Brain Imaging of Attentional Networks in Normal and Pathological States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ability to image the human brain has provided a new perspective for neuropsychologists in their efforts to understand, diagnose, and treat insults to the human brain that might occur as the result of stroke, tumor, traumatic injury, degenerative disease, or errors in development. These new findings are the major theme of this special issue. In our article, we consider brain networks that carry out the functions of attention. We outline several such networks that have been studied in normal and pathological states. These include networks for orienting to sensory stimuli, for maintaining the alert state, and for orchestrating volitional control. There is evidence that these networks have a certain degree of anatomical and functional independence, but that they also interact in many practical situations. Damage to each of these networks, irrespective of the source, produces distinctive neuropsychological deficits. We consider the links between the etiology of the injury and changes in cognition and behavior and examine the role of brain imaging in the study of rehabilitation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it