Cultural landscapes in World Heritage cities are attracting a growing global tourist population. Given the limitations of self-report methods in capturing tourists’ immediate and deep perceptions, and the lack of comprehensive investigation into the cultural types and naturalness of landscapes, this study aims to investigate how cultural landscape types influence tourists’ recommendation intention through the mediating roles of place attachment and perceived restorativeness while examining the moderating effect of landscape naturalness. Integrating Place Attachment Theory (PAT), Attention Restoration Theory (ART), and the Associative–Propositional Evaluation Model (APE), three studies were conducted using behavioral and neurophysiological approaches. Study 1, a scenario-based experiment, revealed that high-culture landscapes enhance recommendation intention via place attachment, with the effect of perceived restorativeness being stronger under low naturalness conditions. Study 2, an event-related potential (ERP) experiment, showed that landscapes with low culture and low naturalness elicit stronger emotional responses, as indicated by heightened P2 and LPP amplitudes. Study 3 demonstrated the efficacy of a Decision Tree model in classifying landscape naturalness based on EEG features. This study deepens the understanding of the complexity of tourist experiences in cultural heritage sites, provides new evidence for the application of Place Attachment Theory in tourism contexts, and offers scientific foundations and practical implications for optimizing landscape design in heritage sites, enhancing tourist experiences, and exploring brain–computer interface applications in the tourism field.

Artificial intelligence (AI), especially generative AI, is becoming deeply embedded in healthcare, making doctor–AI collaborative diagnosis a common mode of medical decision-making. This development raises a critical question about responsibility attribution: When people perceive that a diagnostic error has occurred, how does AI involvement shape patients’ and observers’ judgments of the doctor’s responsibility? Drawing on responsibility attribution theory, we examine this question across five studies—one event-related potential (ERP) experiment and four scenario experiments. We find that when a diagnostic error is perceived, doctor–AI collaborative diagnosis (vs. doctor-only diagnosis) reduces perceived doctor responsibility by increasing perceived shared agency. This responsibility-reducing effect is weaker when the doctor rejects correct AI advice than when the doctor accepts incorrect AI advice. Theoretically, our findings show that responsibility attribution in human–AI collaboration involves two stages: agent identification and responsibility allocation. This account extends responsibility attribution theory to human–AI collaboration and identifies perceived shared agency as a key psychological mechanism underlying responsibility judgments in these settings. Practically, the findings can inform technology deployment, responsibility communication, and governance mechanisms in hospitals, AI firms, and regulatory agencies.