How do humans learn about the reliability of automation?
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 lea...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DE230100171 http://hdl.handle.net/20.500.11937/94790 |
| _version_ | 1848765924512890880 |
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| author | Strickland, Luke Farrell, S. Wilson, Micah Hutchinson, J. Loft, S. |
| author_facet | Strickland, Luke Farrell, S. Wilson, Micah Hutchinson, J. Loft, S. |
| author_sort | Strickland, Luke |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:42:59Z |
| format | Journal Article |
| id | curtin-20.500.11937-94790 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | eng |
| last_indexed | 2025-11-14T11:42:59Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-947902024-05-09T08:10:02Z How do humans learn about the reliability of automation? Strickland, Luke Farrell, S. Wilson, Micah Hutchinson, J. Loft, S. Automation reliability Cognitive model Human-automation teaming Learning Humans Task Performance and Analysis Reproducibility of Results Learning Judgment Automation Humans Reproducibility of Results Learning Judgment Task Performance and Analysis Automation 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. 2024 Journal Article http://hdl.handle.net/20.500.11937/94790 10.1186/s41235-024-00533-1 eng http://purl.org/au-research/grants/arc/DE230100171 http://purl.org/au-research/grants/arc/FT190100812 http://creativecommons.org/licenses/by/4.0/ fulltext |
| spellingShingle | Automation reliability Cognitive model Human-automation teaming Learning Humans Task Performance and Analysis Reproducibility of Results Learning Judgment Automation Humans Reproducibility of Results Learning Judgment Task Performance and Analysis Automation Strickland, Luke Farrell, S. Wilson, Micah Hutchinson, J. Loft, S. How do humans learn about the reliability of automation? |
| title | How do humans learn about the reliability of automation? |
| title_full | How do humans learn about the reliability of automation? |
| title_fullStr | How do humans learn about the reliability of automation? |
| title_full_unstemmed | How do humans learn about the reliability of automation? |
| title_short | How do humans learn about the reliability of automation? |
| title_sort | how do humans learn about the reliability of automation? |
| topic | Automation reliability Cognitive model Human-automation teaming Learning Humans Task Performance and Analysis Reproducibility of Results Learning Judgment Automation Humans Reproducibility of Results Learning Judgment Task Performance and Analysis Automation |
| url | http://purl.org/au-research/grants/arc/DE230100171 http://purl.org/au-research/grants/arc/DE230100171 http://hdl.handle.net/20.500.11937/94790 |