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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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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. |
format | Online Article Text |
id | pubmed-8181412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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