Activity in the superior colliculus reflects dynamic interactions between voluntary and involuntary influences on orienting behaviour
Bibliographic record
Abstract
Performance in a behavioural task can be influenced by both bottom-up and top-down processes such as stimulus modality and prior probability. Here, we exploited differences in behavioural strategy to explore the role of the intermediate and deep layers of the superior colliculus (dSC) in covert orienting. Two monkeys were trained on a predictive cued-saccade task in which the cue predicted the target's upcoming location with 80% validity. When the delay between cue and target onset was 250 ms, both monkeys showed faster responses to the uncued (Invalid) location. This was associated with a reduced target-aligned response in the dSC on Valid trials for both monkeys and is consistent with a bottom-up (i.e. involuntary) bias. When the delay was increased to 650 ms, one monkey continued to show faster responses to the Invalid location whereas the other monkey showed faster responses to the Valid location, consistent with a top-down (i.e. voluntary) bias. This latter behaviour was correlated with an increase in activity in dSC neurons preceding target onset that was absent in the other monkey. Thus, using the information provided by the cue shifted the emphasis towards top-down processing, while ignoring this information allowed bottom-up processing to continue to dominate. Regardless of the selected strategy, however, neurons in the dSC consistently reflected the current bias between the two processes, emphasizing its role in both the bottom-up and top-down control of orienting behaviour.
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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) | 0.000 | 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".