It's about time: why right spatial neglect is mild
Bibliographic record
Abstract
Neglect of right visual space arising from left hemisphere lesions is typically milder than the converse circumstance (left neglect from right hemisphere lesions). Recent theories of left neglect suggest that non-spatial deficits play a crucial role in the disorder. One potential explanation for the more mild nature of right spatial neglect would suggest that these patients may not demonstrate the same impairments in non-spatial functions that are evident in left spatial neglect. We examined this hypothesis in one patient (HW) with a posterior cerebral artery stroke affecting temporal and occipital cortex and the posterior thalamus. Patient HW demonstrated mild right spatial neglect on line bisection and figure copying tasks. We then tested his perception of time on two tasks. The patient first had to estimate the duration (to the nearest second) of visual events presented for intervals of 5, 15, 30, or 60 seconds. Left spatial neglect patients demonstrate a characteristic performance on this task such that they massively underestimate all durations. The second task provided an auditory analogue to the visual time estimation task with the patient asked to estimate the duration of newspaper stories read aloud for durations equal to those used in the visual task. HW demonstrated normal estimates of visual events. For the auditory task he consistently overestimated durations and demonstrated a far greater degree of variance. We suggest that right spatial neglect is mild due to the absence of non-spatial deficits including the temporal perception of visual events.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".