Simply Imagining Sunshine, Lollipops and Rainbows Will Not Budge the Bias: The Role of Ambiguity in Interpretive Bias Modification
Bibliographic record
Abstract
Imagery-based interpretive bias modification (CBM-I) involves repeatedly imagining scenarios that are initially ambiguous before being resolved as either positive or negative in the last word/s. While the presence of such ambiguity is assumed to be important to achieve change in selective interpretation, it is also possible that the act of repeatedly imagining positive or negative events could produce such change in the absence of ambiguity. The present study sought to examine whether the ambiguity in imagery-based CBM-I is necessary to elicit change in interpretive bias, or, if the emotional content of the imagined scenarios is sufficient to produce such change. An imagery-based CBM-I task was delivered to participants in one of four conditions, where the valence of imagined scenarios were either positive or negative, and the ambiguity of the scenario was either present (until the last word/s) or the ambiguity was absent (emotional valence was evident from the start). Results indicate that only those who received scenarios in which the ambiguity was present acquired an interpretive bias consistent with the emotional valence of the scenarios, suggesting that the act of imagining positive or negative events will only influence patterns of interpretation when the emotional ambiguity is a consistent feature.
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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 | 0.013 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".