Independent Processing of Form, Colour, and Texture in Object Perception
Bibliographic record
Abstract
Most investigations of object recognition have focused on the form rather than the material properties of objects. Nevertheless, knowledge of the material properties of an object (via its surface cues) can provide important information about that object's identity. In this study, we used Garner's speeded-classification task to explore whether or not the processing of form and the processing of surface properties are independent. In experiment 1, participants made length and width classifications in an initial form task. Participants were unable to ignore length while making width classifications, and were unable to ignore width while making length classifications. This suggests that the perception of length and the perception of width share common processing resources. In a subsequent task, we examined possible interactions between the processing of form and the processing of surface properties. In contrast to the findings with the form task, participants were able to ignore form while making surface-property classifications, and to ignore surface properties while making form classifications. This suggests that the form of objects and their surface properties are processed independently. In experiment 2, we went on to show that the two prominent surface-property dimensions of colour and texture can also be processed independently. In other words, participants were able to ignore colour while making texture classifications, and vice versa. Finally, in experiment 3, we examined the possibility that the stimuli and required responses that we used in experiment 2 were too categorical and thus not optimal for assessing whether or not colour and texture share common processing resources. Using a different stimulus set, participants were again able to ignore colour while making texture classifications, and vice versa. Taken together, these results provided convincing evidence that the separate ventral-stream brain regions identified for form, texture, and colour in a recent neuroimaging study (Cant and Goodale, 2007 Cerebral Cortex 17 713-731) can indeed function independently.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".