Bilateral Field Advantage of Spatial Attention in Macaque Lateral Prefrontal Cortex
Bibliographic record
Abstract
Allocating visual attention to behaviorally relevant stimuli is easier when distractors are in the opposite visual hemifield relative to when they are in the same hemifield. The neural mechanisms underlying this bilateral field advantage remains unclear. We documented this effect in two macaques performing a covert spatial attention task in two different conditions: when the target and distracter were positioned in different hemifields (across condition), and when they were positioned on the top and bottom quadrants within the same visual hemifield (within condition). The animals' behavioral performance at detecting a change in the attended stimulus was higher in the across relative to the within condition. We recorded the responses of lateral prefrontal cortex (LPFC, area 8A) neurons in one animal. The proportion of LPFC neurons encoding the allocation of attention was larger in the across relative to the within condition. The latter was accompanied by an increase in the ability of single neurons to discriminate the allocation of attention in the across relative to the within condition. Finally, we used linear classifiers to decode the allocation of attention from the activity of neuronal ensembles and found a similar bilateral field advantage in decoding performance in the across relative to the within condition that generalized to different integration time windows and number of neurons used by the classifier. Our finding provides a neural correlate of the bilateral field advantage reported in behavioral studies of attention and suggest a role of the LPFC circuitry in its origin.
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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 | 0.001 |
| 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".