Humans learn by breaking through and plateauing, persisting and resting, and, occasionally, experiencing the blissful flow state. Mastering a skill can take decades, but the learning process unfolds across multiple timescales, from mere moments to days. Researchers have tended to study either the trajectory of lifelong mastery, where learning can appear as a steady, gradual process, or the effects of practice in short, repeated bouts. Rarely are these timescales studied in tandem, but according to the authors of a new study in npj Complexity, “the dynamics of each timescale only make sense in the light of the other.”
The study, authored by three former SFI Postdoctoral Fellows — lead author Mingzhen Lu (NYU), Tyler Marghetis (UC Merced), and Vicky Chuquiao Yang (MIT) — captures the different timescales of learning in a unified theoretical model that offers a task-agnostic view of skill acquisition. The model characterizes the motivation, fatigue, and engagement that drive individual practice sessions as well as elements that shape long-term growth, like skill level and task difficulty.
The study, which began as a conversation during SFI's 2020 Postdocs in Complexity conference, is a response to calls for more formal theory in the psychological sciences. By consolidating the disparate timescales of learning into a unified model, the research offers a new perspective on human learning and a framework for exploring the theoretical benefits and pitfalls of different training regimes.
Read the study "A first-principles mathematical model integrates the 1 disparate timescales of human learning," by Mingzhen Lu, Tyler Marghetis, and Vicky Chuquiao Yang, in npj Complexity (May 2, 2025). DOI: 10.1038/s44260-025-00039-x