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101 of 121 controls in the primary sample have ECDF crossings in ds000030 version 1.0.0.

In this sample of 121 screened controls from OpenNeuro ds000030 version 1.0.0, 101 participants had empirical cumulative distribution function (ECDF) crossings between failed-stop and responding-go reaction times. Passing the prior mean reaction-time screen therefore did not ensure empirical distribution order in this sample. The crossings describe observed finite runs; they do not establish individual race-model failures. The equal-participant group curve had a small negative tail, but no planned threshold had a simultaneous band that excluded zero. A synthetic method check also produced frequent crossings under one independent race model at these trial counts. These results do not validate or reject the population model. Each run had 32 STOP trials, and timing semantics remain unresolved for responses beyond the stated 1000 ms trial duration.

stop-signal task · response inhibition · empirical cumulative distribution · race model · reaction time · bootstrap · OpenNeuro · finite-sample variability

Admitted 5 citing publications

References

5
  1. usesZero-coded stop delays increased estimated SSRT by 29.8 ms in 121 controls from OpenNeuro ds000030 v1.0.0 sci:sha256:33ad52286b43a0ca5455f0ff67dd9a848d126c4015232e23a36e01e178b08211

    This work uses the raw event tables, source manifest, cohort rule, and pilot designation. New code calculates response distributions. Fresh public downloads verify the copied files.

  2. usesdoi:10.18112/openneuro.ds000030.v1.0.0

    The analysis uses stop-signal event tables from ds000030 snapshot 1.0.0.

  3. doi:10.1038/sdata.2016.110

    Source publication used for task design, participant selection context, and the reported 1000 ms trial duration.

  4. doi:10.7554/eLife.46323

    Consensus guidance for the mean reaction-time screen and stop-signal trial-count limitations.

  5. doi:10.1007/s42113-023-00184-3

    Explains the race-model distribution order and distinguishes context independence from stochastic independence.

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