Prefrontal and anterior cingulate cortex neurons encode attentional targets even when they do not apparently bias behavior
Bibliographic record
Abstract
Neurons in anterior cingulate and prefrontal cortex (ACC/PFC) carry information about behaviorally relevant target stimuli. This information is believed to affect behavior by exerting a top-down attentional bias on stimulus selection. However, attention information may not necessarily be a biasing signal but could be a corollary signal that is not directly related to ongoing behavioral success, or it could reflect the monitoring of targets similar to an eligibility trace useful for later attentional adjustment. To test this suggestion we quantified how attention information relates to behavioral success in neurons recorded in multiple subfields in macaque ACC/PFC during a cued attention task. We found that attention cues activated three separable neuronal groups that encoded spatial attention information but were differently linked to behavioral success. A first group encoded attention targets on correct and error trials. This group spread across ACC/PFC and represented targets transiently after cue onset, irrespective of behavior. A second group encoded attention targets on correct trials only, closely predicting behavior. These neurons were not only prevalent in lateral prefrontal but also in anterior cingulate cortex. A third group encoded target locations only on error trials. This group was evident in ACC and PFC and was activated in error trials "as if" attention was shifted to the target location but without evidence for such behavior. These results show that only a portion of neuronaly available information about attention targets biases behavior. We speculate that additionally a unique neural subnetwork encodes counterfactual attention information.
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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) | 0.001 | 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".