Learning to blink strategically is crucial to performance in a predictable saccade task and varies across the lifespan
Bibliographic record
Abstract
Abstract Humans blink their eyes 16-20 times each minute to spread tear film on the cornea, representing a substantial amount of waking time when one’s eyes are closed. These spontaneous blinks are strategically timed to prioritize the processing of important visual input, balancing both stimulus characteristics and personal goals. Until now, the learning process underlying optimal blink timing has not been investigated in detail. Here, we present video-based eye-tracking data from 703 healthy participants (aged 5-91 years, 470 female) performing a structured interleaved pro-/anti-saccade task, in which we previously found that blink suppression occurs in anticipation of visual stimulus appearance (Pitigoi et al., 2024). Our goals are to understand (1) how participants modify their blink timing according to the temporal contingencies of the task; (2) whether the capacity to optimize blink timing impacts performance; and (3) whether this pattern varies with age. We found that participants quickly and strategically modified their blink distribution to optimize task performance. Blink probability decreased in periods that would compromise anti-saccade execution and increased when visual input was less critical. We also found significant differences in blink patterns and adaptive ability based on cognitive control capacity (indicated by participants’ anti-saccade error rates). Furthermore, we demonstrated that blink optimization improves gradually from childhood to early adulthood, before declining with advanced age. This supports a possible link between regulation of blink behavior and age-related changes in learning capacity and inhibitory control across the lifespan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".