Behavioral and Neuroimaging Evidence for a Contribution of Color and Texture Information to Scene Classification in a Patient with Visual Form Agnosia
Bibliographic record
Abstract
A common notion is that object perception is a necessary precursor to scene perception. Behavioral evidence suggests, however, that scene perception can operate independently of object perception. Further, neuroimaging has revealed a specialized human cortical area for viewing scenes that is anatomically distinct from areas activated by viewing objects. Here we show that an individual with visual form agnosia, D.F., who has a profound deficit in object recognition but spared color and visual texture perception, could still classify scenes and that she was fastest when the scenes were presented in the appropriate color. When scenes were presented as black-and-white images, she made a large number of errors in classification. Functional magnetic resonance imaging revealed selective activation in the parahippocampal place area (PPA) when D.F. viewed scenes. Unlike control observers, D.F. demonstrated higher activation in the PPA for scenes presented in the appropriate color than for black-and-white versions. The results demonstrate that an individual with profound form vision deficits can still use visual texture and color to classify scenes-and that this intact ability is reflected in differential activation of the PPA with colored versions of scenes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".