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How do humans learn about the reliability of automation?
Strickland, Luke; Farrell, Simon; Wilson, Micah K; Hutchinson, Jack; Loft, Shayne.
Afiliación
  • Strickland L; The Future of Work Institute, Curtin University, 78 Murray Street, Perth, 6000, Australia. luke.strickland@curtin.edu.au.
  • Farrell S; The School of Psychological Science, The University of Western Australia, Crawley, Perth, Australia.
  • Wilson MK; The Future of Work Institute, Curtin University, 78 Murray Street, Perth, 6000, Australia.
  • Hutchinson J; The School of Psychological Science, The University of Western Australia, Crawley, Perth, Australia.
  • Loft S; The School of Psychological Science, The University of Western Australia, Crawley, Perth, Australia.
Cogn Res Princ Implic ; 9(1): 8, 2024 02 16.
Article en En | MEDLINE | ID: mdl-38361149
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Análisis y Desempeño de Tareas / Aprendizaje Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Cogn Res Princ Implic Año: 2024 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Análisis y Desempeño de Tareas / Aprendizaje Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Cogn Res Princ Implic Año: 2024 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido