Depressive deficits and bias: a direct comparison of two implicit measures of memory
Bibliographic record
Abstract
Abstract Repeated findings of depressive deficits and mood‐congruent biases on explicit measures of memory have lent much support to cognitive models of depression. However, studies to date have been inconclusive with respect to such deficits or biases on implicit measures. Given current assertions that implicit use of memory is far more pervasive than explicit use, clarification of these issues has important implications for our understanding of cognitive factors in clinical depression and its treatment. We consider both these issues, and, in particular, we follow up the suggestion by Roediger and McDermott (1992) that conceptually driven implicit measures of memory are more appropriate to detect depressive bias than those that are typically used, which are perceptually driven. In this study we directly compare the memory performance of 24 clinically depressed patients with 24 nondepressed controls on a perceptually driven implicit task (fragment completion) and a comparable task that is more conceptually driven (cued fragment completion). Although depressive deficits were obtained on both these measures, no bias was revealed. We consider alternative research designs for clarification of these findings. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.020 |
| 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 | 0.001 |
| 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".