Inhibition of eye blinking reveals subjective perceptions of stimulus salience
Bibliographic record
Abstract
Spontaneous eye blinking serves a critical physiological function, but it also interrupts incoming visual information. This tradeoff suggests that the inhibition of eye blinks might constitute an adaptive reaction to minimize the loss of visual information, particularly information that a viewer perceives to be important. To test this hypothesis, we examined whether the timing of blink inhibition, during natural viewing, is modulated between as well as within tasks, and also whether the timing of blink inhibition varies as a function of viewer engagement and stimulus event type. While viewing video scenes, we measured the timing of blinks and blink inhibition, as well as visual scanning, in a group of typical two-year-olds, and in a group of two-year-olds known for attenuated reactivity to affective stimuli: toddlers with Autism Spectrum Disorders (ASD). Although both groups dynamically adjusted the timing of their blink inhibition at levels greater than expected by chance, they inhibited their blinking and shifted visual fixation differentially with respect to salient onscreen events. Moreover, typical toddlers inhibited their blinking earlier than toddlers with ASD, indicating active anticipation of the unfolding of those events. These findings indicate that measures of blink inhibition can serve as temporally precise markers of perceived stimulus salience and are useful quantifiers of atypical processing of social affective signals in toddlers with ASD.
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 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".