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Record W2296383557 · doi:10.14740/jnr.v6i1.364

Pupillary Response to Auditory Stimuli in Depressive State

2016· article· en· W2296383557 on OpenAlexvenueno aff
Hiroaki Oguro, Nobuo Suyama, Kenji Karino, Shuhei Yamaguchi

Bibliographic record

VenueJournal of Neurology Research · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPupillary responsePupilAudiologyMedicinePsychologyNeuroscience

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.335
GPT teacher head0.504
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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