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Abstract

The "Law of Practice" - a simple nonlinear function describing the relation-ship between mean response time (RT) and practice - has provided a practicallyand theoretically useful way of quantifying the speed-up that characterizes skill ac-quisition. Early work favored a power law, but this was shown to be an artifact ofbiases caused by averaging over participants who are individually better describedby an exponential law. However, both power and exponential functions make thestrong assumption that the speedup always proceeds at a steadily decreasing rate,even though there are sometimes clear exceptions. We propose a new law that canboth accommodate an initial delay resulting in a slower-faster-slower rate of learning,with either power or exponential forms as limiting cases, and which can account fornot only mean RT but also the eect of practice on the entire distribution of RT.We evaluate this proposal with data from a broad array of tasks using hierarchicalBayesian modeling, which pools data across participants while minimizing averagingartifacts, and using inference procedures that take into account dierences in exi-bility among laws. In a clear majority of paradigms our results supported a delayedexponential law.

Item Type:	Article
Authors/Creators:	Evans, NJ and Brown, SD and Mewhort, DJK and Heathcote, A
Keywords:	Law of practice, learning, skill acquisition, bayesian hierarchical models
Journal or Publication Title:	Psychological Review
Publisher:	American Psychological Association
ISSN:	0033-295X
DOI / ID Number:	10.1037/rev0000105
Copyright Information:	Copyright 2018 American Psychological Association. This paper is not the copy of record and may not exactly replicate the final, authoritative version of the article. Please do not copy or cite without authors permission. The nal article will be available, upon publication, via its DOI: 10.1037/rev0000105
Related URLs:	Google Scholar: Heathcote, A UTAS Profile: Heathcote, A
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