Detecting patterns of covert attention shifts in psychophysical tasks using microsaccades
Bibliographic record
Abstract
When a person's gaze is fixed, his attention can shift covertly about the visual field. In a recent paper (Hafed and Clark, 2002), we proposed a direct measure of covert attention, one based on the detection and analysis of microsaccades. Here we show how this measure can allow us to uncover new patterns of covert attention shifts that are unobservable using current approaches. We ran a simple task in which trials consisted of the onset of a peripheral stimulus 17 deg to the right or left of fixation followed by a foveal stimulus. All stimuli were 2.5 deg in size, and their colors were red, yellow, green, or blue. The stimulus onset asynchrony between the peripheral and foveal stimuli was randomly chosen from among 0, 50, 100, 150, & 500 ms. Subjects were instructed to maintain fixation and to make speeded same/different judgments on the colors of the peripheral and foveal stimuli. Eye movements were monitored, and microsaccade detection and analysis were as in (Hafed & Clark, 2002). We observed the occurrence of three epochs of ‘microsaccadic responses’ (and therefore of ‘attention shifts’) in our task: one related to peripheral events, one related to foveal events, and one related to response execution. In particular, microsaccade analysis revealed attention shifts to the peripheral stimulus and back after this stimulus' onset, followed by attention shifts to the peripheral stimulus and back after the foveal stimulus' onset, followed finally by an attention shift to the peripheral stimulus that was tightly synchronized with manual response execution. This final shift is hard to uncover using current approaches. We conclude that the accessibility to covert attention shifts that microsaccade analysis allows has tremendous implications on the study of how humans employ covert attention when interacting with their visual environment. HafedZ. M.ClarkJ. J.Microsaccades as an overt measure of covert attention shifts. Vision Research, Vol. 42, 2533–2545.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".