Hemispheric dominance for scene perception differs across different components of the navigation network
Bibliographic record
Abstract
Patients who develop difficulties in orienting in familiar environments have been well-described in neurology and neuropsychology. This topographical disorientation, when it occurs, follows damage to occipitotemporal regions of the brain. The lesions are often bilateral, but when they are one-sided, disorientation is much more likely to follow from damage to the right hemisphere. However, the evidence from the neuroimaging literature on scene perception and spatial navigation rarely refers to cerebral dominance favoring the right hemisphere. This contradiction is in part explained by how threshold-dependent methods in neuroimaging are often not well suited for visualizing let alone quantifying brain asymmetry. In the present investigation, brain asymmetries for scene perception are quantified in a large sample, enriched with non-right-handed participants who are more heterogeneous for brain asymmetries. Results show a weak but consistent right hemispheric bias. A planned region of interest analysis provided only weak support for models of differential lateralization of perceptual and semantic nodes within the scene network. Surprisingly, right dominance was most prominent in retrosplenial cortex, contrary to models that suggest it functions in semantic/mnemonic rather than perceptual domains. Results are discussed in terms of the utility of such an approach for elucidating the functional nature of different scene network subregions, and how publicly-available datasets will prove exceptionally useful for doing so.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".