The Importance of Being Familiar: The Role of Semantic Knowledge in the Activation of Emotions and Factual Knowledge from Music in the Semantic Variant of Primary Progressive Aphasia
Bibliographic record
Abstract
BACKGROUND: The role of semantic knowledge in emotion recognition remains poorly understood. The semantic variant of primary progressive aphasia (svPPA) is a degenerative disorder characterized by progressive loss of semantic knowledge, while other cognitive abilities remain spared, at least in the early stages of the disease. The syndrome is therefore a reliable clinical model of semantic impairment allowing for testing the propositions made in theoretical models of emotion recognition. OBJECTIVE: The main goal of this study was to investigate the role of semantic memory in the recognition of basic emotions conveyed by music in individuals with svPPA. METHODS: The performance of 9 individuals with svPPA was compared to that of 32 control participants in tasks designed to investigate the ability: a) to differentiate between familiar and non-familiar musical excerpts, b) to associate semantic concepts to musical excerpts, and c) to recognize basic emotions conveyed by music. RESULTS: Results revealed that individuals with svPPA showed preserved abilities to recognize familiar musical excerpts but impaired performance on the two other tasks. Moreover, recognition of basic emotions and association of musical excerpts with semantic concepts was significantly better for familiar than non-familiar musical excerpts in participants with svPPA. CONCLUSION: Results of this study have important implications for theoretical models of emotion recognition and music processing. They suggest that impairment of semantic memory in svPPA affects both the activation of emotions and factual knowledge from music and that this impairment is modulated by familiarity with musical tunes.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".