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Identifying inefficient strategies in automation-aided signal detection
Lana Tikhomirov1, Megan L Bartlett2, Jackson Duncan-Reid3
1Australian Institute for Machine Learning, University of Adelaide.
Journal of Experimental Psychology. Applied
|July 20, 2023
Summary
Human operators use automated diagnostic aids inefficiently. A mixture model best explains their strategies, showing they sometimes follow aids and sometimes defer, indicating suboptimal performance in signal detection tasks.
Area of Science:
- Human-Computer Interaction
- Cognitive Psychology
- Signal Detection Theory
Background:
- Automated diagnostic aids assist human operators in signal detection.
- Operators frequently underutilize decision aids, leading to suboptimal performance.
- Previous research suggests a sluggish contingent cutoff (CC) strategy in human-aid interaction.
Purpose of the Study:
- To test the sluggish contingent cutoff (CC) model of operator strategy.
- To examine automation-aided signal detection efficiency under varying task difficulty.
- To compare the CC model with discrete deference (DD) and mixture models.
Main Methods:
- Two experiments involving a numeric decision-making task.
- Participants judged signal or noise based on probabilistic readings.
- Task difficulty was manipulated by varying mean reading values.
Main Results:
- Data were analyzed using the CC model, DD model, and a mixture model.
- Model fits indicated that the mixture model best described operator strategies.
- Results suggest multiple inefficiencies in how operators use signal detection aids.
Conclusions:
- Operator strategies for using automated diagnostic aids are varied and often inefficient.
- A mixture of contingent cutoff and deference strategies explains observed performance.
- Further research is needed to optimize human-automation interaction in diagnostic tasks.
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