Eye Movement Patterns Suggest Different Facial Features are Most Informative at Different Spatial Frequencies
Bibliographic record
Abstract
While many studies have shown that a middle band of spatial frequencies (SF) is most useful for face recognition, others have pointed out that the most informative SF ranges vary depending on location on the face. In two experiments, we examined variations in the utility of different SFs across the face by measuring eye movements during an old/new recognition task using spatially filtered faces. Eye movements were recorded using the Eyelink II (SR-Research.com). In Experiment 1, we measured 32 subjects' eye movements during the learning phase of the old/new task; In Experiment 2 we examined 15 subjects' eye movements during the retrieval phase of the same task. Stimuli were 32 faces filtered to preserve 11 SF bands across the spectrum (bandwidth 2 octaves), plus an unfiltered baseline condition. Twelve areas of interest (AOIs) were defined for each face, and total fixation time was analyzed across AOI and SF. Results show that low SFs elicited more fixations on medial AOIs such as nose, forehead and chin. This may indicate a tendency towards holistic processing, whereby fixation on these features represents an attempt to take in the entire face. In contrast, high SFs elicited more fixations on inner features, such as eyes and mouth, suggesting greater featural processing. Analysis of gaze transitions across AOIs show that fixation patterns vary across SF. Specifically, subjects transition more between the inner features, and exhibit more transitions in general, when examining high SF faces. When looking at low SF faces, transitions tend to be few, and to stay within medial features such as nose, nasion and forehead. Our results are compatible with previous work suggesting that the useful SFs vary by face part. We also find evidence suggesting that low and high SFs respectively support holistic and featural processing. Meeting abstract presented at VSS 2013
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".