Exploring the processing of the shape and material properties of scenes and objects in human visual cortex
Bibliographic record
Abstract
Recently, I demonstrated that the scene-sensitive PPA is more active for judgments of the material properties of objects (whether an object is made of soft or hard material; Cant & Goodale, 2011), compared to judgments of object shape. This appears inconsistent with the view that PPA is specialized for processing scenes, since the single objects used did not invoke scene imagery. But material-property judgments are important in scene processing as they can affect the strategies used to recognize and navigate through an environment (e.g., soft/hard terrain affects the posture and stability used to navigate through a scene). Thus, the material-property task used previously may have invoked a type of processing in PPA that is distinct from its role in processing the geometry of scenes. Specifically, these findings suggest that PPA represents scenes by processing both spatial (shape) and non-spatial (material) visual features. To investigate this possibility, I used fMRI to examine activity in PPA while participants made shape and material-property judgments of both objects and scenes (images consisted of a central object located within an indoor scene). I also examined activity in LOC, to explore if this object shape-sensitive region is also involved in processing the shape of scenes. Interestingly, judgments of object shape produced the highest activation in LOC (compared with judgments of scene shape, scene material, and object material, which did not differ), demonstrating that LOC is not a general-purpose shape-processing region. In PPA, activation was higher for judgments of object material compared with object shape, replicating previous results. But importantly, activation for both shape and material judgments of scenes was higher than activation for judgments of object features. This demonstrates that PPA does indeed process both spatial and non-spatial visual features, but importantly, this processing is specialized for visual features of scenes, not single objects. Meeting abstract presented at VSS 2014
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".