A psycholinguistic analysis of clinical list-learning tests
Bibliographic record
Abstract
Abstract Neuropsychological assessments depend on language-based measures of cognitive functioning and the proper diagnosis of certain disorders relies on patterns of impairment shown on these measures (Lezak et al., 2004). The current project was motivated by the relative lack of literature integrating psycholinguistic experimental findings and clinical neuropsychological research on tests of verbal memory, specifically list learning. It has been well documented that word-level characteristics impact language processing and memory (see Yap & Balota, 2015 for a review). Therefore, it is critical that neuropsychologists begin to understand how current measures can be confounded by the underlying lexical and semantic characteristics of the stimuli and how, if used properly, those characteristics could aid in diagnostic specificity. The current study examined the structure of popular list learning tests and analyzed the influence of several psycholinguistic variables on the performance of healthy undergraduate participants. Results demonstrated that (1) age of acquisition, emotional valence, semantic neighborhood density, and imageability predicted recall accuracy of items from neuropsychological tests and (2) only one of the ten clinical test lists examined adequately controlled for these influential variables. Thus, clinicians could be missing clinically relevant data by ignoring psycholinguistic contributions to patient performance.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".