Category-specific guidance of gaze in photographs and line drawings
Bibliographic record
Abstract
Our group has previously shown that scene content can be predicted from eye movements observers make when viewing colour photographs. The time course of category predictions reveals differential contributions of bottom-up and top-down processes at different viewing times. Here, we use these known differences in order to determine when and to what extent image features at different representational levels contribute toward guiding gaze in a content-specific manner. 77 participants viewed grayscale photographs and line drawings of real-world scenes. In a leave-one-subject-out cross validation analysis, scene categories were predicted from gaze patterns over a 2-second time course. Scene categories could be predicted from gaze at all times in both photographs (average accuracy = 31.4%, chance = 16.7%, p < 0.0001) and line drawings (30.0%, p < 0.0001). We also replicate the time course, with an initial steep decrease in prediction accuracy from 300ms to 500ms, representing the contribution of bottom-up information, followed by a steady increase, representing top-down knowledge of category-specific information. Using DeepGaze II as the leading model of salience, we reconfirm a strong early contribution of bottom-up effects in grayscale photographs. We computed the low-level (luminance contrasts and orientation statistics) and mid-level features (local symmetry and contour junctions) from the images in order assess their differential contributions to content-specific guidance of gaze. For photographs, we find qualitatively similar contributions of these representational levels, contributing mostly to the initial bottom-up peak. For line drawings of the same scenes, we observe that mid-level features that describe scene structure (symmetry and junctions) play a more prominent role in the top-down guidance of gaze. Thus, we show that bottom-up information contributes less to gaze behaviour for line drawings than for photographs, and that structural features increasingly guide category-specific gaze when images are reduced to line drawings. Meeting abstract presented at VSS 2018
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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 | 0.000 |
| 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".