A Dissociation Between Visual Strategy Use and Accuracy after Perceptual Expertise Training
Bibliographic record
Abstract
A perceptual expert is skilled at observing, identifying, and distinguishing between items within their domain of expertise. Previous research examining perceptual expertise with birds (Scott et al., 2006) and cars (Scott et al., 2008) suggests that subordinate-level training improves perceptual discrimination over basic-level training. However, it was previously unclear whether changes in accuracy were accompanied by changes in visual strategy use. To answer this question, adults (n= 32) received 9 hours of training with 2 families of computer-generated objects over a 2-3 week period. Each family included 10 unique species (labeled "A" through "J") each containing 12 exemplars. Within subjects, one family was trained at the subordinate level and the other family was trained at the basic-level. Stimulus features, including color and spatial frequency, were also manipulated to assess the impact of these factors on posttest discrimination. Pre- and posttest assessments included eye-tracking and accuracy (d') during a serial image discrimination task. Consistent with previous reports (Scott et al., 2006; 2008), accuracy (d') increased from pretest to posttest for the subordinate trained family but not for the basic trained family (See Figure 1, top left). Eye-tracking results suggest that although training did not change overall dwell time, the average fixation duration increased and the number of fixations decreased from pretest to posttest (Figure 1). These changes in visual strategies were unrelated to the level of training and the image manipulations did not impact these results. Improvements in perceptual discrimination replicate previous expertise training results. Although behavior is differentially impacted by subordinate versus basic level training, the eye tracking analyses suggest that changes in visual strategies do not differ based on level of training. Meeting abstract presented at VSS 2017
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".