The effect of sleepiness on performance monitoring: I know what I am doing, but do I care?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The behavioral, cognitive, and psychophysiological effects of extended wakefulness are well known. As time awake increases, errors become more common and are often attributed to lapses in attention. Such lapses can be reflected in the error-related negativity (Ne/ERN), a negative electroencephalogram deflection occurring after errors and is thought to be related to error detection or response conflict. Following the Ne/ERN, a positive deflection (error positivity, Pe) is also observed and is thought to reflect further evaluation of the error. To elicit Ne/ERNs, the Eriksen Flanker Task was administered to 17 women (aged 19-45 years) at two levels of alertness (4 and 20 h awake). After extended wakefulness, participants reported being subjectively sleepier and performing worse, but showed no significant difference in subjective effort. Across alertness conditions, they reported a similar number of subjective errors which closely matched an objective analysis of the errors. The Ne/ERN was not significantly reduced by sleepiness in contrast to the Pe which was reduced. Behavioral slowing after errors was larger in the alert than in the sleepy condition. These results show that after 20 h of wakefulness, individuals are reacting to their errors. However, further evaluation of the error, and remediation of these errors may be impaired despite continued effort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it