Engagement of visual fixation suppresses sensory responsiveness and multisensory integration in the primate superior colliculus
Bibliographic record
Abstract
Neurons in the intermediate and deep layers of the superior colliculus (SC) often exhibit sensory-related activity in addition to discharging for saccadic eye movements. These two patterns of activity can combine so that modifications of the sensory response can lead to changes in orienting behaviour. Can behavioural factors, however, influence sensory activity? In this study of rhesus monkeys, we isolate one behavioural factor, the state of visual fixation, and examine its influences on sensory processing and multisensory integration in the primate SC. Two interleaved fixation conditions were used: a FIX condition requiring exogenous fixation of a visible fixation point; and a FIX-BLINK condition, requiring endogenous fixation in the absence of a visible fixation point. Neurons of the SC were influenced by fixation state, exhibiting both lower levels of sensory activity and reduced multisensory interactions when fixation was exogenously engaged on a visible fixation point. These results are consistent with active visual fixation suppressing responses to extraneous stimuli, and thus demonstrate that sensory processing and multisensory responses in the SC are not dependent solely on the physical properties of the sensory environment, but are also dynamically influenced by the behavioural state of the animal.
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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 | 0.000 | 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".