How do humans learn about the reliability of automation?
dc.contributor.author | Strickland, Luke | |
dc.contributor.author | Farrell, S. | |
dc.contributor.author | Wilson, Micah | |
dc.contributor.author | Hutchinson, J. | |
dc.contributor.author | Loft, S. | |
dc.date.accessioned | 2024-04-09T09:26:53Z | |
dc.date.available | 2024-04-09T09:26:53Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Strickland, L. and Farrell, S. and Wilson, M.K. and Hutchinson, J. and Loft, S. 2024. How do humans learn about the reliability of automation? Cognitive Research: Principles and Implications. 9 (1): 8. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/94790 | |
dc.identifier.doi | 10.1186/s41235-024-00533-1 | |
dc.description.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. | |
dc.language | eng | |
dc.relation.sponsoredby | http://purl.org/au-research/grants/arc/DE230100171 | |
dc.relation.sponsoredby | http://purl.org/au-research/grants/arc/FT190100812 | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Automation reliability | |
dc.subject | Cognitive model | |
dc.subject | Human-automation teaming | |
dc.subject | Learning | |
dc.subject | Humans | |
dc.subject | Task Performance and Analysis | |
dc.subject | Reproducibility of Results | |
dc.subject | Learning | |
dc.subject | Judgment | |
dc.subject | Automation | |
dc.subject | Humans | |
dc.subject | Reproducibility of Results | |
dc.subject | Learning | |
dc.subject | Judgment | |
dc.subject | Task Performance and Analysis | |
dc.subject | Automation | |
dc.title | How do humans learn about the reliability of automation? | |
dc.type | Journal Article | |
dcterms.source.volume | 9 | |
dcterms.source.number | 1 | |
dcterms.source.issn | 2365-7464 | |
dcterms.source.title | Cognitive Research: Principles and Implications | |
dc.date.updated | 2024-04-09T09:26:48Z | |
curtin.department | Future of Work Institute | |
curtin.accessStatus | Open access | |
curtin.faculty | Faculty of Business and Law | |
curtin.contributor.orcid | Strickland, Luke [0000-0002-6071-6022] | |
curtin.contributor.orcid | Wilson, Micah [0000-0003-4143-7308] | |
curtin.identifier.article-number | 8 | |
dcterms.source.eissn | 2365-7464 | |
curtin.repositoryagreement | V3 |