Attentional modulation of pupillary light responses by microstimulation of the superior colliculus
Bibliographic record
Abstract
Pupil size changes constantly, mainly to regulate the amount of light entering to the retina, with pupil constriction to luminance increases and dilation to luminance decreases. This illumination-dependent pupil modulation has thought to be independent from the top-down influence such as spatial attention. However, it was shown recently that pupil size is smaller when spatial attention is guided to bright, compared to, dark surfaces, demonstrating the attentional modulation on illumination-dependent pupillary responses, although the underlying neural mechanism is yet explored. The superior colliculus (SC) is a midbrain structure causally involved in various components of orienting, including spatial attention. Here, we examined the attentional modulation of illumination-dependent pupillary responses by microstimulation of the SC (~70 Hz, 400 ms, 4 – 30 μA). We hypothesize that microstimulation of a specific location in the SC map will enhance sensory processing at the corresponding region of space (mimicking spatial attention shifts), inducing the illumination-dependent pupillary response (smaller pupil size in bright, compared to dark, surfaces presented in the region). While requiring monkeys to maintain central fixation, we presented bright and dark surfaces in two different locations that matched either the stimulated SC site or a control location in the opposite hemifield. We found that SC microstimulation modulated pupillary light responses in a spatially selective manner, with enhanced illumination-dependent pupillary responses while stimuli presented at the location corresponding to the stimulated SC site. Our results provide direct evidence arguing that the SC is mediating the attentional modulation of pupillary light responses. Meeting abstract presented at VSS 2016
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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 | 0.000 |
| 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".