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Comparing models of learning and relearning in large-scale cognitive training data sets

Psychology

Comparing models of learning and relearning in large-scale cognitive training data sets

A. Kumar, A. S. Benjamin, et al.

This groundbreaking study by Aakriti Kumar, Aaron S. Benjamin, Andrew Heathcote, and Mark Steyvers delves into how we learn and relearn in real-world settings, analyzing data from over 39,000 individuals on the Lumosity platform. The findings reveal a nuanced interplay between long-term skill acquisition and task preparedness that could reshape our understanding of cognitive training.

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Playback language: English
Abstract
This study investigates learning and relearning in naturalistic settings using data from 39,157 individuals playing two cognitive games on the Lumosity platform over five years. The researchers analyzed highly varied practice schedules with uncontrolled interruptions, concluding that long-term learning is best described by a combination of long-term skill and task-set preparedness. Rapid relearning, even after extended breaks, necessitates an interactive model incorporating both skill and task-set preparedness. The study highlights the importance of using naturalistic learning data to test and refine theoretical accounts of learning.
Publisher
npj Science of Learning
Published On
Oct 04, 2022
Authors
Aakriti Kumar, Aaron S. Benjamin, Andrew Heathcote, Mark Steyvers
Tags
learning
relearning
cognitive games
naturalistic settings
skill acquisition
task preparedness
Lumosity

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