Translating with an Injured Brain: Neurolinguistic Aspects of Translation as Revealed by Bilinguals with Cerebral Lesions
Bibliographic record
Abstract
Despite significant progress in the psycholinguistic study of translation, research on its neurological underpinnings has been limited and sparse. Translation scholars have recently taken an interest in relevant neuroscientific evidence, focusing on imaging studies. This paper addresses the issue by considering an equally important body of data: clinical evidence. Specifically, a hypothesis-driven analysis is offered of 21 cases of brain-lesioned bilinguals exhibiting translation disorders. Three neurofunctional and three neuroanatomical hypotheses are derived from the Revised Hierarchical Model and the Declarative/Procedural Model, respectively. Consistent with relevant predictions, the evidence suggests that there are neurofunctionally independent routes for translation, as opposed to monolingual speech production; backward, as opposed to forward, translation; and form-based, as opposed to conceptually mediated, translation. Available data further indicates that word and sentence translation are critically subserved by posterior brain areas implicated in declarative memory, and by frontobasal areas implicated in procedural memory, respectively. In addition, translation routes appear to be entirely left-lateralized.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".