Human or machine? Do source beliefs shape cognitive bias in post-editing?

As artificial intelligence (AI) translation becomes increasingly integrated into professional and educational contexts, perceptions of "machine vs. human" translation may shape translators' cognitive and evaluative and post-editing behaviors. This study investigates the role of cognitive bias in post-editing, examining how translators' beliefs about translation sources-human or AI-trigger systematic deviations in evaluation and revision behavior. Sixty master students in Translation and Interpreting participated in a between-subjects experiment: the experimental group received mislabeled texts (human translations labeled as AI, AI translations labeled as human), while the control group was informed of the true sources. Participants evaluated two English-to-Chinese translations on fidelity, fluency, and completeness (5-point Likert scales) and performed post-editing, with modifications recorded for type and rationale. Quantitative analyses (revision counts, quality ratings) and qualitative reflections were examined to explore how cognitive preconceptions modulate linguistic judgment and editing behavior. Findings indicate that perceived AI labels elicited lower trust, increased post-editing intensity, heightened error sensitivity, and more conservative quality ratings, even when translation quality was equivalent. This reveals a subtle form of language-related cognitive bias, in which perceived source identity, rather than objective quality, drives evaluation and modification decisions. By bridging translation studies and cognitive psychology, the study provides empirical insight into human-AI interaction and offers pedagogical implications for fostering critical post-editing literacy and inclusive attitudes toward AI-mediated communication.