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In 124 ds000030 control runs, one removal can make 14 of 17 failed arrow groups pass the basic screen.

In the fixed sample of 124 control runs from OpenNeuro ds000030 version 1.0.0, the prior report identified 17 arrow groups with negative mean gaps; one record removal could make 16 gaps nonnegative. This calculation asks whether the same removal also passes the STOP response-probability rule (0.25–0.75, inclusive). The basic screen requires both a calculable nonnegative mean gap and this probability rule. Fourteen of the 17 groups have at least one separate STOP response removal that passes the basic screen. Across those groups, 36 removals qualify: 24 STOP response removals and 12 GO response removals. The pooled and opposite-arrow conditions exclude no further qualifying removals. The two mean-restoring counterexamples that fail the probability rule have post-removal STOP fractions of 2/15 and 3/15. Results are descriptive of the pinned observations and specified rules. Removals keep later recorded values fixed, do not represent behavior in a changed task, and are not independent samples or population probability estimates. Each arrow group has only 16 STOP trials; these observations do not establish a failure of the independent race model or overall task quality. Recorded decimal precision does not establish physical timing accuracy. No response should be discarded on these results alone.

stop-signal task · response probability · reaction time · record removal · OpenNeuro · reproducibility · exact arithmetic

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References

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  1. Removal of one STOP response can change 16 of 17 failed arrow group mean time checks in 124 ds000030 control runs. sci:sha256:b68be4ec4d3d6f5dd666c841cbed60c4984319065412b7eb295a956385bd0d78

    [Legacy relation: reproduces] New code agrees with every compared field in 378 baseline rows and 16,128 removal rows. Counts and flags agree exactly. Numerical tolerance is 1e-9. The target mean result remains 16 of 17 groups in the fixed 124-run sample. The new probability calculation gives 14 of 17 passing groups.

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

    The analysis uses stop-signal event tables from this exact dataset release and commit; the source manifest records the selected files and their SHA-256 checksums.

  3. supportsdoi:10.7554/eLife.46323

    The guide supplies the separate mean and response-probability conditions discussed here and describes apparent mean failures in independent-process simulations.

  4. doi:10.1038/sdata.2016.110

    The data paper describes the source task and its adaptive delay ladders, which limit interpretation of fixed-record removals.

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