Bibliographic record
Abstract
Unlike reading or scene perception, the utility of eye tracking for purposes of studying face processes is limited given that recognition typically occurs within one or two saccades. However, if processing time is extended for a longer duration, eye tracking can be useful for uncovering the strategies mediating face discrimination decisions. To achieve this, we employed a change detection paradigm where two faces were continuously presented with an intervening noise mask until the participant made a “same” or “different” response. The faces were either identical or differed in their featural or configural properties and shown in their upright and inverted orientations. Featural differences were either in the size of the eyes or the mouth. Configural differences were either in the horizontal distance between the eyes or vertical distance between nose and mouth. Eye movements were analyzed in terms of the location of the first fixation, location of last fixation and location of aggregate fixations. Initial fixations to upright face were predominantly directed to the eye area whereas first fixations to inverted face were equally distributed to eyes and the nose area. In terms of accuracy, inversion differentially impaired the detection of featural and configural changes in mouth region than the eye region even though more and longer eye fixations were allocated to this area. Overall, the nose was attended to more on configural change trials than on featural change trails regardless of the orientation. The analysis of last fixations revealed that changes were more likely to be detected if the last fixation was located in the region where the change occurred indicating that the eye movement behavior was predictive of change detection performance. In short, this study showed that face strategies are accurately reflected in eye movement behaviors when the task is self-paced and requires additional processing time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".