Presentation
61. Post-Power Law of Practice: Comparing Newer Models of Human Learning
SessionPoster Session 1
DescriptionNewell and Rosenbloom's (1981) conceptualization of skill acquisition as being described by a power function is so widely accepted that it is now often referred to as "the power law of practice." However, this view has been challenged by subsequent research by Heathcote et al. (2000) who found exponential functions fit better to individual-level data.
This poster introduces a novel approach using dynamical models to represent learning as a function of an internal state that evolves over time. Unlike traditional static models, these dynamical models allow for non-monotonic learning curves and explicitly incorporate feedback mechanisms, potentially offering a more nuanced understanding of how learning unfolds.
This research compares the efficacy of static and dynamical models in representing both individual and averaged learning curves across a dataset involving a motor learning task.
This poster introduces a novel approach using dynamical models to represent learning as a function of an internal state that evolves over time. Unlike traditional static models, these dynamical models allow for non-monotonic learning curves and explicitly incorporate feedback mechanisms, potentially offering a more nuanced understanding of how learning unfolds.
This research compares the efficacy of static and dynamical models in representing both individual and averaged learning curves across a dataset involving a motor learning task.
Event Type
Poster
TimeWednesday, September 11th5:30pm - 6:30pm MST
LocationMcArthur Ballroom
Aerospace Systems
Cognitive Engineering & Decision Making
Computer Systems
Forensics Professional
Health Care
Human Performance Modeling
Individual Differences in Performance
Perception and Performance
Product Design
Safety
Training
Usability and System Evaluation
Extended Reality