So difficult to smile: Why unhappy people avoid enjoyable activities.
Bibliographic record
Abstract
Engaging in an enjoyable activity is often effective in reducing or eliminating a negative mood. However, imagining this activity before deciding whether to perform it can decrease unhappy people's willingness to engage in it. The facial expression that accompanies a negative mood (and the muscles activated by the expression) can conflict with the expression that is elicited by imagining the performance of an enjoyable activity, evoking subjective feelings of difficulty of imagining it. Unhappy people misattribute these feelings of difficulty to the enjoyable activity itself, decreasing their desire to engage in the activity. The effects of metacognitive difficulty are eliminated when (a) unhappy people attribute difficulty to something other than the enjoyable activity and (b) focus their attention on the outcome of the activity rather than the process of engaging in it. Moreover, when an irrelevant factor activates smile-related features while performing the activity, the experience of difficulty is attenuated and its effect on aversion to the activity is not apparent. In contrast, unhappy people also find it easy to imagine an unenjoyable activity and consequently evaluate it more favorably after imagining it. Seven studies demonstrated the role of these metacognitive experiences and their implications for research on affect regulation. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".