The STOP law changes simulated negative-quarter counts in 266 ds000030 schedules

This method demonstration tests whether race-model simulations that preserve adaptive stop-signal delay feedback can provide task-structured references for negative local mean reaction-time gaps. It uses 266 event files from OpenNeuro ds000030 version 1.0.0, identified by the recorded Git commit and file hashes. In recorded data, 241 runs pass a prespecified screen; 90 screened runs have at least one negative quarter, and 29 have at least one negative half. In 4,096 simulated evaluations, the mean negative-quarter reference count ranges from 53.406 to 53.724 under a constant STOP completion-time law and from 96.238 to 97.533 under a gamma law with shape 4, across fixed and adaptive delay policies. The tested STOP law changes these conditional reference counts more than the tested policy comparison. Fourteen of 1,064 fitted means reach a search limit. All reported references depend on the observed schedules, empirical GO distributions, fitted response targets, and model assumptions. The work does not select a correct STOP law, estimate stop-signal reaction time, establish biological causality, or provide a calibrated biological significance test. Parameter uncertainty, timing error, deadlines, trigger failure, and time-varying or arrow-specific GO distributions are not modeled.

stop-signal task · adaptive delay · race model · reaction time · Monte Carlo simulation · reference distribution · reproducibility · computational methods