Trust, control strategies and allocation of function in human-machine systems

Area of Science:

• Human-computer interaction
• Automation and control systems
• Cognitive systems engineering

Background:

• Human operators are increasingly shifting to supervisory roles in complex automated systems.
• The choice between manual and automatic control impacts system performance.
• Operator trust in automation influences control strategy decisions.

Purpose of the Study:

• To characterize how operators' trust changes during interaction with a semi-automatic system.
• To investigate the relationship between evolving trust and control strategies.
• To develop a model for manual vs. automatic control selection.

Main Methods:

• Experimental study with a semi-automatic pasteurization plant.
• Regression modeling to identify trust change drivers.
• Time series analysis to develop a 'trust transfer function'.

Main Results:

• Identified key factors influencing operator trust dynamics.
• Developed a quantitative model for trust changes over time.
• Observed distinct operator strategies in response to system faults.

Conclusions:

• Operator trust in automation is dynamic and influences control decisions.
• A model integrating trust and self-confidence can predict manual/automatic control choices.
• Understanding trust dynamics is crucial for effective human-automation collaboration.