Interhemispheric transfer of semantic information facilitates bilateral word recognition.
Bibliographic record
Abstract
Language comprehension is left-lateralized but with variable contributions from the right hemisphere. When both hemispheres are stimulated simultaneously using divided visual field presentation, performance may be enhanced or hindered depending on the complexity of the task, and these effects may relate to independent processing in both hemispheres or to information transfer between hemispheres. Simultaneous stimulation of both hemispheres is thought to suppress interhemispheric interactions, but information transfer may nonetheless occur. Studies with simultaneous bilateral displays have demonstrated that semantic information from the contralateral visual field (and hemisphere) can facilitate relatedness judgments and lexical decisions. The current study extends this line of research by assessing semantic information transfer in a bilateral word identification task. Task manipulations involving directed spatial attention and asymmetric primes (e.g., ATOM → BOMB) were used to isolate automatic priming as opposed to top-down processing. The results revealed 2 main findings: (a) interhemispheric priming in the form of improved word recognition occurred specifically in conditions designed to isolate automatic transfer of semantic information, that is, in the attended visual field for the target word in asymmetric prime pairs, and (b) there is evidence for an asymmetrical transfer of semantic information, in that the subordinate left visual field-right hemisphere benefited more from such transfer. Together, these results demonstrate evidence for automatic interhemispheric transfer of semantic information, even under conditions of simultaneous bilateral display. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".