Spatial-Frequency Thresholds for Object Categorisation at Basic and Subordinate Levels
Why this work is in the frame
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Bibliographic record
Abstract
In an attempt to understand how low-level visual information contributes to object categorisation, previous studies have examined the effects of spatially filtering images on object recognition at different levels of abstraction. Here, the quantitative thresholds for object categorisation at the basic and subordinate levels are determined by using a combination of the method of adjustment and a match-to-sample method. Participants were asked to adjust the cut-off of either a low-pass or high-pass filter applied to a target image until they reached the threshold at which they could match the target image to one of six simultaneously presented category names. This allowed more quantitative analysis of the spatial frequencies necessary for recognition than previous studies. Results indicate that a more central range of low spatial frequencies is necessary for subordinate categorisation than basic, though the difference is small, at about 0.25 octaves. Conversely, there was no effect of categorisation level on high-pass thresholds.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it