Quantifying the variance in eye movements while watching intact versus scrambled movies.
Bibliographic record
Abstract
Eye movement (EM) analyses for static images are well defined, but less so for dynamic stimuli. There are limits in applying static measures, such as fixation duration, to dynamic stimuli, that prevent the appropriate characterization of eye movements over space and time. Consequently, moving stimuli are often analyzed using ‘swarm analysis’ or dynamic heat maps to describe EM. Although these allow for qualitative representations of EM, it is difficult to quantify similarities between observers. Previous researchers have used the separate variance in X and Y gaze position to describe EM variability, however, in clinical research, two measures of EM stability combine X and Y gaze position: the bivariate contour ellipse area (BCEA), and the within-isoline area. The BCEA calculates the highest density of eye movements using a Gaussian ellipse that encompasses 68% of the fixation data, thus assuming normality in EM. The within-isoline area does not assume normality. It is based on the probability density estimate of EM, with a level of density chosen (e.g., 68%), so that 68% of the data points have a higher density estimate than this level. Here we apply these metrics to EM generated while watching either an intact version of an emotionally salient movie, or a version where the movie’s scenes were scrambled in sequence. EM were recorded using a 60Hz binocular tracker (Mirametrix S2), with sample gaze positions analyzed in MATLAB using two algorithms. We found that both algorithms show EM are more variable during the scrambled movie in comparison to the intact movie. However, the BCEA shows greater variance in EM within and across both conditions in comparison to the within-isoline area. We attribute this difference due to the assumption of normality in the BCEA calculation, and recommend the within-isoline area when describing group variance of EM in dynamic stimuli. Meeting abstract presented at VSS 2015
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".