Microsaccade direction reflects the economic value of potential saccade goals and predicts saccade choice
Bibliographic record
Abstract
Microsaccades are small-amplitude (typically <1°), ballistic eye movements that occur when attempting to fixate gaze. Initially thought to be generated randomly, it has recently been established that microsaccades are influenced by sensory stimuli, attentional processes, and certain cognitive states. Whether decision processes influence microsaccades, however, is unknown. Here, we adapted two classic economic tasks to examine whether microsaccades reflect evolving saccade decisions. Volitional saccade choices of monkey and human subjects provided a measure of the subjective value of targets. Importantly, analyses occurred during a period of complete darkness to minimize the known influence of sensory and attentional processes on microsaccades. As the time of saccadic choice approached, microsaccade direction became the following: 1) biased toward targets as a function of their subjective value and 2) predictive of upcoming, voluntary choice. Our results indicate that microsaccade direction is influenced by and is a reliable tell of evolving saccade decisions. Our results are consistent with dynamic decision processes within the midbrain superior colliculus; that is, microsaccade direction is influenced by the transition of activity toward caudal saccade regions associated with high saccade value and/or future saccade choice.
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".