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Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation

Trust calibration for a human–machine team is the process by which a human adjusts their expectations of the automation’s reliability and trustworthiness; adaptive support for trust calibration is needed to engender appropriate reliance on automation. Herein, we leverage an instance-based learning A...

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Autores principales: Lebiere, Christian, Blaha, Leslie M., Fallon, Corey K., Jefferson, Brett
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8181412/
https://www.ncbi.nlm.nih.gov/pubmed/34109222
http://dx.doi.org/10.3389/frobt.2021.652776
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author Lebiere, Christian
Blaha, Leslie M.
Fallon, Corey K.
Jefferson, Brett
author_facet Lebiere, Christian
Blaha, Leslie M.
Fallon, Corey K.
Jefferson, Brett
author_sort Lebiere, Christian
collection PubMed
description Trust calibration for a human–machine team is the process by which a human adjusts their expectations of the automation’s reliability and trustworthiness; adaptive support for trust calibration is needed to engender appropriate reliance on automation. Herein, we leverage an instance-based learning ACT-R cognitive model of decisions to obtain and rely on an automated assistant for visual search in a UAV interface. This cognitive model matches well with the human predictive power statistics measuring reliance decisions; we obtain from the model an internal estimate of automation reliability that mirrors human subjective ratings. The model is able to predict the effect of various potential disruptions, such as environmental changes or particular classes of adversarial intrusions on human trust in automation. Finally, we consider the use of model predictions to improve automation transparency that account for human cognitive biases in order to optimize the bidirectional interaction between human and machine through supporting trust calibration. The implications of our findings for the design of reliable and trustworthy automation are discussed.
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spelling pubmed-81814122021-06-08 Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation Lebiere, Christian Blaha, Leslie M. Fallon, Corey K. Jefferson, Brett Front Robot AI Robotics and AI Trust calibration for a human–machine team is the process by which a human adjusts their expectations of the automation’s reliability and trustworthiness; adaptive support for trust calibration is needed to engender appropriate reliance on automation. Herein, we leverage an instance-based learning ACT-R cognitive model of decisions to obtain and rely on an automated assistant for visual search in a UAV interface. This cognitive model matches well with the human predictive power statistics measuring reliance decisions; we obtain from the model an internal estimate of automation reliability that mirrors human subjective ratings. The model is able to predict the effect of various potential disruptions, such as environmental changes or particular classes of adversarial intrusions on human trust in automation. Finally, we consider the use of model predictions to improve automation transparency that account for human cognitive biases in order to optimize the bidirectional interaction between human and machine through supporting trust calibration. The implications of our findings for the design of reliable and trustworthy automation are discussed. Frontiers Media S.A. 2021-05-24 /pmc/articles/PMC8181412/ /pubmed/34109222 http://dx.doi.org/10.3389/frobt.2021.652776 Text en Copyright © 2021 Lebiere, Blaha, Fallon and Jefferson. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Lebiere, Christian
Blaha, Leslie M.
Fallon, Corey K.
Jefferson, Brett
Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation
title Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation
title_full Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation
title_fullStr Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation
title_full_unstemmed Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation
title_short Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation
title_sort adaptive cognitive mechanisms to maintain calibrated trust and reliance in automation
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8181412/
https://www.ncbi.nlm.nih.gov/pubmed/34109222
http://dx.doi.org/10.3389/frobt.2021.652776
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