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Updated: Aug 15, 2026
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Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
Published on: December 5, 2025
How Cognitive Load Affects Dynamic Trust Calibration in Human-AI Collaboration: Evidence for Selective Pathway
Xiaojiao Chen1, Yonghan Liu1, Yiran Ma2
1College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Human Factors
|August 14, 2026
Summary
Cognitive load impacts trust in AI by altering how people update their beliefs. High cognitive load weakens analytical trust updating but increases reliance on outcomes, affecting AI collaboration.
Area of Science:
- Human-Computer Interaction
- Cognitive Psychology
- Artificial Intelligence
Background:
- Cognitive load is known to influence trust in automation.
- However, its specific effects on the mechanisms of trial-by-trial trust updating in AI-assisted decision-making are not well understood.
Purpose of the Study:
- To investigate whether cognitive load selectively affects different trust updating pathways in AI-assisted decision-making.
- To understand how cognitive constraints rebalance analytical and heuristic trust updating mechanisms.
Main Methods:
- A dual-task paradigm was employed within a mining exploration task to manipulate cognitive load.
- Dynamic trust calibration was captured, operationalizing process-based (analytical) and outcome-based (heuristic) trust updating pathways.
- Linear mixed-effects models analyzed trust dynamics and behavioral reliance.
Main Results:
- High cognitive load attenuated process-based trust updating, reducing sensitivity to AI recommendation correctness.
- Outcome-based trust updating gained influence under high load, amplifying outcome-driven bias.
- Cognitive load increased subjective trust and behavioral reliance on AI, with asymmetric effects depending on task outcomes.
Conclusions:
- Cognitive load shapes trust calibration by reconfiguring underlying mechanisms, not through global impairment.
- It weakens analytical evaluation while amplifying heuristic reliance on outcomes, advancing the understanding of dynamic trust in human-AI collaboration.
- Findings offer practical guidance for designing AI systems to support analytic trust and mitigate over-reliance under cognitive strain.
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