Eccentricity effects in the rapid visual encoding of natural images
Bibliographic record
Abstract
Purpose: In studies of natural image perception, eccentricity effects may arise from (1) a decline in visual performance as a function of retinal eccentricity (observer effect), and/or (2) a decline in the detail or salience of the image content as a function of image eccentricity (framing effect). Here we assess the role of both factors in the rapid visual coding of natural images. Method: A local recognition task was employed. Each trial sequence consisted of a fixation, test, mask and probe stimulus. The test and mask stimuli were randomly-selected 31×31 deg natural images, displayed for 59 and 506 ms, respectively. A black grid divided each of the images into 64 3.8 deg square blocks. The probe stimulus consisted of two blocks presented on either side of fixation, one drawn randomly from the test image, the other from a random image. The task was to identify which of the blocks was drawn from the test image. In order to independently assess the influence of observer and framing effects, the test stimuli were cropped from both central and non-central locations of larger natural images. Results: Recognition performance for coherent natural images was found to decline significantly with eccentricity. However, this effect disappeared completely when the 64 blocks of the test image were scrambled. Multiple regression analysis revealed that both retinal and image eccentricity were significant factors, although the magnitude of the observer effect was roughly twice that of the framing effect. Discussion: Both spatial acuity and chromatic sensitivity decline with eccentricity. However, the absence of an eccentricity effect for scrambled images argues against these being the primary factors. Rather, the observer effect appears to be due to a higher-level facilitation in foveal processing triggered by the coherent structure of the image.
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 | 0.001 | 0.005 |
| 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 | 0.000 | 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".