| Autor: |
Hopko, Sarah K., Zhang, Yinsu, Yadav, Aakash, Pagilla, Prabhakar R., Mehta, Ranjana K. |
| Předmět: |
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| Zdroj: |
ACM Transactions on Human-Robot Interaction; Mar2024, Vol. 13 Issue 1, p1-23, 23p |
| Abstrakt: |
Trust in human–robot collaboration is an essential consideration that relates to operator performance, utilization, and experience. While trust's importance is understood, the state-of-the-art methods to study trust in automation, like surveys, drastically limit the types of insights that can be made. Improvements in measuring techniques can provide a granular understanding of influencers like robot reliability and their subsequent impact on human behavior and experience. This investigation quantifies the brain–behavior relationships associated with trust manipulation in shared space human–robot collaboration to advance the scope of metrics to study trust. Thirty-eight participants, balanced by sex, were recruited to perform an assembly task with a collaborative robot under reliable and unreliable robot conditions. Brain imaging, psychological and behavioral eye-tracking, quantitative and qualitative performance, and subjective experiences were monitored. Results from this investigation identify specific information processing and cognitive strategies that result in identified trust-related behaviors that were found to be sex specific. The use of covert measurements of trust can reveal insights that humans cannot consciously report, thus shedding light on processes systematically overlooked by subjective measures. Our findings connect a trust influencer (robot reliability) to upstream cognition and downstream human behavior and are enabled by the utilization of granular metrics. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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