Impact of divided attention during verbal learning in young adults following mild traumatic brain injury
Bibliographic record
Abstract
PRIMARY OBJECTIVE: The goal of the present study was to assess the impact of mild traumatic brain injury (MTBI) on episodic memory performance in relation to attentional and executive control processes in young adults. RESEARCH DESIGN/METHODS: A verbal memory paradigm manipulating attentional load (full attention or divided attention) and semantic congruency between pairs of category-target words during encoding was administrated to 13 individuals with MTBI and 12 normal control participants. Environmental supports during retrieval (free recall, cued recall and recognition modes) were also manipulated. MAIN OUTCOMES AND RESULTS: Results show that recall performances of individuals with MTBI were similar to those of controls when words were encoded under full attention. In contrast, individuals with MTBI performed worse than control participants when encoding under divided attention, whatever the semantic link between pairs of words. CONCLUSIONS: By using a sensitive test, one was able to objectively measure subtle impairments in memory performance, suggesting a diminished availability of attentional resources after MTBI. Young adults' learning of verbal material under divided attention might be compromised by the reduction of cognitive resources following MTBI. These findings are also discussed in light of different factors that can influence cognitive performance.
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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 | 0.001 | 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 | 0.000 | 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".