Affective forecasting accuracy in obsessive compulsive disorder
Bibliographic record
Abstract
BACKGROUND: Research indicates that people suffering from obsessive compulsive disorder (OCD) possess several cognitive biases, including a tendency to over-estimate threat and avoid risk. Studies have suggested that people with OCD not only over-estimate the severity of negative events, but also under-estimate their ability to cope with such occurrences. What is less clear is if they also miscalculate the extent to which they will be emotionally impacted by a given experience. AIMS: The aim of the current study was twofold. First, we examined if people with OCD are especially poor at predicting their emotional responses to future events (i.e. affective forecasting). Second, we analysed the relationship between affective forecasting accuracy and risk assessment across a broad domain of behaviours. METHOD: Forty-one OCD, 42 non-anxious, and 40 socially anxious subjects completed an affective forecasting task and a self-report measure of risk-taking. RESULTS: Findings revealed that affective forecasting accuracy did not differ among the groups. In addition, there was little evidence that affective forecasting errors are related to how people assess risk in a variety of situations. CONCLUSIONS: The results of our study suggest that affective forecasting is unlikely to contribute to the phenomenology of OCD or social anxiety disorder. However, that people over-estimate the hedonic impact of negative events might have interesting implications for the treatment of OCD and other disorders treated with exposure therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".