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How Do People Process Information from Automated Decision Aids: an Application of Systems Factorial Technology.
Cara M Kneeland1,2, Joseph W Houpt3, Ion Juvina1
1Wright State University, Dayton, OH USA.
Computational Brain & Behavior
|July 16, 2026
Summary
Automated recommender systems influence user decision-making by affecting information processing. Findings suggest design improvements for better human-automation interaction and decision performance.
Area of Science:
- Human-Computer Interaction
- Cognitive Psychology
- Decision Science
Background:
- Limited research exists on how automated recommender systems impact user decision-making processes.
- Understanding information processing is crucial for designing effective human-automation collaboration.
Purpose of the Study:
- To measure how individuals process information when using automated recommender systems.
- To investigate factors influencing information utilization and processing strategies.
Main Methods:
- Employed the Systems Factorial Technology (SFT) framework across two experiments.
- Participants performed a speeded length judgment task with a reliable but imperfect automated aid.
- Varied conditions to examine information use (single vs. multiple sources) and processing (serial vs. parallel).
Main Results:
- Participants predominantly exhibited serial information processing and utilized a single information source.
- Integrating display information and providing accurate training enhanced information processing efficiency.
- Display characteristics, incentives, and training influenced information processing speed and strategy.
Conclusions:
- Human decision-making with automation involves serial processing and selective information use.
- System design elements like display integration and training are key to optimizing information processing.
- This research provides insights for designing automated systems that improve user decision performance.
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