Bibliographic record
Abstract
The extent to which color is used in high-level vision is contentious. Proponents of edge-based theories suggest that objects are recognized mainly based on their shape. On the other hand, surface-plus-edge-based theories support the notion that color and shape both contribute to object recognition. In favor of the latter account, objects that are strongly associated with color (i.e., high color diagnostic objects) are recognized faster when shown in a congruent color than when shown in an incongruent color or in gray-scale (Tanaka & Presnell, 1999). The extent to which this association depends on experience remains uncertain. In the current study, we examined the effects of experience and color on object recognition. In Experiment 1, expert bird watchers and novice participants were asked to categorize common birds at the subordinate level (e.g., "robin"). The bird images were shown in their congruent color, incongruent color and grey-scale. The main finding was that the expert bird watchers were faster to categorize congruent color versions of the bird images than they were to categorize incongruent color and grey-scale versions. In contrast, the novice participants were equally as fast at categorizing congruent color, incongruent color and grey-scale versions of the birds. In Experiment 2, expert bird watchers were asked to categorize congruent color, incongruent color and grey-scale images of birds at the sub-subordinate level (e.g., "nashville warbler"). Bird experts were faster to categorize congruent color versions of the birds compared to incongruent color and grey-scale versions. Collectively, the current findings demonstrate that the fast and accurate recognition of birds by expert bird watchers is facilitated by color information. Thus, perceptual experience can enhance the object representation to include color. Meeting abstract presented at VSS 2013
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".