Load-induced transient perceptual neglect is insensitive to reference frame manipulations
Bibliographic record
Abstract
Recently, Emrich, Burianová, and Ferber (2011) reported that high visual working memory (VWM) load can induce a neglect-like disadvantage in object recognition, confined to the viewer’s left hemifield. The authors suggested that inhibition of the right temporoparietal junction (TPJ) which results from high VWM load causes interference with selecting objects from the left side of space. This explanation fits well with the role of TPJ lesions in causing visual neglect. But is the nature of this transient, load-induced perceptual neglect similar to the neglect behaviour observed in patient populations? One way to address this question is to test the sensitivity of this transient neglect to manipulations of spatial reference frames. Neglect patients show deficits not only in retino-centric reference frames, but also in stimulus-centered reference frames. To determine if load-induced transient neglect is also sensitive to stimulus-centred reference frames, we used a change-detection task containing conceptual cues (e.g., ‘left’, ‘above’) to probe a memory item on each trial. Critically, subjects were told to interpret the cues within a particular reference frame (rotated 0°, 90°, or 180° from the retino-centric frame) fixed throughout an entire block. Performance on the change-detection task served as a manipulation check to monitor subjects' ability to maintain the stimulus-based reference frame. Object recognition stimuli were presented on 30% of trials, bilaterally, allowing a comparison between both the retino-centric and stimulus-centered left and right. Results suggest that load-induced transient neglect is only a function of the retino-centric reference frame and does not depend on higher-order reference frames. Importantly, these results suggest that load-induced perceptual neglect does not capture the full extent of visual neglect symptoms and is primarily due to inhibited visual encoding. Meeting abstract presented at VSS 2012
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".