The effect of intranasal oxytocin on perceiving and understanding emotion on the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT).
Bibliographic record
Abstract
Evidence suggests that intranasal oxytocin enhances the perception of emotion in facial expressions during standard emotion identification tasks. However, it is not clear whether this effect is desirable in people who do not show deficits in emotion perception. That is, a heightened perception of emotion in faces could lead to "oversensitivity" to the emotions of others in nonclinical participants. The goal of this study was to assess the effects of intranasal oxytocin on emotion perception using ecologically valid social and nonsocial visual tasks. Eighty-two participants (42 women) self-administered a 24 IU dose of intranasal oxytocin or a placebo in a double-blind, randomized experiment and then completed the perceiving and understanding emotion components of the Mayer-Salovey-Caruso Emotional Intelligence Test. In this test, emotion identification accuracy is based on agreement with a normative sample. As expected, participants administered intranasal oxytocin rated emotion in facial stimuli as expressing greater emotional intensity than those given a placebo. Consequently, accurate identification of emotion in faces, based on agreement with a normative sample, was impaired in the oxytocin group relative to placebo. No such effect was observed for tests using nonsocial stimuli. The results are consistent with the hypothesis that intranasal oxytocin enhances the salience of social stimuli in the environment, but not nonsocial stimuli. The present findings support a growing literature showing that the effects of intranasal oxytocin on social cognition can be negative under certain circumstances, in this case promoting "oversensitivity" to emotion in faces in healthy people.
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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 | 0.004 |
| 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 | 0.001 |
| 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".