Developing a measure of interpretation bias for depressed mood: An ambiguous scenarios test
Bibliographic record
Abstract
The tendency to interpret ambiguous everyday situations in a relatively negative manner (negative interpretation bias) is central to cognitive models of depression. Limited tools are available to measure this bias, either experimentally or in the clinic. This study aimed to develop a pragmatic interpretation bias measure using an ambiguous scenarios test relevant to depressed mood (the AST-D). In Study 1, after a pilot phase (N = 53), the AST-D was presented via a web-based survey (N = 208). Participants imagined and rated each AST-D ambiguous scenario. As predicted, higher dysphoric mood was associated with lower pleasantness ratings (more negative bias), independent of mental imagery measures. In Study 2, self-report ratings were compared with objective ratings of participants' imagined outcomes of the ambiguous scenarios (N = 41). Data were collected in the experimental context of a functional Magnetic Resonance Imaging scanner. Consistent with subjective bias scores, independent judges rated more sentences as negatively valenced for the high versus low dysphoric group. Overall, results suggest the potential utility of the AST-D in assessing interpretation bias associated with depressed mood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".