Optimizing Naturalistic Object Categorization with Diagnostic Low-Level Visual Information
Bibliographic record
Abstract
How do we tell that one bird on a tree is a sparrow and the other is a warbler? Humans recognize visual objects by processing a hierarchy of low- to high-level visual information, but the involvement of low-level information in object categorization remains to be explored. Unlike higher-level information (e.g., shapes and textures), low-level information (e.g., spatial orientations and frequencies) diagnostic of object categories can be challenging to capture in naturalistic images. Here, we aimed to leverage category-diagnostic low-level visual information to optimize human category learning – particularly the learning of unfamiliar bird categories. Specifically, we used a variant of the Spatial Envelope model to represent naturalistic bird images as sets of low-level features based on oriented Gabor filters. Then, we trained a classifier to categorize these low-level bird representations. From the trained classifier, we obtained weights of the low-level features to create weighted masks that selectively added noise to the diagnostic or non-diagnostic low-level information in each image. Subsequently, we randomly assigned 48 participants to learn to categorize the bird images with masked diagnostic or masked non-diagnostic information. Compared to the masking of diagnostic information, the masking of non-diagnostic information resulted in a steeper learning slope and a greater speed-up in reaction time for correct learning trials. When participants categorized novel bird images after learning, the masking of non-diagnostic information led to faster responses in correct trials relative to the masking of diagnostic information. In conclusion, our findings revealed that low-level visual information defining specific categories can be extracted from naturalistic object images. Furthermore, we demonstrated that diagnostic low-level information can be leveraged to optimize learning of naturalistic object categories and support generalization to novel objects from those categories.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".