Scratching Beneath the Surface: New Insights into the Functional Properties of the Lateral Occipital Area and Parahippocampal Place Area
Bibliographic record
Abstract
We used fMRI on neurologically intact humans to investigate whether or not there are different neural substrates for the different kinds of information that a visual surface signals (shape from texture vs material properties from texture). Participants attended to differences in the shape (flat/convex), texture and color (wood/rock), or material properties (soft/hard) of a set of circular surfaces. Attending to shape activated the contour-sensitive lateral occipital (LO) area, and attending to texture activated a region of the collateral sulcus (CoS) that overlaps with the parahippocampal place area (PPA). Interestingly, attending to material properties activated the same texture-sensitive region in the CoS. These results demonstrate the existence of different neural substrates for the different types of information that a visual surface signals. With regard to object shape, the organization of the LO area may be complex, with neurons tuned not only to the outline shape of objects, but also to their surface curvature independent of contour. Moreover, to our knowledge, this is the first study to demonstrate that processing surface texture, which occurs within the scene-sensitive PPA, is a route to accessing knowledge about an object's material properties. With this in mind, we propose that models of visual cortical organization should focus not only on the particular stimulus category to which a region maximally responds (e.g., objects, scenes), but also on the stimulus attributes that best support the processing of that category (e.g., shape, texture, material 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".