Comparability of Total Score Performance on the Rey–Osterrieth Complex Figure and a Modified Taylor Complex Figure
Bibliographic record
Abstract
One of the most commonly used neuropsychological measures of visuo-spatial abilities is the Rey-Osterrieth Complex Figure (ROCF) Test. Previous research has reliably shown that its companion figure, the Taylor Complex Figure, is not a comparable measure of visuo-spatial memory. The aims of the three studies presented here are to (a) introduce a modified version of the Taylor Figure (MTCF), and (b) examine the comparability of the ROCF and the MTCF using two different administration procedures. The first two studies used a between-subjects design in which half of the study participants received the ROCF and half received the MTCF. In Study 1, an incidental procedure was used in which participants were asked to first copy a figure and then reproduce it from memory without prior warning. In Study 2, an intentional procedure was used in which participants were first asked to observe the figures for the express purpose of reproducing them from memory followed by delay and copy trials. In Study 3, a within-subjects design using counterbalancing and an intentional procedure was used to examine the comparability of the two figures when administered to the same participants as in a true test-retest situation. Overall, the studies demonstrated that learning, memory, and copy score performance on the MTCF was comparable to that on the ROCF.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".