Quantum Translation: A New Heuristic for Cognitive Uncertainty in the AI Era
Keywords:
heuristic decision-making; LLM-assisted translation; quantum-like cognition; translator cognition; uncertaintyAbstract
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. Using a PRISMA-consistent systematic literature review, we screened records from Scopus, Crossref, and Google 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, screen capture) and findings were synthesized thematically. Across the corpus, uncertainty is acknowledged as central yet treated implicitly as ambiguity, difficulty, or risk. Product-focused evaluation routinely obscures process-level signals such as cognitive load, recursive drafting, and attentional control. QT addresses this gap by modeling (i) superposition as coexisting candidate renderings, (ii) collapse as context-triggered resolution constrained by skopos, register, and pragmatics, and (iii) entanglement as cross-level dependencies linking lexical, syntactic, and discourse decisions. The review also charts convergences 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.
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