Processing of metaphors in transcortical motor aphasia
Bibliographic record
Abstract
Great emphasis has been placed on the right hemisphere, due to its possible selective contribution, in the processing of metaphorical statements. OBJECTIVES: To describe the processing of metaphors in the case of a patient with transcortical motor aphasia, using specific tests for patients with encephalic injuries of the right hemisphere, and to contribute to the discussion on the inter-hemispheric relationships associated with this function. METHODS: A 54 year-old man with transcortical motor aphasia was evaluated three years after a left hemisphere stroke. The tasks of comprehension of metaphors were based on the subtest Metaphor Comprehension Task of the Montreal Evaluation of Communications Scale (MEC). Two metaphor comprehension tests were applied, in 45-minute sessions with a 48 hour interval between each. Test 1 involved comprehension of the metaphors according to the options offered, and Test 2 the comprehension of metaphors measured by response time and visual field. RESULTS: Although the right hemisphere was not affected by the stroke in this case, difficulties were observed in the processing of metaphors. CONCLUSIONS: This study suggests that the left hemisphere participates in the processing of figurative meanings. The adaptability of the brain can also re-accommodate the uninjured areas of the brain, causing the dynamic of the brain to be modified. As a result, deducing cerebral functions based on clinical data can be problematic. The value of this study is that it can contribute to clinical aspects of language rehabilitation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".