Cognitive-Affective Verbal Learning Test: An integrated measure of affective and neutral words.
Bibliographic record
Abstract
Despite growing affective-memory research, only 2 potential clinical measures have been published, each with limitations. We describe the development and piloting of an integrated memory measure for neutral and affectively valenced words, the Cognitive-Affective Verbal Learning Test (C-AVLT). The C-AVLT and mood self-report measures were administered to 124 healthy university students in Study 1, with readministration to 40 students after 1 week. In Study 2, the C-AVLT and other neuropsychological measures of memory and emotion were administered to 61 patients referred for polysomnogram evaluation of obstructive sleep apnea (OSA). Study 1 supported the C-AVLT's internal and test-retest reliabilities, as well as concurrent validity, that is, the affective-bias scores but not performance scores correlated with self-reported mood. In Study 2, convergent, criterion (specifically cross-sectional concurrent validity), and incremental validity were supported with regard to both performance and affective-bias scores within the OSA sample. We demonstrated the C-AVLT is a reliable and clinically useful measure of both memory and affective-processing bias in 2 samples. Future clinical and research recommendations for the C-AVLT are discussed, including broadening normative data and criterion validity data in psychiatric and neurological samples. (PsycINFO Database Record
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".