A first-principles mathematical model integrates the disparate timescales of human learning

Lifelong learning occurs on timescales ranging from moments to decades. People can lose themselves in a new skill, practice for hours until exhausted, and pursue mastery intermittently over decades. A full understanding of learning requires an account that integrates these timescales. Here, in response to calls for more formal theory in the psychological sciences, we present a parsimonious mathematical model that unifies the nested timescales of learning. Our model recovers well-established patterns of skill acquisition, and explains how these patterns can emerge from short-timescale dynamics of motivation, fatigue, and effort. Conversely, the model explains how patterns in these short-timescale dynamics are shaped by longer-term dynamics of skill selection, mastery, and abandonment. We use this model to explore the theoretical benefits and pitfalls of a variety of training regimes. Our model connects disparate timescales—and the subdisciplines that typically study each timescale in isolation—to offer a unified, multiscale account of skill acquisition.

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Publication Details

Journal
npj Complexity
Published
2025-05-02
DOI
https://doi.org/10.1038/s44260-025-00039-x
Citations
2
Primary Topic
Innovative Teaching and Learning Methods
Type
article
Field-Weighted Citation Impact
6.89

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article

A first-principles mathematical model integrates the disparate timescales of human learning

Vicky Chuqiao Yang, Tyler Marghetis, Mingzhen Lu
2 citations
npj Complexity
Innovative Teaching and Learning Methods
6.89
article

A first-principles mathematical model integrates the disparate timescales of human learning

Vicky Chuqiao Yang, Tyler Marghetis, Mingzhen Lu
article en
2 citations

Abstract

Lifelong learning occurs on timescales ranging from moments to decades. People can lose themselves in a new skill, practice for hours until exhausted, and pursue mastery intermittently over decades. A full understanding of learning requires an account that integrates these timescales. Here, in response to calls for more formal theory in the psychological sciences, we present a parsimonious mathematical model that unifies the nested timescales of learning. Our model recovers well-established patterns of skill acquisition, and explains how these patterns can emerge from short-timescale dynamics of motivation, fatigue, and effort. Conversely, the model explains how patterns in these short-timescale dynamics are shaped by longer-term dynamics of skill selection, mastery, and abandonment. We use this model to explore the theoretical benefits and pitfalls of a variety of training regimes. Our model connects disparate timescales—and the subdisciplines that typically study each timescale in isolation—to offer a unified, multiscale account of skill acquisition.

npj ComplexityVol. 2(1)
Santa Fe Institute (US), Mercer University (US), University of California, Merced (US), New York University (US), Massachusetts Institute of Technology (US)
James S. McDonnell Foundation, Santa Fe Institute, Science Foundation Ireland
Quality Education
Openalex Percentile: Top 4%
Innovative Teaching and Learning Methods
6.89
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