A Dual-Channel Feedback Framework for Anthropomorphic Uncertainty Communication in Behavior Change Systems

Behavior change technologies are increasingly deployed in everyday contexts where perception errors are difficult to avoid. Such errors can undermine user trust and long-term engagement, while purely technical approaches to error elimination are often impractical in open-world environments. This study proposes a fault-tolerant design that translates algorithmic uncertainty into anthropomorphic expressions of vulnerability. By decoupling task-outcome feedback from internal confidence states, an embodied agent communicates uncertainty through a five-level nonverbal framework comprising posture, facial expression, and motion intensity. The approach was implemented in an interactive waste-sorting system and examined through a three-week field study in a semi-public university corridor. Three feedback strategies were compared: an outcome-only baseline, a persistently confident agent, and an adaptive agent whose vulnerability expression varied according to a transformed confidence signal. The findings suggest differences in user behavior across conditions. Under the adaptive condition, user sorting accuracy exhibited a fluctuation–recovery pattern during the final deployment phase, whereas accuracy under the confident-agent condition showed a declining trend. Correct-trial stay durations were shorter under the adaptive condition, consistent with the formation of a more streamlined interaction routine. In contrast, observations from error cases were limited by the small number of misclassification events. Due to the exploratory nature of the study, the small sample size of the questionnaire and the sequential deployment structure, the results should be considered preliminary evidence. Nevertheless, the findings suggest that expressing vulnerability in an anthropomorphic way may be a promising approach for communicating uncertainty in behavior change systems.