Understanding Explanation Content for Cognitive and Affective Trust in Automated Vehicles
DOI: 10.1177/10711813251366284
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Summary
This study investigates how different types of explanations provided by automated vehicles (AVs) influence user trust, specifically distinguishing between cognitive trust (based on perceptions of reliability and competence) and affective trust (rooted in emotional connection). Motivated by the critical role of trust in AV adoption and the lack of research addressing how explanation content impacts these distinct trust dimensions, the authors utilize Agency Theory to frame the human-AV relationship. They hypothesize that "what-only" explanations (describing actions) enhance cognitive trust by reducing information asymmetry, while "why-only" explanations (providing reasoning) improve both cognitive and affective trust by addressing goal misalignment. The researchers conducted a between-subjects experiment with 121 U.S. drivers recruited via CloudResearch. Participants viewed six simulated driving scenarios across urban, highway, and rural environments, presented through videos from a passenger’s perspective. The study manipulated the explanation content into four conditions: no explanation, what-only, why-only, and what + why. Explanations were delivered via an American-accented female voice five seconds before each event. Cognitive and affective trust were measured using adapted Likert-scale items. Statistical analysis employed a linear mixed-effects model with one-way ANOVA to test the effects of explanation type on trust scores, with Bonferroni corrections for post-hoc comparisons. The results demonstrated that explanation content significantly impacts both trust dimensions. Cognitive trust was significantly higher in all three explanation conditions compared to the no-explanation control, indicating that any form of explanation improves users' rational assessment of the AV’s competence. However, affective trust increased only when explanations included reasoning. Specifically, participants in the why-only and what + why conditions reported significantly higher affective trust than those in the what-only or no-explanation conditions. The what-only condition did not differ significantly from the control group in terms of affective trust. These findings support the hypothesis that while describing actions aids cognitive understanding, providing reasons is necessary to foster emotional connection and perceived alignment with user goals. The study concludes that AV explanations must be designed to address both informational clarity and emotional connection to build comprehensive trust. By differentiating between cognitive and affective trust, the research highlights that transparent communication of the AV’s rationale is crucial for reducing anxiety and fostering a sense of shared intentionality. These insights provide practical guidance for developers to create explanation systems that are not only functionally reliable but also socially responsive, thereby encouraging broader public acceptance. The authors suggest future research should explore multimodal explanations and longitudinal trust development across diverse user groups.
Key finding
Explanations that include reasoning for automated vehicle actions significantly enhance affective trust, whereas any explanation improves cognitive trust compared to no explanation.
Methodology
lab_experiment
Sample size: 121
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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