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Post-power law of practice: Comparing static and dynamical models of skill acquisition.

Year: 2026

Type: article

Abstract: Since the 1980s, the power law has been the dominant view of the trajectory of skill acquisition. More recent research has challenged this "law," suggesting other models may better capture individual-level data. Furthermore, the motor learning and recovery literature suggests dynamical models might better capture nonmonotonic behavior and the effect of feedback. This study compares the fits of six models on data from two mirror-tracing experiments with different feedback metrics delivered through haptics. This includes two power models, the exponential model, a hybrid power and exponential model, and two more recent dynamical models that allow for nonmonotonic learning curves and can incorporate the role of feedback. Like others before, these results show that the "power law" model is not necessarily the best way to describe individual learning. However, none of the models examined showed a clear advantage in fitting individual-level data, and we discuss multiple reasons why this might be the case. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Source: Journal of Experimental Psychology Human Perception & Performance

DOI: 10.1037/xhp0001414

Authors: Charles R. Weeks, Michael D. Byrne, Shivam Pandey, Patrick B. Yee, Emery Engling, Priyanshu Agarwal, Jennifer L. Sullivan, Ziqin Zhu, Lianne R. Johnson, Marcia K. O'Malley

Institution: Rice University

Language: English

Topic: Motor Control and Adaptation

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: hybrid

Funder: National Science Foundation

OpenAlex ID: W7160856705

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