Year: 2025
Type: preprint
Abstract: As people nowadays increasingly rely on artificial intelligence (AI) to curate information and make decisions, assigning the appropriate amount of trust in automated intelligent systems has become ever more important. However, current measurements of trust in automation still largely rely on self-reports that are subjective and disruptive to the user. Here, we take music recommendation as a model to investigate the neural and cognitive processes underlying trust in automation. We observed that system accuracy was directly related to users' trust and modulated the influence of recommendation cues on music preference. Modelling users' reward encoding process with a reinforcement learning model further revealed that system accuracy, expected reward, and prediction error were related to oscillatory neural activity recorded via EEG and changes in pupil diameter. Our results provide a neurally grounded account of calibrating trust in automation and highlight the promises of a multimodal approach towards developing trustable AI systems.
Source: arXiv (Cornell University)
Language: English
Subfield: Human Factors and Ergonomics
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
Award: G3775593313
OpenAlex ID: W4415917298