Dopamine and light: effects on facial emotion recognition
Bibliographic record
Abstract
Bright light can affect mood states and social behaviours. Here, we tested potential interacting effects of light and dopamine on facial emotion recognition. Participants were 32 women with subsyndromal seasonal affective disorder tested in either a bright (3000 lux) or dim light (10 lux) environment. Each participant completed two test days, one following the ingestion of a phenylalanine/tyrosine-deficient mixture and one with a nutritionally balanced control mixture, both administered double blind in a randomised order. Approximately four hours post-ingestion participants completed a self-report measure of mood followed by a facial emotion recognition task. All testing took place between November and March when seasonal symptoms would be present. Following acute phenylalanine/tyrosine depletion (APTD), compared to the nutritionally balanced control mixture, participants in the dim light condition were more accurate at recognising sad faces, less likely to misclassify them, and faster at responding to them, effects that were independent of changes in mood. Effects of APTD on responses to sad faces in the bright light group were less consistent. There were no APTD effects on responses to other emotions, with one exception: a significant light × mixture interaction was seen for the reaction time to fear, but the pattern of effect was not predicted a priori or seen on other measures. Together, the results suggest that the processing of sad emotional stimuli might be greater when dopamine transmission is low. Bright light exposure, used for the treatment of both seasonal and non-seasonal mood disorders, might produce some of its benefits by preventing this effect.
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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 | 0.000 |
| 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) | 0.001 | 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".