Resolving stages of processing in visual search: Frontal eye field neurophysiology with two degrees of difficulty
Bibliographic record
Abstract
Behavior is the outcome of covert perceptual, cognitive, and motor operations that can be described by mathematical models and are produced by brain systems comprised of diverse neurons. Using the logic of selective influence, we are distinguishing stages of processing supporting visual search. Macaque monkeys searched for a color singleton among distractors. Two operations necessary for the task—search efficiency and stimulus-response mapping—were independently manipulated. Search efficiency was manipulated by varying the similarity of singleton and distractor colors. Stimulus-response mapping, or stimulus-response encoding, was manipulated by varying the elongation of stimuli that cued GO or NO-GO responses. The response times of both monkeys were modified selectively by the 2x2 (High vs Low efficiency) x (High vs Low encoding) manipulations. Single-unit spiking was sampled in frontal eye field of two monkeys. Neurons representing stimulus salience were distinguished from neurons mediating saccade preparation. The times of modulation of both categories of neurons were measured in the 2x2 (High vs Low efficiency) x (High vs Low encoding) manipulations. The manipulation of search efficiency influenced the time taken to resolve singleton location and the delay of saccade preparation of most neurons. The manipulation of stimulus-response encoding did not influence the time taken to resolve singleton location of most neurons but also delayed saccade preparation. The convergence of performance and neural results provide evidence that distinct operations during visual search can be resolved via the experimental logic of selective influence.
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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".