Mapping visual search errors to covert operations with frontal eye field neurophysiology and double factorial design
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 previously distinguished the stages of processing supporting visual search (Lyu, Reppert, Schall, 2023). In that study, macaque monkeys searched for a color singleton among distractors. Two operations necessary for the task were independently manipulated. Singleton localizability was manipulated by varying the similarity between singleton and distractor colors. Stimulus-response mapping was manipulated by varying the discriminability of search array shape, signaling GO/NOGO response. The organization and termination rule of the two operations were determined using System Factorial Technology (SFT; Lowe et al, 2019). The necessary next step in this research is to account for performance errors in this difficult task, which influence the logic of the SFT diagnosis. Monkeys made two key errors: on GO trials, monkeys occasionally shifted gaze to a distractor due to unsuccessful localization (GO error). On NOGO trials, they failed to inhibit their saccade towards either the singleton or the distractors (NOGO errors). NOGO errors reflect failure in discrimination alone or both operations, respectively. We probed the neural sources of these error saccades using single-unit spiking in frontal eye field. Neurons representing stimulus salience were distinguished from neurons mediating saccade preparation. Our data suggest that GO errors occur when visual salience neurons misrepresenting the distractor as the singleton. NOGO errors to singleton arise from incorrect discrimination by saccade preparation neurons whereas NOGO errors to distractor arise from inaccurate response from both neuron types. The convergence of performance and neural results on error trials offer constraints to mathematical models and provide evidence so that distinct operations and their organization during visual search can be resolved.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".