Staying on the job: the frontal lobes control individual performance variability
Bibliographic record
Abstract
The causes of variability of performance by individual subjects have rarely been investigated, although excessive variability or inconsistency may be a functionally significant factor for many real-life activities. Our objective was to determine whether patients with focal frontal brain lesions have excessive individual performance variability. Thirty-six patients with focal frontal (n=25) or non-frontal (n=11) lesions were compared with 12 control subjects on different measures of intra-individual variability: dispersion within a testing session; and consistency across testing sessions. Four reaction time tasks, varying in levels of complexity and based on a model of detection using feature integration, were administered. Following the first test session, 22 patients and 10 controls returned for two subsequent test sessions, which permitted the assessment of consistency of performance. Measures of abnormal dispersion of performance on these tests were observed in frontal patients only (except those with exclusively inferior medial damage). Disturbances in consistency of performance were observed primarily in patients with frontal lesions. Damage to the frontal lobes impairs the stability of cognitive performance. Damage to different frontal regions causes different profiles of abnormal variability. Fluctuations in performance of a task may underlie some of the reported difficulties in daily tasks reported by patients with frontal injuries.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 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 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".