Bibliographic record
Abstract
This study examined the effects on recall of story details of congruity or incongruity between the hedonic valence of literary texts and odours inhaled while reading them. During the reading session, 24 undergraduates (12 males and 12 females) read two passages involving positive subject matter and two with negative subject matter while sniffing pleasant or unpleasant odours in a within-subject fully counterbalanced design. Subjects rated their experience of each text on eleven 7-point scales. During the test session 48 hours later, subjects read a two-word title associated with each of the passages and inhaled the odour that was paired with it in the reading session. They also rated their experience on six of the scales that had been used during the reading session. Results showed that hedonic congruence between the passage and the odour fostered enhanced recall during the test session. The combination of positive subject matter and positive odour was reflected in more accurate recall of character details, while pairing negative subject matter and negative odour resulted in more accurate recall of setting details. Regression analysis showed that overall recall accuracy was increased by identifying with the characters in the stories and for passages that were found pleasing and personally meaningful. Consistent with the literature on implicit learning involving odours, recall accuracy varied inversely with perceived odour intensity. Implicit learning involving odours and literary passages is therefore fostered by unity in the reading experience.
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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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".