Fixation-dependent memory for natural scenes: An experimental test of scanpath theory.
Why this work is in the frame
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Bibliographic record
Abstract
Many modern theories propose that perceptual information is represented by the sensorimotor activity elicited by the original stimulus. Scanpath theory (Noton & Stark, 1971) predicts that reinstating a sequence of eye fixations will help an observer recognize a previously seen image. However, the only studies to investigate this are correlational ones based on calculating scanpath similarity. We therefore describe a series of 5 experiments that constrain the fixations during encoding or recognition of images in order to manipulate scanpath similarity. Participants encoded a set of images and later had to recognize those that they had seen. They spontaneously selected regions that they had fixated during encoding (Experiment 1), and this was a predictor of recognition accuracy. Yoking the parts of the image available at recognition to the encoded scanpath led to better memory performance than randomly selected image regions (Experiment 2), and this could not be explained by the spatial distribution of locations (Experiment 3). However, there was no recognition advantage for re-viewing one's own fixations versus someone else's (Experiment 4) or for retaining their serial order (Experiment 5). Therefore, although it is beneficial to look at encoded regions, there is no evidence that scanpaths are stored or that scanpath recapitulation is functional in scene memory. This paradigm provides a controlled way of studying the integration of scene content, spatial structure, and oculomotor signals, with consequences for the perception, representation, and retrieval of visual information.
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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 | 0.000 | 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 it