Evaluating Attentional Impulsivity: A Biomechatronic Approach
Bibliographic record
Abstract
Executive function, also known as executive control, is a multifaceted construct encompassing several cognitive abilities, including working memory, attention, impulse control, and cognitive flexibility. To accurately measure executive functioning skills, it is necessary to develop assessment tools and strategies that can quantify the behaviors associated with cognitive control. Impulsivity, a range of cognitive control deficits, is typically evaluated using conventional neuropsychological tests. However, this study proposes a biomechatronic approach to assess impulsivity as a behavioral construct, in line with traditional neuropsychological assessments. The study involved 32 healthy adults, who completed the Barratt Impulsiveness Scale (BIS-11) as an initial step. A low-cost biomechatronic system was developed, and an approach based on standard neuropsychological tests, including the trial-making test (TMT) and serial subtraction-by-seven, was used to evaluate impulsivity. Three tests were conducted: WTMT-A (numbers only), WTMT-B (numbers and letters), and a dual-task of WTMT-A and serial subtraction-by-seven. The preliminary findings suggest that the proposed instrument and experiments successfully generated an attentional impulsivity score and differentiated between participants with high and low attentional impulsivity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".