“Bigger” or “better”: the roles of magnitude and valence in “affective bias”
Bibliographic record
Abstract
Negative affective biases are thought to be a key symptom driving and upholding many psychiatric disorders. When presented with ambiguous information, anxious individuals, for example, tend to anticipate lower rewards than asymptomatic individuals (Aylward et al., 2019. Translating a rodent measure of negative bias into humans: the impact of induced anxiety and unmedicated mood and anxiety disorders. Psychological Medicine). The assumption is that this is because anxious individuals assume “worse” outcomes. However, predictions are often made about high and low rewards, so it is not clear whether the bias is due to the valence (the “worse” option) or just magnitude (the lower number). We therefore explored the roles of valence and magnitude in a translational measure of negative affective bias. We adapted a two-alternative forced choice (2AFC) “reward-reward” task into a “punishment-punishment” paradigm, and followed up with “high reward-high punishment” and “low reward-high punishment” variants. The results from the “punishment-punishment” paradigm – a bias towards higher punishments in healthy controls – suggest that it is outcome magnitude that is important. However, this is qualified by the other variants which indicate that both valence and magnitude are important. Overall, our results temper the assumption that negative affective biases observed in tasks using numeric outcomes are solely as a result of subjective outcome valence.
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 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 | 0.004 |
| 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) | 0.003 | 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".