Prefrontal neural activity predicts and mitigates spatial uncertainty in a gaze task
Bibliographic record
Abstract
To predict the future, the brain must integrate past with current sensory information. Research has suggested that prefrontal cortex predicts the timing of events (Fu et al., 2023). Here, we investigated if it also predicts spatial uncertainty. To do this, we recorded neural activity in the frontal (FEF) and supplementary (SEF) eye fields of two rhesus macaques, trained to saccade toward remembered visual targets (T) in presence of a landmark (L) that was surreptitiously shifted to a new position (L’) by a fixed amplitude in one of eight randomized directions arranged circularly around L. Previously, we showed that this results in retrospective shifts in FEF/SEF memory and gaze signals (Bharmauria et al., 2020, 2021). Here, we examined the period from the initial visual response to 300 ms after the landmark shift in 147/68 spatially tuned FEF/SEF neurons for prospective coding of this shift. We used a model-fitting technique to test memory delay coding along a T-T’ continuum. Remarkably, just before the landmark shift, SEF coded a shift toward T’. Since this direction was randomized, we hypothesized that SEF might be ‘guessing’ the direction of the shift. We tested this using a 2D analysis with the real shift (T’) rotated to the right and seven other imaginary shifts circularly arranged. Shortly after the visual response, response fields developed a donut-like prediction in all directions. This did not occur in shuffled controls and could not be accounted for by attraction toward landmark position (TL) or gaze error (TG). Eventually, after the real shift, this predictive ‘donut’ code shifted toward the actual L’. These data suggest that after thousands of training trials, the monkey brain, specifically SEF, created a guessing strategy based on learned probabilities and anticipation. This might allow the brain to optimize behavior and mitigate spatial uncertainty in the surrounding 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 | 0.002 |
| 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".