Being Sad Is Not Always Bad: The Influence of Affect on Expository Text Comprehension
Bibliographic record
Abstract
We investigated how affective states influence expository text comprehension and whether text valence moderates the effects (i.e., mood congruency). In Experiment 1 participants were randomly assigned to a happy or sad affective state (elicited via films) before reading a positive or negative version of a scientific text on animal adaptations. Participants (n = 79) in the sad (film) group had higher scores on deep-reasoning (d = .312) but not surface-level questions on a subsequent multiple-choice comprehension assessment; there was also no evidence for mood congruence. Using a neutral version of the same text, in Experiment 2 participants (n = 52) in a fearful condition performed better on surface-level comprehension questions (d = .594) compared with a sad condition, but the groups were on par for deep-reasoning questions. Experiment 3 (n = 595) did not replicate the findings from Experiment 2 (no comprehension differences between the sad and fear groups) and there were no differences between the fear and happy groups. However, the sad group outperformed the happy group on deep-reasoning questions (d = .210), thereby replicating Experiment 1. The overall findings were confirmed after pooling the data from the three experiments to increase power.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".