Processing sentence negation in spanish-speaking people with aphasia
Bibliographic record
Abstract
Background People with aphasia (PWA) experience difficulty producing and comprehending sentences, but relatively little is known about their processing of sentences with negation, a universal phenomenon in human language. Previous studies that have investigated the processing of sentence negation in aphasia have yielded mixed results. In some investigations, people with nonfluent aphasia did not have greater difficulty generating sentences with negation as compared to affirmative sentences. In contrast, other studies found lower accuracy in processing sentences with negation compared to sentences without negation.Aim The aim of this study was to advance our understanding of negation processing in PWA. The study asked whether Spanish-speaking people with nonfluent aphasia have difficulty comprehending and producing sentences with negation markers and whether that difficulty varies across sentence structures and tasks.Methods Ten native speakers of Spanish participated in the study: five people with nonfluent aphasia and a control group of five neurologically healthy people. A negation battery was designed that included four tasks: Sentence-Picture Matching Task, Anagram Task, Repetition Task, and Say-the-Opposite Task. The stimuli in the task conditions included sentences with different argument structures (subject-verb vs. subject-verb-object), tense (sentences with verbs in present simple vs. present progressive tense), and negative concord (never/always; something/anything).Results Analyses of performance accuracy demonstrated that, across all tasks, neurologically healthy people performed at ceiling. For the PWA, response accuracy varied across tasks, with higher performance on the Sentence-Picture Matching and the Anagram tasks and lower performance on the Repetition and Say-the-Opposite tasks. Further analysis of the Say-the-Opposite task demonstrated an interaction between argument structure and negation as well as an effect of negative concord. No reliable effect of tense was observed. Furthermore, the errors committed by the participants demonstrated difficulty with morphosyntactic aspects of the negation markers.Conclusions The results support the view that people with nonfluent aphasia have difficulty with negation but that difficulty varies across tasks and is modulated by morphosyntactic properties of the negation markers. The present study highlights the advantages of employing task batteries with varying sentence structures rather than single tasks in order to further our understanding of the processing of sentences with negation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".