Inhibitory Cognitive Control Allows Automated Advice to Improve Accuracy While Minimizing Misuse
Abstract
Humans increasingly use automated decision aids. However, environmental uncertainty means that automated advice can be incorrect, creating the potential for humans to act on incorrect advice or to disregard correct advice. We present a quantitative model of the cognitive process by which humans use automation when deciding whether aircraft would violate requirements for minimum separation. The model closely fitted the performance of 24 participants, who each made 2,400 conflict-detection decisions (conflict vs. nonconflict), either manually (with no assistance) or with the assistance of 90% reliable automation. When the decision aid was correct, conflict-detection accuracy improved, but when the decision aid was incorrect, accuracy and response time were impaired. The model indicated that participants integrated advice into their decision process by inhibiting evidence accumulation toward the task response that was incongruent with that advice, thereby ensuring that decisions could not be made solely on automated advice without first sampling information from the task environment.
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- Open Data
All data and analysis code have been made publicly available via OSF and can be accessed at https://osf.io/28g3j. The design and analysis plan for the study were not preregistered. This article has received the badge for Open Data. More information about the Open Practices badges can be found at http://www.psychologicalscience.org/publications/badges.
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Action Editor: Sachiko Kinoshita
Editor: Patricia J. Bauer
Author Contributions
L. Strickland, S. Loft, V. K. Bowden, R. J. Boag, M. K. Wilson, and A. Heathcote designed the study. L. Strickland analyzed the data. S. Khan conducted testing and data collection. L. Strickland wrote the manuscript, and S. Loft, V. K. Bowden, R. J. Boag, M. K. Wilson, and A. Heathcote provided critical feedback and revisions. All the authors approved the final manuscript for submission.
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Article first published online: September 27, 2021
Issue published: November 2021
Keywords
Open practices
- Open Data
PubMed: 34570615
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