In fact, in an invited article, I recently proposed the CAMML (cognitive-affective-motivation model of learning) “crossing the rubicon” model of learning that integrates conative (motivation and self-regulated learning), affective (Big 5 personality) and cognitive (CHC) variables in an overarching framework (building on Richard Snow’s concept of aptitude-trait complexes).  Click here to download or read the CAMML article.  Below are the two key figures for understanding the CAMML model.

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Research Byte: #Cognitive Factors Underlying #Mathematical Skills: A Systematic Review and #MetaAnalysis - relevant for #schoolpsychology

Cognitive Factors Underlying Mathematical Skills: A Systematic Review and Meta-Analysis.  

Amland, T., Grande, G., Scherer, R., Lervåg, A., & Melby-Lervåg, M. (2024). Cognitive factors underlying mathematical skills: A systematic review and meta-analysis.Psychological Bulletin.Advance online publication. 

(click to download and read—open access)

Abstract

In understanding the nature of mathematical skills, the most influential theories suggest that mathematical cognition draws on different systems: numerical, linguistic, spatial, and general cognitive skills. Studies show that skills in these areas are highly predictive of outcomes in mathematics. Nonetheless, the strength of these relations with mathematical achievement varies, and little is known about the moderators or relative importance of each predictor. Based on 269 concurrent and 174 longitudinal studies comprising 2,696 correlations, this meta-analysis summarizes the evidence on cognitive predictors of mathematical skills in children and adolescents. The results showed that nonsymbolic number skills (often labeled approximate number sense) correlate significantly less with mathematical achievement than symbolic number skills and that various aspects of language relate differently to mathematical outcomes. We observed differential predictive patterns for arithmetic and word problems, and these patterns only partly supported the theory of three pathways—quantitative, linguistic, and spatial—for mathematical skills. Concurrently, nonsymbolic number and phonological skills were weak but exclusive predictors of arithmetic skills, whereas nonverbal intelligence quotient (IQ) predicted word problems only. Only symbolic number skills predicted both arithmetic and word problems concurrently. Longitudinally, symbolic number skills, spatial ability, and nonverbal IQ predicted both arithmetic and word problems, whereas language comprehension was important for word problem solving only. As in the concurrent data, nonsymbolic number skill was a weak longitudinal predictor of arithmetic skills. We conclude that the candidates to target in intervention studies are symbolic number skills and language comprehension. It is uncertain whether the two other important predictors, nonverbal IQ and spatial skills, are actually malleable.

Public Significance Statement 

This systematic review and meta-analysis found that symbolic number skills, language comprehension, and nonverbal reasoning skills are the most important foundational skills of achievement in mathematics in childhood and early adolescence. Children's understanding of digits and number words seems to be the most promising target to design content that can be tested in future intervention studies. Moreover, whether interventions targeting language comprehension could benefit children struggling with mathematical word problems should be further examined. Mathematical skills is a fundamental factor both for a productive society and for individual development and employment and finding ways that might increase mathematical abilities can potentially have great consequences.

Keywords: mathematics achievement, language, spatial ability, number sense, meta-analytic structural equation modeling

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Research byte: Good article with summary of the major #cognitive #neuroscience models of human #intelligence

OPINIONVolume 22, Issue 1P8-20January 2018

Network Neuroscience Theory of Human Intelligence
Aron K. Barbey.  Open access available for download

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#Intelligence (#IQ) #cognitive testing in perspective: An #ecological systems brief video explanation—useful for #schoolpsychology

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An oldie but goodie!  This is a 19+ minute narrated video (sit down with your favorite beverage and enjoy) where I explain how intelligence (IQ) or cognitive ability testing should be better understood in the context of a larger ecological systems model perspective (Bronfenbrenner).  

I first posted the video in 2015—-9 years ago! So be gentle…I’m much better with these videos now :) Thus, some of my COI statements/disclaimers/affiliations are no longer accurte (and updated version can be found a theMindHub.com—Under About IAP: The Director: Disclosures & Bio).

If all works well, just click the start arrow on the video screen…and tap the enlarge icon in the lower right corner.  This video is now hosted on YouTube, so it may be possible that you may first encounter 1-2 very brief adds that you can skip within the first 15-10 seconds.  It is possible (it seems to vary everytime) that you might be asked to “sign in” to show you are not a bot.  All you need to do is press the message, or if images of muliptle videos appear, press the first one…if you only get the message you may need to back up and try link again (no signing in….I hate having lost control of how these work by using YouTube 9 years ago…as now the starting has these mild annoyances..but it is the price for a free service).  Be aware that some of the first 4-5 slides may have minimal or no narration and you can skip ahead to the beginning…it is the first slide shown immediately below before the video. Given the caveats above, it is possible the video might not deploy exactly how I describe…the platform seems to be a bit tempormental, at least for me.  Enjoy.

A massive #dataset of the #NeuroCognitive Performance Test, a web-based #cognitive assessment

A massive dataset of the NeuroCognitive Performance Test, a web-based cognitive assessment

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Paul I. Jaffe , Aaron  Kaluszka, Nicole  F.  Ng & Robert  J.  Schafer  

We present a dataset of approximately 5.5 million subtest scores from over 750,000 adults who
completed the NeuroCognitive Performance test (NCPt; Lumos Labs, Inc.), a validated, self- administered cognitive test accessed via web browser. the dataset includes assessment scores from eight test batteries consisting of 5–11 subtests that collectively span several cognitive domains including working memory, visual attention, and abstract reasoning. In addition to the raw scores and normative data from each subtest, the dataset includes basic demographic information from each participant (age, gender, and educational background). the scale and diversity of the dataset provides an unprecedented opportunity for researchers to investigate population-level variability in cognitive abilities and their relation to demographic factors. to facilitate reuse of this dataset by other researchers, we provide a Python module that supports several common preprocessing steps.