Recurrence quantification analysis of eye movements during mental imagery
Bibliographic record
Abstract
Several studies demonstrated similarities of eye fixations during mental imagery and visual perception but-to our knowledge-the temporal characteristics of eye movements during imagery have not yet been considered in detail. To fill this gap, the same data is analyzed with conventional spatial techniques such as analysis of areas of interest (AOI), ScanMatch, and MultiMatch and with recurrence quantification analysis (RQA), a new way of analyzing gaze data by tracking re-fixations and their temporal dynamics. Participants viewed and afterwards imagined three different kinds of pictures (art, faces, and landscapes) while their eye movements were recorded. While fixation locations during imagery were related to those during perception, participants returned more often to areas they had previously looked at during imagery and their scan paths were more clustered and more repetitive when compared to visual perception. Furthermore, refixations of the same area occurred sooner after initial fixation during mental imagery. The results highlight not only content-driven spatial similarities between imagery and perception but also shed light on the processes of mental imagery maintenance and interindividual differences in these processes.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 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".