An fMRI investigation of the perception of form, texture, and colour in human occipito-temporal cortical pathways
Bibliographic record
Abstract
In a previous neuroimaging study (Cant et al., VSS 04), we showed that the processing of object form selectively engages the lateral occipital area (LO), whereas the processing of surface properties selectively engages the inferior occipital gyrus (IOG) and the collateral sulcus (CoS). It was not clear, however, whether the pattern of activation associated with attending to the surface properties of objects was related to the processing of colour, the processing of visual texture, or a weighted combination of the two. To explore this question, we carried out an additional neuroimaging study in which participants performed same/different judgements in separate blocks of trials on pairs of unfamiliar ‘nonsense’ objects that shared the same form, colour, or texture. Again we showed that attention to object form was associated with specific activation in area LO, whereas attention to surface properties was associated with specific activation in areas IOG and CoS. Additional surface-property selective regions were also identified, including the inferior temporal sulcus, the lingual sulcus, and the fusiform gyrus. In all the surface-property selective regions, however, the activation associated with surface texture was significantly higher than the activation associated with either colour or form. In fact, the only regions where the activation associated with colour was higher than the activation associated with texture were primary visual cortex and neighbouring regions in the cuneus. Taken together, the results show that the processing of object form and the processing of surface properties engage anatomically distinct regions in the occipito-temporal cortex. With respect to surface properties, it appears that the visual system can extract the colour of objects relatively early in visual processing whereas information about texture, perhaps because it is more complex, requires the participation of higher-order visual 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 | 0.001 |
| 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".