Selective scanpath repetition during memory-guided visual search
Bibliographic record
Abstract
Visual search efficiency improves with repetition of a search display, yet the mechanisms behind these processing gains remain unclear. According to Scanpath Theory, memory retrieval is mediated by repetition of the pattern of eye movements or "scanpath" elicited during stimulus encoding. Using this framework, we tested the prediction that scanpath recapitulation reflects relational memory guidance during repeated search events. Younger and older subjects were instructed to find changing targets within flickering naturalistic scenes. Search efficiency (search time, number of fixations, fixation duration) and scanpath similarity (repetition) were compared across age groups for novel (V1) and repeated (V2) search events. Younger adults outperformed older adults on all efficiency measures at both V1 and V2, while the search time benefit for repeated viewing (V1-V2) did not differ by age. Fixation-binned scanpath similarity analyses revealed repetition of initial and final (but not middle) V1 fixations at V2, with older adults repeating more initial V1 fixations than young adults. In young adults only, early scanpath similarity correlated negatively with search time at test, indicating increased efficiency, whereas the similarity of V2 fixations to middle V1 fixations predicted poor search performance. We conclude that scanpath compression mediates increased search efficiency by selectively recapitulating encoding fixations that provide goal-relevant input. Extending Scanpath Theory, results suggest that scanpath repetition varies as a function of time and memory integrity.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".