An investigation into the effect of OCD tendencies in reveral learning
Bibliographic record
Abstract
Individuals diagnosed with Obsessive-Compulsive Disorder (OCD) appear to have reduced cognitive flexibility and slower reaction times when adjusting associations that they have attached to previous behaviours and creating new ones. In addition, research has found deficits in reversal learning related to patients of OCD. The current study therefore investigated the relationship between OCD and reversal learning. In contrast to previous studies, the findings displayed no significance between the Obsessive-Compulsive Inventory – Revised (OCI-R) scores and reversal learning variables such as adjusting stimuli or selecting the same stimulus depending on the positive/negative response received and correct number of responses. As hypothesised, lower scores on the Vancouver Obsessional Compulsive Inventory – Mental Contamination Scale (VOCI-MC) were found to predict higher number of correct responses. Future research could focus on such variables on a larger scales as well as incorporating clinically diagnosed patients in addition to sub-clinical. Results of the current study add to the growing literature related to OCD. Such literature being crucial in gaining a greater understanding of the disorder and are especially relevant to the current global pandemic.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".