Global and local processing near the left and right hands
Bibliographic record
Abstract
Visual targets can be processed more quickly and reliably when a hand is placed near the target. Both unimodal and bimodal representations of hands are largely lateralized to the contralateral hemisphere, and since each hemisphere demonstrates specialized cognitive processing, it is possible that targets appearing near the left hand may be processed differently than targets appearing near the right hand. The purpose of this study was to determine whether visual processing near the left and right hands interacts with hemispheric specialization. We presented hierarchical-letter stimuli (e.g., small characters used as local elements to compose large characters at the global level) near the left or right hands separately and instructed participants to discriminate the presence of target letters (X and O) from non-target letters (T and U) at either the global or local levels as quickly as possible. Targets appeared at either the global or local level of the display, at both levels, or were absent from the display; participants made foot-press responses. When discriminating target presence at the global level, participants responded more quickly to stimuli presented near the left hand than near either the right hand or in the no-hand condition. Hand presence did not influence target discrimination at the local level. Our interpretation is that left-hand presence may help participants discriminate global information, a right hemisphere (RH) process, and that the left hand may influence visual processing in a way that is distinct from the right hand.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".