A perceptual field test in object experts using gaze-contingent eye tracking
Bibliographic record
Abstract
A hallmark of expert object recognition is rapid and accurate subordinate-category recognition of visually homogenous objects. However, the perceptual strategies by which expert recognition is achieved is less known. The current study investigated whether visual expertise changes observers' perceptual field (e.g., their ability to use information away from fixation for recognition) for objects in their domain of expertise, using a gaze-contingent eye-tracking paradigm. In the current study, bird experts and novices were presented with two bird images sequentially, and their task was to determine whether the two images were of the same species (e.g., two different song sparrows) or different species (e.g., song sparrow and chipping sparrow). The first study bird image was presented in full view. The second test bird image was presented fully visible (full-view), restricted to a circular window centered on gaze position (central-view), or restricted to image regions beyond a circular mask centered on gaze position (peripheral-view). While experts and novices did not differ in their eye-movement behavior, experts' performance on the discrimination task for the fastest responses was less impaired than novices in the peripheral-view condition. Thus, the experts used peripheral information to a greater extent than novices, indicating that the experts have a wider perceptual field to support their speeded subordinate 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".