Visual working memory deficits following right brain damage
Bibliographic record
Abstract
Visual working memory (VWM) involves the encoding and maintenance of visual information over time, with the requirement that object features be accurately bound to spatial locations. We and others have shown that damage to the right hemisphere leads to impaired spatial working memory. Here, we test the notion that right brain damage (RBD) may have consequences for domain general VWM. We had eight RBD patients and a group of healthy control participants perform a VWM task under different loads (1 to 3 items to recall) and spatial competition (high vs. low). All participants were asked to remember the colour of target items presented on the right side of space. Patients showed impaired encoding of information evident in poor precision of memory representations and increased guessing rates even at a set size of only one item. Our data suggests that VWM capacity is severely limited following RBD. Although five of the eight patients presented with neglect, it is not clear whether this deficit in VWM is unique to the syndrome. We suggest that future work should directly pit attention and VWM demands against one another in the same patients to determine whether the confluence of deficits in these domains is the critical determinant of the neglect syndrome. Regardless of the implications for the neglect syndrome, however, our data show that VWM deficits in RBD patients extend into non-spatial feature space.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".