Recognizing improved Complex Figure memory assessment: The Emory 4-choice Complex Figure recognition task
Bibliographic record
Abstract
OBJECTIVE: We compare the Emory 10-item, 4-choice Rey Complex Figure (CF) Recognition task with the Meyers and Lange (M&L) 24-item yes/no CF Recognition task in a large cohort of healthy research participants and in patients with heterogeneous movement disorder diagnoses. While both tasks assess CF recognition, they differ in key aspects including the saliency of target and distractor responses, self-selection versus forced-choice formats, and the length of the item sets. PARTICIPANTS AND METHODS: There were 1056 participants from the Emory Healthy Brain Study (EHBS; average MoCA = 26.8, SD = 2.4) and 223 movement disorder patients undergoing neuropsychological evaluation (average MoCA = 24.3, SD = 4.0). RESULTS: = 0.81). Unidimensional two-parameter logistic item response theory analysis revealed that many M&L items had low discrimination values and extreme difficulty parameters, which contributed to the task's reduced sensitivity, particularly at lower cognitive proficiency levels relevant to clinical diagnosis. Dimensionality analyses indicated the influence of response sets as a potential contributor to poor item performance. CONCLUSIONS: Emory CF Recognition task demonstrates superior psychometric properties and greater sensitivity to cognitive impairment compared to the M&L task. Its ability to more precisely measure lower levels of cognitive functioning, along with its brevity, suggests it may be more effective for diagnostic use, especially in clinical populations with cognitive decline.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".