Why the contralesional hemifield is scanned by patients with hemianopia but not with hemineglect: computational modeling of mechanisms of neural compensation
Bibliographic record
Abstract
Hemianopia patients have a contralesional visual hemifield deficit yet, during visual search, direct eye movements toward and explore their blind side. In fact, during line bisection tasks, eye movements are guided preferentially to the contralesional blind hemifield and there is a line bisection bias toward this hemifield. In contrast, scan paths from hemineglect patients typically ignore the contralesional hemifield during both line bisection and visual search, and these subjects show an ipsilesional bisection bias. What strategies do hemianopia patients have or develop that compensate for the lack of visual information in their blind hemifield and why is such a compensatory process not accessible in visual neglect? We used a neurophysiology-based computational model to examine possible neural compensatory processes implemented in hemianopia and why these are ineffective in hemineglect following parietal lesions. We propose two different compensation mechanisms that could be employed during hemianopic adaptation to facilitate scanning eye movements towards objects they cannot see in their blind fields. First, a spatial compensatory bias can facilitate search scanning in a complex scene and allows locations in the blind field to attract attention and be fixated. Second, a strategy based on Gestalt grouping, which we implement through extrastriate lateral interactions, permits accurate placement of fixations when viewing the portion of a continuous object that falls into the blind field, such as a horizontal line. We show that, while these compensatory mechanisms facilitate attentional scanning in the blind hemifield in hemianopia, these same mechanisms are ineffective in hemineglect following parietal lesion. We conclude that this type of neurobiologically realistic computational modeling can suggest plausible neural mechanisms of compensation in hemianopia, which can be tested empirically, and which may have some use in guiding rehabilitation strategies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".