<![CDATA[This study reframes uncertainty in translator cognition by proposing a Quantum Translation (QT)heuristic superposition, collapse, and entanglement as a probabilistic lexicon for process analysis. Usinga PRISMA-consistent systematic literature review, we screened records from Scopus, Crossref, andGoogle Scholar (2020–2025) via database queries and citation chasing, yielding 22 empirical studies.Data extraction targeted instruments used in primary studies (e.g., eye tracking, key logging, screencapture) and findings were synthesized thematically. Across the corpus, uncertainty is acknowledged ascentral yet treated implicitly as ambiguity, difficulty, or risk. Product-focused evaluation routinelyobscures process-level signals such as cognitive load, recursive drafting, and attentional control. QTaddresses this gap by modeling (i) superposition as coexisting candidate renderings, (ii) collapse ascontext-triggered resolution constrained by skopos, register, and pragmatics, and (iii) entanglement ascross-level dependencies linking lexical, syntactic, and discourse decisions. The review also chartsconvergences between human process traces and computational predictors (e.g., surprisal), informing risk-aware human AI workflows. We contribute a testable heuristic and implications: integrate QT-informed diagnostics in translator education; report AI use transparently; and adopt evaluation models that combine process and product. Together, these steps strengthen accountability and professional preparedness for human AI collaboration.]]>
