The role of spatial frequency in expert object recognition.
Bibliographic record
Abstract
Novices recognize objects at the basic-category level (e.g., dog, chair, and bird) at which identification is based on the global form of the objects (Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976). In contrast, experts recognize objects within their domain of expertise at the subordinate level (e.g., Sparrow or Finch) for which the internal object information may play an important role in identification (Tanaka & Taylor, 1991). To investigate whether expert recognition relies on internal object information, we band-pass filtered bird images over a range of spatial frequencies (SF) and then masked the filtered image to preserve its global form. In Experiment 1, bird experts categorized common birds at the family level (e.g., Robin or Sparrow) more quickly and more accurately than novices. Both experts and novices were more accurate when bird images contained the internal information represented by a middle range of SFs, and this finding was characterized by a quadratic function in which accuracy decreased toward each end of the SF spectrum. However, the experts, but not the novices, showed a similar quadratic relationship between response times and SF range. In Experiment 2, experts categorized Warblers and Finches at the more specific, species level (e.g., Wilson's Warbler or House Finch). Recognition was again fastest and most accurate for images filtered in the middle range of SFs. Collectively, these results indicate that a midrange of SFs contain crucial information for subordinate recognition, and that extensive perceptual experience can influence the efficiency with which this information is utilized. (PsycINFO Database Record
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".