Slowing Down after a Mild Traumatic Brain Injury: A Strategy to Improve Cognitive Task Performance?
Bibliographic record
Abstract
Long-term persistent attention and memory difficulties following a mild traumatic brain injury (TBI) often go undetected on standard neuropsychological tests, despite complaints by mild TBI individuals. We conducted a visual Repetition Detection working memory task to digits, in which we manipulated task difficulty by increasing cognitive load, to identify subtle deficits long after a mild TBI. Twenty-six undergraduate students with a self-report of one mild TBI, which occurred at least 6 months prior, and 31 non-head-injured controls took part in the study. Participants were not informed until study completion that the study's purpose was to examine cognitive changes following a mild TBI, to reduce the influence of "diagnosis threat" on performance. Neuropsychological tasks did not differentiate the groups, though mild TBI participants reported higher state anxiety levels. On our working memory task, the mild TBI group took significantly longer to accurately detect repeated targets on our task, suggesting that slowed information processing is a long-term consequence of mild TBI. Accuracy was comparable in the low-load condition and, unexpectedly, mild TBI performance surpassed that of controls in the high-load condition. Temporal analysis of target identification suggested a strategy difference between groups: mild TBI participants made a significantly greater number of accurate responses following the target's offset, and significantly fewer erroneous distracter responses prior to target onset, compared with controls. Results suggest that long after a mild TBI, high-functioning young adults invoke a strategy of delaying their identification of targets in order to maintain, and facilitate, accuracy on cognitively demanding tasks.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".