The Nonverbal Processing of Actions Is an Area of Relative Strength in the Semantic Variant of Primary Progressive Aphasia
Bibliographic record
Abstract
Purpose Better performance for actions compared to objects has been reported in the semantic variant of primary progressive aphasia (svPPA). This study investigated the influence of the assessment task (naming, semantic picture matching) over the dissociation between objects and actions. Method Ten individuals with svPPA and 17 matched controls completed object and action naming tests, and object and action semantic picture matching tests. Performance was compared between the svPPA and control groups, within the svPPA group, and for each participant with svPPA versus the control group individually. Results Compared to controls, participants with svPPA were impaired on object and action naming, and object and action semantic picture matching. As a group, participants with svPPA had an advantage for actions over objects and for semantic picture matching tests over naming tests. Eight participants had a better performance for actions compared to objects in naming, with three showing a significant difference. Nine participants had a better performance for actions compared to objects in semantic picture matching, with six showing a significant difference. For objects, semantic picture matching was better than naming in nine participants, with five showing a significant difference. For actions, semantic picture matching was better than naming in all 10 participants, with nine showing a significant difference. Conclusion The nonverbal processing of actions, as assessed with a semantic picture matching test, is an area of relative strength in svPPA. Clinical implications for assessment planning and interpretation and theoretical implications for current models of semantic cognition are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".