Interpretation biases in chronic pain patients: an incidental learning task
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate the impact of chronic pain on interpretation bias for ambiguous faces, using a recently developed paradigm with ecologically valid stimuli. METHODS: Fifty patients with chronic pain and 25 healthy controls were trained to respond to probes following the presentation of happy or painful faces, using an incidental learning task. During a test phase, ambiguous faces were presented. The degree to which participants were faster to respond to probes presented where painful (rather than happy) faces had previously been presented was taken as an indication of the interpretation bias towards painful faces. RESULTS: All participants had learnt the originally presented contingency. As predicted, chronic pain patients showed a greater bias towards interpreting ambiguous faces as painful than control participants. Further, there were correlations between fear of pain and catastrophizing and interpretation bias, indicating that participants with higher fear of pain and higher scores on a measure of catastrophizing were more likely to interpret ambiguous faces as painful. Severity of pain was inversely associated with increased interpretation bias for pain. CONCLUSION: These results show clear evidence that chronic pain patients do demonstrate an interpretation bias towards painful faces and that this bias is greater for those who catastrophize more and have higher levels of fear of pain, but experienced less pain in the preceding week. Given the recent potential shown for interventions that modify cognitive biases, this paradigm would seem to be well suited to future efforts to modify interpretation biases in pain.
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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 | 0.044 | 0.027 |
| 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; both teacher heads agree on what is shown here.
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".