Pupillary Response to Auditory Stimuli in Depressive State
Bibliographic record
Abstract
Background: Previous reports indicate that depressed individuals have worse memory for negative information than other types of information. They tend to ruminate upon negative information with high sensitivity. Depressive subjects also tend to display greater sustained pupil dilation in response to negative information. Methods: We evaluated pupil diameter with charge coupled device (CCD) infrared camera while scoring depressive scale in 41 healthy elderly subjects. Six kinds of sounds were used as auditory stimuli with characters of startle-eliciting. The measurements of pupil were made in a rested state and done twice with pre- and post-auditory and light stimulations. The indexes of pupil responses were dilations for auditory stimulations and contractions for light stimulations. Results: The pupil diameter stimulated by emotional sound “glasses broken on the floor” was solely positive correlated with depressive score. The pupil contractions for light stimuli were weakly negative correlated with depressive score. Conclusion: Some emotional sounds with negative information could cause pupil dilation in depressive participants. This sound “glasses broken on the floor” would be a biological marker assistant to depressive diagnosis. J Neurol Res. 2016;6(1):8-11 doi: http://dx.doi.org/10.14740/jnr364w
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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) | 0.003 | 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".