Predictive language comprehension in Parkinson’s disease
Bibliographic record
Abstract
Language impairment in Parkinson’s disease (PD) may be attributable to motor and action/event knowledge deficits. We predicted that cognitively intact PD participants would be impaired in anticipating objects in sentences from event-based thematic fit information. Twenty-four PD and 24 healthy age-matched participants completed comprehensive neuropsychological assessments. We recorded participants’ eye movements as they heard predictive (The fisherman rocks the boat) and non-predictive baseline sentences (Look at the bathtub). Predictive sentences contained target, agent-related, verb-related, and unrelated images. Baseline sentences used phonologically and semantically unrelated distractors. We tested effects of group (PD/control) on gaze using growth curve models. There were no significant differences between PD and control participants in either sentence type, suggesting that PD participants successfully and rapidly use combinatory thematic fit information to predict upcoming language. Additionally, we conducted an exploratory analysis contrasting PD and controls’ performance on low motion content versus high motion content verbs. This analysis revealed fewer predictive fixations in high-motion sentences only for healthy older adults, suggesting that people with Parkinson’s disease may adapt to their disease by relying on spared, non-action-simulation-based language prediction and processing mechanisms. Given that multiple studies have shown that individuals with PD have difficulty processing verbs, it is highly surprising that they match healthy adults in their ability to use verb meaning to predict upcoming nouns.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".