Selective representation of task‐relevant objects and locations in the monkey prefrontal cortex
Bibliographic record
Abstract
In the monkey prefrontal cortex (PFC), task context exerts a strong influence on neural activity. We examined different aspects of task context in a temporal search task. On each trial, the monkey (Macaca mulatta) watched a stream of pictures presented to left or right of fixation. The task was to hold fixation until seeing a particular target, and then to make an immediate saccade to it. Sometimes (unilateral task), the attended pictures appeared alone, with a cue at trial onset indicating whether they would be presented to left or right. Sometimes (bilateral task), the attended picture stream (cued side) was accompanied by an irrelevant stream on the opposite side. In two macaques, we recorded responses from a total of 161 cells in the lateral PFC. Many cells (75/161) showed visual responses. Object-selective responses were strongly shaped by task relevance - with stronger responses to targets than to nontargets, failure to discriminate one nontarget from another, and filtering out of information from an irrelevant stimulus stream. Location selectivity occurred rather independently of object selectivity, and independently in visual responses and delay periods between one stimulus and the next. On error trials, PFC activity followed the correct rules of the task, rather than the incorrect overt behaviour. Together, these results suggest a highly programmable system, with responses strongly determined by the rules and requirements of the task performed.
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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 | 0.000 |
| 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".