Comparison of superior colliculus and primary visual cortex in the coding of visual saliency
Bibliographic record
Abstract
The superior colliculus (SC) is a phylogenetically ancient midbrain structure with purely visual representations in the superficial-layers (SCs), and sensorimotor representations linked to the control of eye movements/attention in the intermediate-layers (SCi). In primitive species, SC played a central role in vision and orienting independent of cortex. Through mammalian evolution, primary visual cortex (V1) introduced more specialized feature processing, but also became a dominant source of input for SCs. We quantified the relative roles of these early visual areas in the coding of higher-order stimuli that give rise to saliency in complex scenes. Rhesus monkeys viewed a wide-field arrangement of stimuli (210 radially-arranged items spanning ~40-50deg) extending beyond the classic receptive field (RF). The stimuli were oriented color bars (~0.4x1.2deg) that formed a perceptual "pop-out" array the monkeys had to ignore; i.e., reward was contingent upon gaze directed to a separate achromatic stimulus that always stepped orthogonal to the pop-out singleton. We compared visually-evoked responses when the goal-irrelevant pop-out singleton fell in versus opposite the RF, and compared it to a single-item control condition. First, visual onset latency was reliably earlier, and RFs 5-10 fold smaller, in V1 than SCs (or SCi). This is consistent with the idea that each SCs neuron might integrate the outputs of multiple V1 neurons. Second, surround suppression evoked by the wide-field array was dramatically stronger in SCs (and SCi) than V1. This indicates the prominence of long-range interactions in SC, an essential component of the saliency-map hypothesis. Lastly, only SCs neurons showed a reliable preference for the goal-irrelevant pop-out singleton; in SCi stimuli were heavily suppressed unless made goal-relevant. From these observations, we propose that V1 fits the role of a local feature processer, SCs a bottom-up saliency map, and SCi a priority map shaped by a combined representation of bottom-up saliency and top-down relevancy. Meeting abstract presented at VSS 2014
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".