Different responses of the scene-selective cortical regions to magnocellular- and parvocellular-biased visual information
Bibliographic record
Abstract
Scene perception relies on a set of cortical regions, such as the parahippocampal place area (PPA), retrosplenial complex (RSC), and occipital place area (OPA), exhibiting dissociable functional selectivity to various scene properties. Debates are ongoing about what specific types of visual information are represented in these regions to mediate their different functions. This fMRI study examined the neural bases of functional dissociations of scene-selective regions by selectively biasing visual inputs from the magnocellular (M) and parvocellular (P) cells and comparing patterns of their cortical projections. We manipulated 96 scene images to create stimuli that biased M- (low-contrast, achromatic images defined by luminance) or P- (defined by iso-luminant, red-green contrast) responses, adjusted for each participant’s thresholds. Twenty-four participants performed indoor/outdoor categorization of M- or P-stimuli presented for 1 second. Participants were significantly faster at categorizing P-biased scenes than M-biased, interestingly contrasted to the M-advantages reported in fearful/neutral face categorization (Cushing et al., 2019). For fMRI data analyses, each participant’s PPA, RSC, and OPA were functionally defined using a separate localizer scan. The PPA was significantly more responsive to P-stimuli in general, whereas the RSC showed greater responses to M-stimuli with a slight preference for outdoor images. The OPA activations did not show systematic M/P bias. We next tested whether the PPA and RSC preferences for P- and M-stimuli, respectively, were specific to the task of scene processing. In separate fMRI runs, participants viewed rapid flashes of an achromatic, low-spatial-frequency grating (M-biased) or slow alterations of a red-green, high-spatial-frequency grating (P-biased). Although the M-bias became weaker in the RSC, the P-bias of the PPA remained robust for the scene-irrelevant gratings. Our findings together demonstrate differential processing biases of the scene-selective regions for visual attributes conveyed from the retina to the cortex, facilitating the efficient perception of complementary scene properties.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".