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Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation
Christian Lebiere1, Leslie M Blaha2, Corey K Fallon3
1Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, United States.
Frontiers in Robotics and AI
|June 10, 2021
Summary
This study developed a cognitive model to understand human trust in automation for Unmanned Aerial Vehicle (UAV) visual search. The model accurately predicts human reliance and the impact of disruptions on trust calibration.
Area of Science:
- Human-Computer Interaction
- Cognitive Science
- Artificial Intelligence
Background:
- Trust calibration is crucial for effective human-machine teaming, requiring adaptive support for appropriate reliance on automation.
- Understanding how humans adjust their expectations of automation's reliability is key to designing trustworthy systems.
Purpose of the Study:
- To develop and validate a cognitive model for trust calibration in human-automation interaction.
- To predict human reliance on automated visual search in Unmanned Aerial Vehicle (UAV) interfaces.
- To explore how model predictions can enhance automation transparency and address human cognitive biases.
Main Methods:
- Leveraged an instance-based learning ACT-R cognitive model of decisions.
- Utilized the model to simulate and analyze reliance on an automated visual search assistant.
- Compared model-generated internal estimates of automation reliability with human subjective ratings.
Main Results:
- The cognitive model accurately mirrored human predictive power statistics for reliance decisions.
- The model's internal reliability estimate aligned with human subjective trust ratings.
- The model successfully predicted the impact of environmental changes and adversarial intrusions on trust.
Conclusions:
- The developed cognitive model provides a valuable tool for understanding and predicting trust calibration in human-automation teams.
- Model predictions can inform the design of more transparent and trustworthy automation systems.
- Optimizing the human-machine interaction through supported trust calibration is essential for reliable automation.
Keywords:
ACT-Rautomation transparencycognitive architectureshuman–machine teamingtrust calibrationtrust in automationMore Related Videos
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