Adult norms for the decision-making MindPulse Digital Test
Bibliographic record
Abstract
We present adult normalized data for MindPulse (MP), a new tool evaluating attentional and executive functioning (AEF) in decision-making. We recruited 722 neurotypical participants (18–80 years), with 149 retested. The MP test includes three tasks: Simple Reaction Time (SRT), Go/No-go, and complex Go/No-go, involving perceptual components, motor responses, and measurements of reaction time (RT) and correctness. We compare responses, evaluating 14 cognitive indices (including new composite indices to describe AEF: Executive Speed and Reaction to Difficulty). We adjust for age/sex effects, introduce a difficulty scale, and consider standard deviations, aberrant times, and Spearman Correlation for speed-accuracy balance. Wilcoxon unpaired rank test is used to assess sex effects, and linear regression is employed to assess the age linear dependency model on the normalized database. The study demonstrated age and sex effects on RTs, in all three subtests, and the ability to correct it for individual results. The test showed excellent validity (Cronbach Alpha for the three subtasks is 92, 87, 95%) and high internal consistency (p < 0.001 for each subtask significantly faster than the more complex subtask) of the MP across the wide age range. Results showed correlation within the three RT parts of the test (p < .001 for each) and the independence of SRT, RD, and ES indices. The Retest effect was lower than intersubject variance, showing consistency over time. This study highlights the MP test’s strong validity on a homogeneous, large adult sample. It emphasizes assessing AEF and Reaction to Difficulty dynamically with high sensitivity.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".