Evaluating Effective Connectivity of Trust in Human-Automation Interaction: A Dynamic Causal Modeling (DCM) Study

Area of Science:

• Neuroscience
• Cognitive Science
• Human-Computer Interaction

Background:

• Trust in automation is crucial for effective human-automation interaction.
• Previous research implicated central executive network (CEN) and default mode network (DMN) in trust judgments.
• Neural correlates of trust, specifically directed information flow, remain underexplored.

Purpose of the Study:

• To investigate how credibility and reliability influence effective connectivity (EC) between brain regions during trust in automation.
• To explore the neural dynamics of trust and distrust using dynamic causal modeling (DCM).

Main Methods:

• Utilized dynamic causal modeling (DCM) to analyze brain activity in 16 participants.
• Examined effective connectivity (EC) within the central executive network (CEN) and default mode network (DMN).
• Employed Bayesian model averaging (BMA) to quantify connectivity strengths across 30 distinct connection models.

Main Results:

• Low trust conditions exhibited unique connections, stronger prefrontal cortex influence, and increased network complexity compared to high trust.
• High trust conditions were characterized by a lack of backward connections.
• Dynamic causal modeling revealed distinct neural patterns for trust and distrust.

Conclusions:

• Trust and distrust represent separate neural processes in human-automation interaction.
• Distrust may involve a more complex neural network, potentially due to elevated cognitive load.
• Understanding causal brain network architecture aids in designing balanced human-automation interfaces and optimizing automation use.