Deficits in reflexive covert attention following cerebellar injury
Bibliographic record
Abstract
Traditionally the cerebellum has been known for its important role in coordinating motor output. Over the past 15 years numerous studies have indicated that the cerebellum plays a role in a variety of cognitive functions including working memory, language, perceptual functions, and emotion. In addition, recent work suggests that regions of the cerebellum involved in eye movements also play a role in controlling covert visual attention. Here we investigated whether regions of the cerebellum that are not strictly tied to the control of eye movements might also contribute to covert attention. To address this question we examined the effects of circumscribed cerebellar lesions on reflexive covert attention in a group of patients (n = 11) without any gross motor or oculomotor deficits, and compared their performance to a group of age-matched controls (n = 11). Results indicated that the traditional RT advantage for validly cued targets was significantly smaller at the shortest (50 ms) SOA for cerebellar patients compared to controls. Critically, a lesion overlap analysis indicated that this deficit in the rapid deployment of attention was linked to damage in Crus I and Crus II of the lateral cerebellum. Importantly, both cerebellar regions have connections to non-motor regions of the prefrontal and posterior parietal cortices-regions important for controlling visuospatial attention. Together, these data provide converging evidence that both lateral and midline regions of the cerebellum play an important role in the control of reflexive covert visual attention.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".