The influence of perceived size/distance on object and place ROIs
Bibliographic record
Abstract
Distinct regions of posterior neocortex show reliable fMRI activation to images of objects and places (including buildings), respectively. What is not known is whether these regions are sensitive only to the geometry and surface cues defining the object and place stimuli, or whether the implied size and/or distance of those stimuli also matters. Regions of interest that responded primarily to images of places, faces, and small objects were determined with localizer fMRI scans. Participants then performed an oddball-detection task while viewing images of small objects (cameras) and buildings (garages) in different contexts: against a blank background (no size/distance cues other than those implied by the object's form); in the foreground of a simple size/depth illusion (implied small/close); and in the background of the illusion (implied large/far). Even though retinal size and eccentricity of the stimuli were equal in all conditions, activity in a ‘place’ ROI corresponding to the parahippocampal place area (PPA) was increased in the large/far conditions, relative to both the near/close and the blank background conditions. Conversely, activity in an ‘object’ ROI (lateral occipital area, LO) was increased in the near/close conditions. Both PPA and LO also showed category specificity. A more posterior place ROI (in the collateral sulcus) did not show the same effects of the size/distance manipulation as the PPA but was driven primarily by image category. his study clarifies the nature of the processing within higher-level visual areas by revealing the relative strengths of category and size/distance effects in different functional areas.
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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 | 0.003 |
| 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".