Hedging in decision making in disorders of the impulsive-compulsive spectrum
Bibliographic record
Abstract
This dissertation comprises three studies that investigated the construct of hedging as a decision making strategy in individuals with Obsessive-Compulsive Disorder (OCD). Hedging refers to the tendency to keep options available when there is a threat of loss of the options that is motivated by the underlying construct of loss aversion (i.e., Prospect Theory). Hedging introduces a behavioural economic approach to the study and understanding of the impact of loss aversion on decision making. Participants played two conditions of the Doors Game (Shin & Ariely, 2004) in which they were instructed to maximize their earnings by tapping three doors in any order: i) constant availability (CA), where all doors remain available; and ii) decreasing availability (DA), where doors fade and disappear if left untapped after a short time (to elicit hedging). In Study One, undergraduates (N = 108) played both the CA and DA conditions and evidence indicates more frequent switching in the DA than the CA condition. There was also a significant negative association between hedging and the cognitive concern subscale of anxiety sensitivity. Study Two examined other psychological correlates of hedging in another undergraduate sample (N = 63) and yielded significant negative associations with the physical component of state anxiety and experience seeking. In Study Three, the results of a comparison of hedging among OCD, Gambling Disorder (GD), and Healthy Control (HC) groups yielded no significant differences. Correlates of hedging, however, differed among the groups and regression analyses suggest that hedging in OCD is negatively predicted by obsessiveness and decisiveness (subscale of the Need for Cognitive Closure; NFC), and positively predicted by experience seeking (subscale of the Sensation Seeking Scale). In the GD group, closed-mindedness (subscale of NFC) positively predicted hedging. In the HC group, fun-seeking (subscale of Behavioral Inhibition and Behavioral Activation Scale) positively predicted hedging. Implications: This work is the first to demonstrate predictors of hedging in OCD using a loss aversion paradigm where evidence suggests that obsessional and motivational drives lead to premature choice selection. Pursuing the loss aversion perspective could significantly advance the decision making research in OCD and in other clinical populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".