Associations between major depressive disorder and performance-based and self-reported music cognition
Bibliographic record
Abstract
Background: There is evidence that major depressive disorder (MDD) comes with multiple cognitive impairments including deficits in perception and memory. Music cognition is one of the least explored cognitive functions in relation to MDD, with some studies pointing to mild amusic deficits. These findings, however, are derived only from performance-based tests. Our objectives were to explore differences in music perception and memory between individuals with MDD and a control group, in both performance test and self-report of amusic dysfunction, and to assess the correlation between these measures. Method: = 18). All the participants were evaluated with the Montreal Battery for Evaluation of Amusia (MBEA) and the Amusic Dysfunction Inventory (ADI). Results: None of the assessed dimensions from the MBEA or the ADI showed statistical differences between groups. Some significant associations were found between ADI's Vocal Production and the MBEA's three tests of the melodic dimension (Scale, Contour and Interval) and between MBEA's Scale and Memory, Meter and ADI's Melodic Perception, and tests of Memory from each respective instrument. Conclusion: Results suggest that perception and memory of basic music stimuli are not among the cognitive deficits within MDD, however, they may be indirectly affected by other cognitive phenomena common to this psychopathology, such as poor sustained concentration due to mental fatigue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".