Long-term working memory deficits after concussion: Electrophysiological evidence
Bibliographic record
Abstract
BACKGROUND: Persistent complaints of lingering memory and concentration difficulties are common following a concussion, although the brain basis of these is unknown. Some suggest abnormalities can be found on the P300 event-related potential component, recorded using electroencephalography (EEG), despite unobservable cognitive impairments. OBJECTIVE: To examine the P300 and cognitive performance following a remote concussion during an n-back task that varies in working memory load. RESEARCH DESIGN: Seventeen participants with a remote concussion and 17 controls performed a visual n-back task in which working memory demands were systematically increased by manipulating cognitive load. Participants also completed neuropsychological and self-report measures. RESULTS: The concussion group showed a decrease in P300 amplitude compared to controls that was independent of working memory load on the n-back task. While no performance differences were observed between groups, P300 amplitude was negatively correlated with response times at higher loads in both groups. CONCLUSION: High functioning young adults with a remote concussion may have inefficient recruitment of processing resources for target identification, evident by the attenuated P300. The negative correlations between response time and P300 amplitude suggest that the time necessary to accurately respond to targets increases as the efficiency of allocating processing resources decreases during highly demanding working memory tasks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".