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.
Authors
- Vicky Chuqiao Yang (ORCID: https://orcid.org/0000-0001-6111-3699)
- Tyler Marghetis (ORCID: https://orcid.org/0000-0001-9649-6378)
- Mingzhen Lu (ORCID: https://orcid.org/0000-0002-8707-8745)
Institutions
- Santa Fe Institute (US)
- Mercer University (US)
- University of California, Merced (US)
- New York University (US)
- Massachusetts Institute of Technology (US)
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
Funders
- James S. McDonnell Foundation
- Santa Fe Institute
- Science Foundation Ireland