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Updated: Jul 3, 2025

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
Published on: February 12, 2017
How do humans learn about the reliability of automation?
Luke Strickland1, Simon Farrell2, Micah K Wilson3
1The Future of Work Institute, Curtin University, 78 Murray Street, Perth, 6000, Australia. luke.strickland@curtin.edu.au.
Abstract:
In a range of settings, human operators make decisions with the assistance of automation, the reliability of which can vary depending upon context. Currently, the processes by which humans track the level of reliability of automation are unclear. In the current study, we test cognitive models of learning that could potentially explain how humans track automation reliability. We fitted several alternative cognitive models to a series of participants' judgements of automation reliability observed in a maritime classification task in which participants were provided with automated advice. We examined three experiments including eight between-subjects conditions and 240 participants in total. Our results favoured a two-kernel delta-rule model of learning, which specifies that humans learn by prediction error, and respond according to a learning rate that is sensitive to environmental volatility. However, we found substantial heterogeneity in learning processes across participants. These outcomes speak to the learning processes underlying how humans estimate automation reliability and thus have implications for practice.
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