Response profiles of macaque dorsolateral prefrontal cortex neurons during a rule-guided target selection and sustained attention task
Bibliographic record
Abstract
We investigated the role of primate dorsolateral prefrontal cortex (dlPFC) neurons in target selection and sustained attention by recording single-cell activity within the area in behaving macaque monkeys. The animals were presented with two colored moving random dot patterns (RDPs), the target and the distracter. The target was defined using a color rank selection rule: turquoise > red > blue > green > pink > grey. All colors were approximately isoluminant and randomly assigned to the stimuli. The animals were required to select the target, sustain attention to it, and detect a transient change in its motion direction. We recorded the activity of 222 neurons, of which 147 (66%) showed an increase in activity during task trials relative to baseline. Out of the 147, 68% reliably encoded the position of the target. This latter group was subdivided into three distinct populations: one group encoded target position transiently, starting ∼150ms after color cue onset (21%, ‘selection neurons’), a second group signaled target position in a sustained manner, starting ∼350 ms after cue onset (42%, sustained attention neurons), and the third group combined features of the aforementioned groups (37%). Using ROC (receiver operating characteristic) analysis we found that these neurons effectively discriminated target and distractor through their firing rate as early as ∼150 ms after cue onset. Moreover, immediately following color cue onset, discrimination occurs earlier for greater distance between target and distractor position in the color-rank scale. This finding follows the animals' behavioral performance; proportion of correct discriminations was higher the greater the distance between target and distractor in the color scale. Overall, our results indicate that different populations of dlPFC neurons may be involved in target selection and sustained attention and that the neurometric performance of these units closely follows the one of the animals.
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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) | 0.000 | 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".