Caffeine alters emotion and emotional responses in low habitual caffeine consumers
Bibliographic record
Abstract
Caffeine reliably increases emotional arousal, but it is unclear whether and how it influences other dimensions of emotion such as emotional valence. These experiments documented whether caffeine influences emotion and emotion regulation choice and success. Low to abstinent caffeine consumers (maximum 100 mg/day) completed measures of state anxiety, positive and negative emotion, and salivary cortisol before, 45 min after, and 75 min after consuming 400 mg caffeine or placebo. Participants also completed an emotion regulation choice task, in which they chose to employ cognitive reappraisal or distraction in response to high and low intensity negative pictures (Experiment 1), or a cognitive reappraisal task, in which they employed cognitive reappraisal or no emotion regulation strategy in response to negative and neutral pictures (Experiment 2). State anxiety, negative emotion, and salivary cortisol were heightened both 45 and 75 min after caffeine intake relative to placebo. In Experiment 1, caffeine did not influence the frequency with which participants chose reappraisal or distraction, but reduced negativity of the picture ratings. In Experiment 2, caffeine did not influence cognitive reappraisal success. Thus, caffeine mitigated emotional responses to negative situations, but not how participants chose to regulate such responses or the success with which they did so.
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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".