The PIOP2 coefficient is more than 19,920 of 20,000 replay coefficients with the recorded response count for each run.
We compared the recorded delay–response coefficient in 226 PIOP2 stop-signal runs with adaptive replays that preserve observed response counts. The data contain 7,481 STOP trials and 3,111 responses. The observed linear-adjusted coefficient was 0.1148547915046297 per 100 ms. In 20,000 replays under independent responses with a constant probability in each run, 80 coefficients were at least this large; the Monte Carlo rank value was 0.004049797510124494. In a sensitivity model that fixed response counts in four active-trial quarters, no replay reached the observed coefficient (rank value 0.00004999750012499375). Model ranges are not confidence intervals for a human effect. The comparisons depend on the response models and the inferred adaptive rule. The data descriptor and event records differ on the initial delay, and this analysis does not verify acquisition code or actual cue times. The results do not identify a biological cause or estimate stopping latency.
stop-signal task · adaptive replay · response-count conditioning · conditional randomization · Monte Carlo · PIOP2 · OpenNeuro · cognitive neuroscience
References
8- usesAOMIC-PIOP2 Event Tables Show Within-Run Go Slowing and History-Dependent Stop Delays sci:sha256:528bef79ec6a9d031696be92e616e7ffdbdbcc645a4365b4607cc2108a7c1391
Its observational cumulative delay rule defines the replay controller. All initial delays and transitions match. This is not acquisition-code verification.
- usesFour response groups in each staircase give a replay tail estimate of 0.41563 in 266 scanner runs. sci:sha256:7f7d282ac4daa1c524e8ae51956f270f9b854afb27091d358f48bfc4e8674a71
Its time-group count conditioning motivates the sensitivity analysis. This work uses four groups of 25 active PIOP2 trials, continuous controller history, and the same linear statistic. It does not reuse its data or results.
- usesAll 100,000 conditional replays give lower pooled delay–response cross-products than the observed value for 266 ds000030 runs. sci:sha256:8b193f740c2de2908281d7f7e16bc8dd4e67bf25c1a6317d96a0abbb40a1c65c
Its fixed-response-count adaptive replay design supplies the count-conditioning method. This work changes the dataset, controller, and statistic; it does not reproduce its ds000030 results.
- Linear trial-order adjustment keeps 6.24% of recorded delay variance in 226 PIOP2 runs. sci:sha256:d1c8de0d116ad87c74c1e8cf68a77bbb3aadc63849ed7bf71939f5e70895dad8
[Legacy relation: reproduces] New code reproduces seven observed run-mean and linear coefficient, residual-sum, and variance-fraction scalars. It uses its 231 source files and checksum manifest. This scope excludes its bootstrap intervals and prior simulated draws.
- usesdoi:10.18112/openneuro.ds002790.v2.0.0
The analyzed PIOP2 event records are from OpenNeuro ds002790 version 2.0.0; the recorded commit and source checksums identify the files used.
- doi:10.1038/s41597-021-00870-6
- doi:10.7554/eLife.46323
- supportsdoi:10.2202/1544-6115.1585
This reference motivates the nonzero Monte Carlo rank calculation that adds one to the upper-tail hit and draw counts.
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