Mathematical model bridges disparate timescales of lifelong learning

TL;DR

This paper introduces a minimal mathematical model that unifies the short-term and long-term dynamics of lifelong learning, integrating motivation, fatigue, skill acquisition, mastery, and abandonment.

Contribution

It provides a unified quantitative framework that connects different timescales of learning, bridging gaps between existing models focused on isolated timescales.

Findings

Model recovers classic skill acquisition patterns

Explores effects of various training regimes

Characterizes individual differences in motivation

Abstract

Lifelong learning occurs on timescales ranging from minutes to decades. People can lose themselves in a new skill, practicing for hours until exhausted. And they can pursue mastery over days or decades, perhaps abandoning old skills entirely to seek out new challenges. A full understanding of learning requires an account that integrates these timescales. Here, we present a minimal quantitative model that unifies the nested timescales of learning. Our dynamical model recovers classic accounts of skill acquisition, and describes how learning emerges from moment-to-moment dynamics of motivation, fatigue, and work, while also situated within longer-term dynamics of skill selection, mastery, and abandonment. We apply this model to explore the benefits and pitfalls of a variety of training regimes and to characterize individual differences in motivation and skill development. Our model connects previously disparate timescales -- and the subdisciplines that typically study each timescale in isolation -- to offer a unified account of the timecourse of skill acquisition.