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From Trust in Automation to Decision Neuroscience: Applying Cognitive Neuroscience Methods to Understand and Improve
Kim Drnec1, Amar R Marathe1, Jamie R Lukos2
1Human Research and Engineering Directorate, U.S. Army Research Laboratory Aberdeen, MD, USA.
Frontiers in Human Neuroscience
|July 23, 2016
Summary
Human automation interaction (HAI) systems fail due to inappropriate user behavior. Understanding the decision-making process and its psychophysiological correlates is key to mitigating these issues and improving system performance.
Area of Science:
- Human-Automation Interaction (HAI)
- Decision Neuroscience
- Trust in Automation (TiA)
Background:
- HAI systems often underperform because users interact with automation inappropriately.
- Trust in automation (TiA) influences user interaction, leading to overuse (high trust) or disuse (low trust).
- Current research identifies HAI behaviors but lacks a clear mapping between trust states and outcomes.
Purpose of the Study:
- To investigate the interaction decision preceding user behavior in HAI systems.
- To leverage decision neuroscience findings to improve human-automation collaboration.
- To identify a research path for mitigating inappropriate interaction behaviors.
Main Methods:
- Reviewed the Trust in Automation (TiA) literature to understand trust outcomes and knowledge gaps.
- Reviewed the decision-making literature from a decision neuroscience perspective.
- Examined the psychophysiological correlates of decision-making processes.
Main Results:
- Inappropriate interaction behaviors occur after the user's decision.
- Decisions are stereotyped processes with measurable psychophysiological correlates.
- Understanding the decision process is critical for mitigating inappropriate interaction behavior.
Conclusions:
- Mitigating inappropriate HAI behavior requires understanding the preceding interaction decision.
- Leveraging decision neuroscience can enhance joint system performance.
- Future research should focus on the psychophysiological correlates of decisions to predict and mitigate interaction behaviors.
Keywords:
decision makinghuman automation interactioninteraction decisionsneuroergonomicstrust in automationMore Related Videos
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