Distinct roles of eye movements during memory encoding and retrieval
Bibliographic record
Abstract
Eye movements help to facilitate memory for complex visual scenes. Here we ask whether the benefit of eye movements for memory is stronger during the encoding phase or the recognition phase. 20 participants viewed photographs of real-world scenes, followed by a new-old memory task. They were either allowed to freely explore the scenes via eye movements in both the study and test phases (Condition S+T+), or had to refrain from making eye movements in either the test phase (Condition S+T-), the study phase (Condition S-T+), or both (Condition S-T-). Recognition accuracy (d-prime) was significantly higher when participants were able to move their eyes (Condition S+T+: 1.16) than when they were constrained in some way (Condition S+T-: 0.67, Condition S-T+: 0.42, Condition S-T-: 0.35, p < 10-6). A separate analysis on Hit rates and False Alarm rates indicates a dissociation between the effects of eye movements on memory during the study and test phases. The Hit Rate was greatly influenced by the ability to make eye movements during the study phase, while the False Alarm rate was affected by whether participants could make eye movements during the test phase. Taken together, these results suggest that eye movements during the first viewing of a scene, used to visually explore and encode the scene, are critical for accurate subsequent memory. Eye movements during the test phase, on the other hand, are used to re-explore the scene and to confirm or deny recognition. Thus, eye movements during both the first encounter and subsequent encounters with a scene are beneficial for recognition through proper exploration, encoding, and re-exploration of the visual environment. Meeting abstract presented at VSS 2016
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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 | 0.000 | 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".