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
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".