Neural activity in monkey prefrontal cortex during delayed-match-to-sample and conditional pro-saccade - anti-saccade tasks
Bibliographic record
Abstract
A number of previous studies have investigated the response properties of prefrontal (PFC) neurons during conditional visuomotor tasks in which a monkey is required to perform one response (for example, a rightward saccade) following presentation of one stimulus, and a different response following presentation of another (for example, a leftward saccade). These tasks typically require the animal to perform the same general behaviour (a saccade), but modify the direction of their response depending upon the behavioural rule indicated by the stimulus. Real-world situations, however, often require that an individual be able to flexibly choose between different behavioural alternatives depending upon the contingencies dictated by the environment. Here we recorded from 159 neurons in the left lateral PFC of one monkey during a delayed-match-to-sample task and a conditional visuomotor task in which the animal was required to perform one of two behaviors depending upon which of four visual stimuli was presented. In this task, the monkey was rewarded for performing a pro-saccade following presentation of two stimuli, and an anti-saccade following presentation of the other two. Results of two-way ANOVAs evaluated at p<.01 revealed that many of these PFC cells showed task-related differences in activity during cue (53/159= 33.3%), and delay (60/159= 37.7%) epochs. Fewer cells showed stimulus-related activity differences during these time periods (13/159= 8.18%, and 11/159= 6.91%, respectively). An interaction between task and stimulus was found in only a small number of cells (2/159= 1.26% and 6/159= 3.77%). These findings demonstrate modulations of cue and delay-related activity in PFC neurons by behavioural rules.
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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) | 0.001 | 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".