Effects of development on low-level feature processing during natural viewing of dynamic scenes
Bibliographic record
Abstract
Eye movements have been widely used to examine many aspects of brain functions, such as reflexive response, inhibitory controls, and working memory, in normal development. However, it is unclear how normal development affects eye movements of natural viewing behavior. This study specifically examined the developmental trajectory of low-level features processing while participants freely viewed videos of natural scenes. These videos are composed of short (2–4 seconds), unrelated clips. This design was to reduce top-down expectation and to magnify the difference in gaze allocation at every scene change. Gazes of 3 groups of participants (18 children, 10.7 ± 1.8 yr; 18 young adults, 23.2 ± 2.6 yr; 24 elderly, 70.3 ± 7.5 yr) were tracked while they watched the videos for 20 minutes. First, we used a computational saliency model (Itti & Koch, 2001) to compute bottom-up saliency maps for each video frame. These saliency maps can be computed from a single feature (e.g. color contrast, motion contrast) or a combination of them. Next, we computed the correlation between salience and gaze of each population. To reveal the developmental trajectory of low-level features processing, classifiers were built to differentiate (1) children vs. young adults, and (2) young adults vs. elderly. In the mean time, a feature selection method was performed to identify the most discriminative features for differentiating the populations. Using this method, we found that during normal maturation (children to young adults), there was a reduction in saccade interval and an increase in correlation between gaze and texture contrast, orientated edges, and color contrast. On the other hand, during normal aging (young adults to elderly), we found an increase in saccade interval and a decrease in correlation between gaze and oriented edges. In conclusion, this study revealed for the first time the differences between age groups in low-level feature processing during natural viewing of dynamic scenes.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".