Are eye movements beneficial for memory retrieval?
Bibliographic record
Abstract
When remembering an object at a given location, observers tend to return their gaze to this location even after the object has disappeared, known as the Looking-at-Nothing (LAN) phenomenon. However, it is unclear whether the LAN phenomenon is associated with better memory performance. Previous studies reporting beneficial effects of LAN have not systematically assessed eye movements. Here we related memory performance to eye movements during memory retrieval— saccades in a free-viewing condition and microsaccades in a fixation condition. In each trial, observers (n=19) had to remember eight images of objects shown for 5 seconds, during which observers could freely move their eyes. Object pairs were shown at 12, 3, 6 and 9 o’clock at a distance of 6 degrees from screen center. At the end of each trial, observers indicated by button press whether an auditory statement about an object’s location (e.g., “Pineapple up”) was correct, incorrect, or whether the prompted object had not been shown. Results show similar memory accuracy in free-viewing and fixation conditions (85% vs. 88%). Our eye movement analysis revealed that in only 62% of free-viewing trials observers made saccades. Yet, memory accuracy did not differ between free-viewing trials in which observers did or did not move their eyes (82% vs. 89%). Similarly, in the fixation condition we did not find a benefit of micro-saccades (88% memory accuracy with vs. 89% without microsaccades). The LAN phenomenon was observed in free-viewing trials in which observers made saccades, and performance tended to be superior in those trials. However, given the global lack of memory performance differences between eye movement conditions we conclude that eye movements are not necessary for accurate memory retrieval. These results may explain why previous literature shows contradictory findings on beneficial effects of LAN.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".