Fishing for happiness: The effects of generating positive imagery on mood and behaviour
Bibliographic record
Abstract
Experimental evidence using picture-word cues has shown that generating mental imagery has a causal impact on emotion, at least for images prompted by negative or benign stimuli. It remains unclear whether this finding extends to overtly positive stimuli and whether generating positive imagery can increase positive affect in people with dysphoria. Dysphoric participants were assigned to one of three conditions, and given instructions to generate mental images in response to picture-word cues which were either positive, negative or mixed (control) in valence. Results showed that the positive picture-word condition increased positive affect more than the control and negative conditions. Participants in the positive condition also demonstrated enhanced performance on a behavioural task compared to the two other conditions. Compared to participants in the negative condition, participants in the positive condition provided more positive responses on a homophone task administered after 24h to assess the durability of effects. These findings suggest that a positive picture-word task used to evoke mental imagery leads to improvements in positive mood, with transfer to later performance. Understanding the mechanisms underlying mood change in dysphoria may hold implications for both theory and treatment development.
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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 | 0.002 |
| 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".