Independent processing of object form and surface properties
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 an initial form task, participants made length and width classifications. Participants were unable to ignore length while making width classifications, and were unable to ignore width while making length classifications. This suggests that length and width, which are two cues to object form, share common processing resources. In a subsequent surface-property task, participants made texture and colour classifications. Participants were unable to ignore colour while making texture classifications, and were unable to ignore texture while making colour classifications. This result in turn suggests that texture and colour, which are two characteristics of an object's surface, share common processing resources. Finally, in a combined task, we directly examined possible interactions between the processing of form and the processing of surface properties. In contrast to the findings with the other two tasks, participants were able to ignore form while making surface-property classifications, and to ignore surface properties while making form classifications. These behavioural results converge nicely with neuroimaging studies (Cant et al. VSS 04 & 05) showing that the form of objects and their surface properties are processed by relatively independent neural mechanisms.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".