Global versus local processing: seeing the left side of the forest and the right side of the trees
Bibliographic record
Abstract
Previous studies using hierarchical figures (where a large global shape is composed of a series of smaller local shapes) suggest that performance is better for local features presented in the right relative to left visual field, whereas the opposite pattern is observed for global features. However, these previous studies have focused on effects between hemifields. Recent data from patients with neurological damage suggest that local deficits can be allocentric (e.g., following left hemisphere injury, individuals are relatively slow to detect features on the right side of an object, regardless of visual field). Therefore, we decided to extend previous global versus local research by also observing local performance within hemifields. Specifically, on each trial we presented two hierarchical figures (one in each hemifield), but crucially the left and right side of each item were composed of different local features. In this task, the participant simply reports if a circle is present, regardless of location or whether this is a local or global feature. We observed that both neurologically healthy individuals, as well as an individual with brain injury, were relatively better detecting local information on the right side of objects, regardless of spatial location, while both showed better performance for global stimuli in the left visual field. This work is consistent with recent work in patients with neurological damage, and provides a new paradigm for exploring hemispheric specialization.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".