Pupillometric imaging reveals the spatiotemporal dynamics of covert attention
Bibliographic record
Abstract
The visual system uses attention to process relevant aspects in the environment. Particularly covert attention plays an important role as it allows us to inspect information presented in the visual periphery before or without an eye movement. However, due to its latent and dynamic nature, it has been a challenge to characterize the spatial and temporal properties of covert attentional shifts. To date little is known about how the focus of attention moves across space and time. We developed a novel pupillometric imaging paradigm to directly probe and visualize spatiotemporal shifts of attention in observers that performed a classic Posner’s cueing task. The distribution of attentional resources was measured by proxy of the amplitude of pupil orienting responses to salient probes that sampled various positions and timepoints around cue and target onsets. The resulting attention maps confirm that the analogy of attention as a local spotlight holds when stationary. The analysis of its temporal dynamics indicate that the attentional spotlight, when shifting between peripheral locations, gradually fades out at start positions and fades in at end positions across time. When shifting from foveal to peripheral locations, the degree of attention only decreases at its start position (i.e., fixation), resulting in relatively more attention at start and end positions before and after a shift, respectively. As the first two-dimensional imaging effort of covert attention shifts across peripheral locations, this study lays the foundation to characterize attentional properties at an unprecedentedly high spatiotemporal resolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".