Connecting Self-Awareness and Error-Awareness in Patients with Traumatic Brain Injury
Bibliographic record
Abstract
Impaired self-awareness after traumatic brain injury (TBI) is often seen in stark contrast to the observations of significant-others, who are acutely aware of the difficulties experienced by patients. Our objective was to investigate the relationship between metacognitive knowledge in daily life and emergent awareness of errors during laboratory tasks, since the breakdown of error detection mechanisms may impose limitations on the recovery of metacognitive knowledge after TBI. We also examined the extent to which these measures of awareness can predict dysexecutive behaviors. A sample of TBI patients (n=62) and their significant-others, provided reports of daily functioning post injury. In addition, patients underwent a neuropsychological assessment and were instructed to signal their errors during go/no-go tests. Interrelationships between metacognitive and emergent levels of awareness were examined, after controlling for the influence of secondary cognitive variables. Significant-other ratings correlated with errors made by the patients on neuropsychological tests but not with their premorbid function. Patients who under-reported daily life difficulties or over-reported their competency, compared to significant-other reports, were less likely to show awareness of laboratory errors. Emergent awareness was also identified as the sole predictor of performance on the modified six-element test, an ecologically valid test of multitasking. The online breakdown of error awareness after brain injury is related to difficulties with metacognitive awareness as reported in daily life, and is also predictive of dysexecutive behaviors. These findings are discussed in the context of multidimensional and neural models of awareness and error monitoring.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".