Random Item Generation Is Affected by Age
Bibliographic record
Abstract
Purpose: Random item generation (RIG) involves central executive functioning. Measuring aspects of random sequences can therefore provide a simple method to complement other tools for cognitive assessment. We examine the extent to which RIG relates to specific measures of cognitive function, and whether those measures can be estimated using RIG only. Method: Twelve healthy older adults (age: M = 70.3 years, SD = 4.9; 8 women and 4 men) and 20 healthy young adults (age: M = 24 years, SD = 4.0; 12 women and 8 men) participated in this pilot study. Each completed a RIG task, along with the color Stroop test, the Repeatable Battery for the Assessment of Neuropsychological Status, and the Peabody Picture Vocabulary Test-Fourth Edition (Dunn & Dunn, 2007). Several statistical features extracted from RIG sequences, including recurrence quantification, were found to be related to the other measures through correlation, regression, and a neural-network model. Results: The authors found significant effects of age in RIG and demonstrate that nonlinear machine learning can use measures of RIG to accurately predict outcomes from other tools. Conclusions: These results suggest that RIG can be used as a relatively simple predictor for other tools and in particular seems promising as a potential screening tool for selective attention in healthy aging.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".