Disclosure willingness anthropomorphism and thinking style shape perceived fit and comfort in AI mental health chatbot interactions

Abstract Generative artificial intelligence (AI) conversational agents are increasingly used for everyday, non-clinical mental-health support, yet little is known about when these exchanges feel fitting and comfortable. We used a laboratory mixed-methods design to examine associations among perceived convenience of interaction (PCI), relational attitude (RA), willingness to self-disclose (WSD), anthropomorphism (AN), perceived fit/comfort (FC), and thinking style in 83 participants (43 rational-dominant; 40 experiential-dominant). WSD showed the strongest unique association with FC. A post hoc reverse model also found a unique FC-WSD association, leaving their temporal order unresolved. AN had no stable direct association with FC, but the WSD-FC association became stronger as AN increased. Group comparisons and continuous Rational-Experiential Inventory (REI) analyses converged: relatively stronger experiential processing was associated with higher AN, whereas relatively stronger rational processing was associated with higher WSD. The group-blind thematic analysis linked disclosure decisions to privacy, control, social distance, functional usefulness, and affective comfort. Together, the findings identify WSD as a key correlate of FC and show that anthropomorphism conditions this association rather than simply improving the interaction. Clear boundaries, controllability, and adjustable relational cues therefore warrant attention in future design research.