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

Aakriti Kumar, Aaron S. Benjamin, Andrew Heathcote, Mark Steyvers

Research output: Contribution to journalArticlepeer-review


Practice in real-world settings exhibits many idiosyncracies of scheduling and duration that can only be roughly approximated by laboratory research. Here we investigate 39,157 individuals’ performance on two cognitive games on the Lumosity platform over a span of 5 years. The large-scale nature of the data allows us to observe highly varied lengths of uncontrolled interruptions to practice and offers a unique view of learning in naturalistic settings. We enlist a suite of models that grow in the complexity of the mechanisms they postulate and conclude that long-term naturalistic learning is best described with a combination of long-term skill and task-set preparedness. We focus additionally on the nature and speed of relearning after breaks in practice and conclude that those components must operate interactively to produce the rapid relearning that is evident even at exceptionally long delays (over 2 years). Naturalistic learning over long time spans provides a strong test for the robustness of theoretical accounts of learning, and should be more broadly used in the learning sciences.

Original languageEnglish (US)
Article number24
Journalnpj Science of Learning
Issue number1
StatePublished - Dec 2022

ASJC Scopus subject areas

  • Education
  • Developmental Neuroscience


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