Fixation-dependent memory for natural scenes: An experimental test of scanpath theory.
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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".