The role of spatial frequencies in expert object recognition
Bibliographic record
Abstract
Objects are typically recognized at the basic level (e.g., bird) in which the external contour shape of the object is important for recognition. Experts, on the other hand, typically recognize objects at the subordinate level (e.g., sparrow), which seem to depend more on internal features of objects. Here we investigated whether bird experts rely on internal object features to facilitate fast and accurate subordinate-level recognition of objects in their domain of expertise. We filtered bird images over a range of spatial frequencies corresponding to 2-4 cycles per image (cpi), 4-8 cpi, 8-16 cpi, 16-32 cpi, and 32-64 cpi. This manipulation preserved the external shape of the object while systematically degrading its internal feature information. In Experiment 1, bird experts and novices categorized common birds at the subordinate, family-level (e.g., robin, sparrow, cardinal). The main finding was that experts were faster and more accurate than novices. Moreover, experts were fastest with images filtered between 8 cpi and 32 cpi in which external and internal information were both preserved. In Experiment 2, experts categorized birds at the subordinate, species level (e.g., Wilson's warbler, Tennessee warbler), in which external shape is less diagnostic and internal features are more important to identification. For species-level categorizations, experts were faster at recognizing birds filtered between 4 cpi and 32 cpi relative to images filtered at 2-4 cpi. Response time distribution analyses revealed that only images filtered at 8-16 cpi led to a reaction time advantage in the fastest trials. In summary, bird experts form elaborate object representations that include information about the internal features of birds allowing them to make fast and accurate subordinate-level recognition for objects in their domain of expertise. Meeting abstract presented at VSS 2014
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".