Spatial Working Memory Deficits Represent a Core Challenge for Rehabilitating Neglect
Bibliographic record
Abstract
Left neglect following right hemisphere injury is a debilitating disorder that has proven extremely difficult to rehabilitate. Traditional models of neglect have focused on impaired spatial attention as the core deficit and as such, most rehabilitation methods have tried to improve attentional processes. However, many of these techniques (e.g., visual scanning training, caloric stimulation, neck muscle vibration) produce only short-lived effects, or are too uncomfortable to use as a routine treatment. More recently, many investigators have begun examining the beneficial effects of prism adaptation for the treatment of neglect. Although prism adaptation has been shown to have some beneficial effects on both overt and covert spatial attention, it does not reliably alter many of the perceptual biases evident in neglect. One of the challenges of neglect rehabilitation may lie in the heterogeneous nature of the deficits. Most notably, a number of researchers have shown that neglect patients present with severe deficits in spatial working memory (SWM) in addition to their attentional impairments. Given that SWM can be seen as a foundational cognitive mechanism, critical for a wide range of other functions, any deficit in SWM memory will undoubtedly have severe consequences. In the current review we examine the evidence for SWM deficits in neglect and propose that it constitutes a core component of the syndrome. We present preliminary data which suggest that at least one current rehabilitation method (prism adaptation) has no effect on SWM deficits in neglect. Finally, we end by reviewing recent work that examines the effectiveness of SWM training and how SWM training may prove to be a useful avenue for future rehabilitative efforts in patients with neglect.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".