Visual adaptation and novelty responses in the superior colliculus
Bibliographic record
Abstract
The brain's ability to ignore repeating, often redundant, information while enhancing novel information processing is paramount to survival. When stimuli are repeatedly presented, the response of visually sensitive neurons decreases in magnitude, that is, neurons adapt or habituate, although the mechanism is not yet known. We monitored the activity of visual neurons in the superior colliculus (SC) of rhesus monkeys who actively fixated while repeated visual events were presented. We dissociated adaptation from habituation as mechanisms of the response decrement by using a Bayesian model of adaptation, and by employing a paradigm including rare trials that included an oddball stimulus that was either brighter or dimmer. If the mechanism is adaptation, response recovery should be seen only for the brighter stimulus; if the mechanism is habituation, response recovery ('dishabituation') should be seen for both the brighter and dimmer stimuli. We observed a reduction in the magnitude of the initial transient response and an increase in response onset latency with stimulus repetition for all visually responsive neurons in the SC. Response decrement was successfully captured by the adaptation model, which also predicted the effects of presentation rate and rare luminance changes. However, in a subset of neurons with sustained activity in response to visual stimuli, a novelty signal akin to dishabituation was observed late in the visual response profile for both brighter and dimmer stimuli, and was not captured by the model. This suggests that SC neurons integrate both rapidly discounted information about repeating stimuli and novelty information about oddball events, to support efficient selection in a cluttered dynamic world.
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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".