Visual landmark information is multiplexed with target information in the visual responses of prefrontal gaze centres.
Bibliographic record
Abstract
How does the visual system extract useful information from a rich environment for action? For example, while reaching for a coffee cup, the brain may use several allocentric visual cues (nearby book or computer) to effectively grasp it. These landmarks influence spatial cognition and goal-directed behavior, but how landmark-related visual coding subserves action is poorly understood. To this goal, with gaze system as a model, we recorded 101/312 frontal (FEF) and 43/256 supplementary (SEF) eye field visual responses (in two head-unrestrained monkeys) to the presentation of a target (T, 100 ms), in presence of a visual landmark (L, intersecting lines; presented in one of four diagonal directions/configurations from T). First, using a response field model fitting approach, we confirmed our previous findings (Bharmauria et al. 2020, 2021) that Te (Target relative to initial 3D eye orientation) was the best model for FEF and SEF visual responses at the population level, but some neurons (30% in FEF and 20% in SEF) preferentially coded for landmark. We then specifically tested two mathematical continua (of ten equal steps) to quantify the influence of L on visual response: 1) Target to landmark in eye coordinates (Te-Le) that directly tested the influence of L and Target-in-eye to Target-relative-to landmark (Te-TL) to test the multiplexed influence of T and L. Along both continua, we found a significant influence of the landmark relative to the shuffled control data in most neurons (suggesting both landmark coding and influence of landmark on target coding). Further, the same analysis on separate T-L configurations resulted in a significant shift toward landmark-centered target coding at the population level in FEF, suggesting an influence of landmark coding. These results show that visual landmark influences visual responses in the gaze system, potentially stabilizing future gaze in the presence of noisy 3D eye position signals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 teacher head, 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".