Features for visual object recognition.
Bibliographic record
Abstract
Human visual object recognition largely relies on shape information. However, the nature of the shape features that actually underlie this task remain largely unknown despite the wealth of competing theories aiming to account for the code by which human vision represents shape. Here, we report a series of five object recognition experiments using a spatial sampling paradigm (cf. Bubbles) to calculate classification images (CIs) that demonstrate the efficient features used by human participants. In all experiments, the targets were behind an occluding mask and partially revealed for 100 ms by a collection of 12 circular gaussian apertures of 0.8° in diameter. Participants pressed a keyboard key to indicate the identity of the target. Response accuracy was maintained at 50% correct by manipulating the degree of degradation of the target image. The experiments essentially differ from one another in terms of the class of stimuli and the exposure of instances from various viewpoints or not. The mean CIs in all experiments constitute a fair representation of those of individual participants. Moreover, when only the significantly effective features from these CIs are visible, this image is easily mapped to the target object by any normal human observer. However, the CIs of human participants correlate poorly with those obtained from an ideal observer carrying out the task under the same conditions. There is no particular type of feature such as those proposed by major shape perception theories (e.g. concavities, convexities, edge intersections, object parts, etc.) that dominate in the mean CIs and feature sizes are quite variable. Overall, these features appear most compatible with the ‘image fragment’ theory proposed by Ullman and collaborators. Remarkably, for all objects that were presented along variable viewpoints, the regions on the object’s surface which constituted the effective features were extremely similar across viewpoints.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.029 |
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".