The role of local and holistic processes in the perceptual organization of object shape
Bibliographic record
Abstract
Perceptual grouping is the problem of determining what features go together and in what configuration. Since this is a computationally hard problem, it is important to ask whether object perception really depends on perceptual grouping. For example, under ideal conditions, a collection of local features may be sufficient to classify an object. These features could be computed via a feedforward process, obviating the need for perceptual grouping. Indeed, this fast feedforward `bag of features’ conception of object processing is prevalent in both human and computer vision research. Here I will review psychophysical and computational research that challenges the ability of this class of model to explain object perception. Psychophysical assessment shows that humans are largely unable to pool local shape features to make object judgements unless these features are configured holistically. Further, the formation of these perceptual groups is itself found to rely on holistic shape representations, pointing to a recurrent circuit that conditions local grouping computations on this holistic encoding. While feedforward deep learning models for object classification are more powerful than earlier bag-of-feature models, we find that these models also fail to capture human sensitivity to holistic shape and perceptual robustness to occlusion. This leads to the hypothesis that a computational model designed to solve perceptual grouping tasks as well as object classification will form a better account of human object perception, and I will highlight how optimal solutions to these grouping tasks are typically based on a fusion of feedforward local computations with holistic optimization and feedback.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".