Comparing models of learning and relearning in large-scale cognitive training data sets Article Swipe
Aakriti Kumar
,
Aaron S. Benjamin
,
Andrew Heathcote
,
Mark Steyvers
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1038/s41539-022-00142-x
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1038/s41539-022-00142-x
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41539-022-00142-x
- https://www.nature.com/articles/s41539-022-00142-x.pdf
- OA Status
- gold
- Cited By
- 10
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4301394884
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4301394884Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41539-022-00142-xDigital Object Identifier
- Title
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Comparing models of learning and relearning in large-scale cognitive training data setsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-04Full publication date if available
- Authors
-
Aakriti Kumar, Aaron S. Benjamin, Andrew Heathcote, Mark SteyversList of authors in order
- Landing page
-
https://doi.org/10.1038/s41539-022-00142-xPublisher landing page
- PDF URL
-
https://www.nature.com/articles/s41539-022-00142-x.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41539-022-00142-x.pdfDirect OA link when available
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Suite, Computer science, Artificial intelligence, Robustness (evolution), Machine learning, Cognition, Cognitive psychology, Task (project management), Psychology, Engineering, Archaeology, Neuroscience, Biochemistry, History, Gene, Chemistry, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 2, 2023: 4Per-year citation counts (last 5 years)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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