Trust between users and autonomous agents is dynamic, yet most measurement instruments capture only a static end-state. Think-aloud protocol offers a window into the reasoning behind trust judgements, but no prior work has examined how retrospective think-aloud (RTA) data relates to performance trust. This paper presents a user study in which 30 participants interacted with a scripted AI agent in a collaborative box-moving game, verbalised their thoughts while watching a recording of their own gameplay (RTA), and then completed the Multidimensional Measure of Trust (MDMT). A deductive content analysis grounded in the MDMT Reliable and Capable subscales produced a 16-code scheme applied to all 30 transcripts. Negative trust language in RTA tracked the game's scripted trust trajectory, peaking during the most severe failure phase, but did not relate to participants' final MDMT scores - suggesting RTA's diagnostic value lies in the timing of this language across the session, rather than its overall volume. These findings support RTA as a method for capturing within-session trust dynamics that static questionnaires cannot surface. ... Read more

Trust is a key factor in human-agent interaction, especially when users collaborate with an AI agent whose behavior is not fully predictable. Trust is often measured after an interaction using questionnaires, but these measures do not fully show how trust changes during the interaction or which agent behaviors shape the user's judgment. To address this limitation, this paper investigates whether Concurrent Think-Aloud (CTA) data can capture participants' real-time reasoning about the agent's moral trustworthiness during interaction. The study uses a modified version of the Moving Out game, in which participants collaborate with an AI agent while verbalizing their thoughts. Trust is examined using repeated in-game pop-up questionnaires, a post-interaction questionnaire, coded CTA transcripts, game logs, and screen recordings. The results show that morality-related trust is rarely expressed through formal moral terms such as "ethical'' or "principled''. Instead, participants express it through task-specific comments about whether the agent cooperates with them, ignores them, or acts in their interest. The findings also show that CTA markers align more closely with in-game trust ratings than with post-interaction questionnaire scores. Overall, the results suggest that CTA can complement questionnaire-based trust measures by capturing situated, real-time expressions of morality trust during interaction. ... Read more

Trust in an AI teammate is usually measured with a single post-hoc questionnaire, which records a settled judgment but reveals little about when trust shifts or why. Think-aloud protocols are a promising way to recover what questionnaires leave out, because they expose the reasoning behind a judgment rather than only its endpoint, but little work has examined how they reflect trust, and almost none how they reflect its separate dimensions. This paper asks how retrospective think-aloud (RTA) data reflects a user's morality trust in an agent. Using 30 RTA transcripts from a cooperative game with a scripted faulty agent, the study develops a coding scheme for morality-trust markers, links them to the in-game events that elicit them, and compares the result against the morality subscale of the Multi-Dimensional Measure of Trust (MDMT). Morality trust surfaces in RTA narrowly and reactively: mainly as attributions of the agent's intent, strongly skewed negative and ordered in time. Convergence with the MDMT is real but narrow, while the humanising attributions RTA captures have no questionnaire counterpart. The two thus emerge as complementary rather than interchangeable: the questionnaire records a settled judgment, RTA recovers when and why it moved. ... Read more