Thursday, November 07, 2019

Individual differences in learning efficiency


Kathleen B. McDermott and Christopher L. Zerr

Abstract 

Most research on long-term memory uses an experimental approach whereby participants are assigned to different conditions, and condition means are the measures of interest. This approach has demonstrated repeatedly that conditions that slow the rate of learning tend to improve later retention. A neglected question is whether aggregate findings at the level of the group (i.e., slower learning tends to improve retention) translate to the level of individual people. We identify a discrepancy whereby—across people—slower learning tends to coincide with poorer memory. The positive relation between learning rate (speed of learning) and retention (amount remembered after a delay) across people is referred to as learning efficiency. A more efficient learner can acquire information faster and remember more of it over time. We discuss potential characteristics of efficient learners and consider future directions for research.

Keywords learning efficiency, individual differences, memory, learning rate, retention

A few select quotes below.  Dr. Joel Schneider and I have written elsewhere that we believe that attentional control (AC; a key mechanism of working memory or Gwm) is a key cognitive mechanism in learning and cognitive functioning.

Learning strategy differences:  Faster learners generate more mediators while learning, and these mediators tend to be both implemented earlier in the learning process and more effective in aiding memory 

Prior knowledge:  However, prior knowledge (or crystallized intelligence) may still promote integration of new information into existing knowledge and improve the efficacy of learning strategies—the more knowledge someone possesses, the richer the set of potential mediators.

Attentional control:  People who are better able to focus their attention are less susceptible to interfering information; further, they more quickly search long-term memory when retrieving information (Unsworth & Spillers, 2010). Neuroimaging evidence suggests that the ability to control one's attention is a potential driver of efficient learning. 


Saturday, September 21, 2019

All you need is g? Predicting piano skill acquisition in beginners: The role of general intelligence, music aptitude, and mindset


Abstract
;  This study was designed to investigate sources of individual differences in musical skill acquisition. We had 171 undergraduates with little or no piano-playing experience attempt to learn a piece of piano music with the aid of a video-guide, and then, following practice with the guide, attempt to perform the piece from memory. A panel of musicians evaluated the performances based on their melodic and rhythmic accuracy. Participants also completed tests of working memory capacity, fluid intelligence, crystallized intelligence, processing speed, and two tests of music aptitude (the Swedish Music Discrimination Test and the Advanced Measures of Music Audiation). Measures of general intelligence and music aptitude correlated significantly with skill acquisition, but mindset did not. Structural equation modeling revealed that general intelligence, music aptitude, and mindset together accounted for 22.4% of the variance in skill acquisition. However, only general intelligence contributed significantly to the model (β = 0.44, p < .001). The contributions of music aptitude (β = 0.08, p = .39) and mindset (β = −0.06, p = .50) were non-significant after accounting for general intelligence. We also found that openness to experience did not significantly predict skill acquisition or music aptitude. Overall, the results suggest that after accounting for individual differences in general intelligence, music aptitude and mindset do not predict piano skill acquisition in beginners.




 


Tuesday, September 17, 2019

Digit Span Subscale Scores May Be Insufficiently Reliable for Clinical Interpretation: Distinguishing Between Stratified Coefficient Alpha and Omega Hierarchical - Gilles E. Gignac, Matthew R. Reynolds, Kristof Kovacs, 2019



Digit Span Subscale Scores May Be Insufficiently Reliable for Clinical Interpretation: Distinguishing Between Stratified Coefficient Alpha and Omega Hierarchical - Gilles E. Gignac, Matthew R. Reynolds, Kristof Kovacs, 2019
https://journals.sagepub.com/doi/full/10.1177/1073191117748396

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Kevin McGrew, PhD
Educational Psychologist
Director, Institute for Applied Psychometrics
IAP
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