Functional MRI of Letter Cancellation Task Performance in Older Adults
Bibliographic record
Abstract
The Letter Cancellation Task (LCT) is a widely used pen-and-paper probe of attention in clinical and research settings. Despite its popularity, the neural correlates of the task are not well understood. The present study uses functional magnetic resonance imaging (fMRI) and specialized tablet technology to identify the neural correlates of the LCT in 32 healthy older adults between 50-85 years of age, and further investigates the effect of healthy aging on performance. Subjects performed the LCT in its standard pen-and-paper administration and with the tablet during fMRI. Performance on the tablet was significantly slower than on pen-and-paper, with both response modes showing slower performance as a function of age. Across all ages, bilateral brain activation was observed in the cerebellum, superior temporal lobe, precentral gyrus, frontal gyrus, and occipital and parietal areas. Increasing age correlated with reduced brain activity in the supplementary motor area, middle occipital gyrus, medial and inferior frontal gyrus, cerebellum and putamen. Better LCT performance was correlated with increased activity in the middle frontal gyrus, and reduced activity in the cerebellum. The brain regions activated are associated with visuospatial attention and motor control, and are consistent with the neural correlates of LCT performance previously identified in lesion studies.
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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 | 0.001 |
| 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".