Individual differences effects on the psychological refractory period
Bibliographic record
Abstract
The goal of this study was to assess the impact of individual neuropsychological differences on the ability to share attention between concurrent tasks. Participants (n = 20) were trained on six single task practice sessions and dual-task was assessed with reaction time performance on a psychological refractory period (PRP) paradigm. Neuropsychological test scores were also acquired. Furthermore, one of the known variables that can influence performances on neuropsychological tests is gender, which was added as a potential predictor. Results show that the small PRP group was associated with better performances in processing speed, inhibition, flexibility and working memory on neuropsychological tests. Gender also had an impact on the PRP, males having a lower PRP than females. A multiple regression was performed to determine which variables explained the most PRP duration, which showed that 49.1% of the variance of the PRP length could be explained by gender, reaction times of the PRP practice trials at the sixth session, the denomination and flexibility conditions of the Modified Stroop Task as well as results on the Symbol Search Test. Gender was the variable that explained the PRP variance the most (23%). Processing speed also seemed to be a great determinant of the PRP as well as the ability to alternate between task-sets as assessed by the Flexibility condition of the Modified Stroop Task. Thus, this study reveals that good performances on certain neuropsychological tests could predict one's ease to manage two tasks simultaneously with a higher chance for males to perform better.
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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 | 0.004 |
| 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) | 0.004 | 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".