Bibliographic record
Abstract
Research on the contribution of different spatial bands to object recognition has been prolific, but there has been relatively little work examining how spatial factors and level of categorization task interact. In this study we examined the effects of spatial frequency filtering on object categorization at the Basic, Subordinate and Superordinate levels. In each trial, subjects were shown first a word at either the Basic level (e.g., Dog, Car, Boat, etc.), the Subordinate level (e.g., Collie, Limousine, Sailboat, etc.), or the Superordinate level (e.g., Animal, Vehicle) and then a picture of an object. Their task was to indicate if the pictured object matched the word. The picture could be either low-passed (50% cutoff at 8.0 cycles/image width), high-passed (50% cutoff at 16.0 cycles/image width) or full-bandwidth. The design was blocked along the spatial frequency and category level dimensions; trials were otherwise completely randomized. Reaction time and error rates were assessed. Our results show that while both superordinate and subordinate classifications are adversely affected by spatial filtering, basic-level categorization is robust to this manipulation. Subordinate classification proved especially vulnerable to low-passing. These findings are in agreement with previous studies suggesting that basic-level representations are robust to changes in image information (rotation, scrambling, etc.). These results also suggest that differences in the level of categorization task may explain in part differences in findings regarding which spatial bands are most effective for object recognition.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".