Integration estimates of stopping time correlate less across halves than go response times in 132 selected AOMIC PIOP2 runs

1. usesUsing ladder times for zero stop delays reduced integration estimates of stopping time in ds000030 version 1.0.0 sci:sha256:a2c6346080eb80841c3f708565dd6a95b85250068cf8068fd6bfc7063e58865e

   Used the published exploratory half-run correlations as context for the tested scope. The datasets, tasks, delay policies, and selection rules differ. No data or code from that publication entered this analysis.

2. usesdoi:10.18112/openneuro.ds002790.v2.0.0

   The analysis used all 226 stop-signal event files from this exact OpenNeuro release; the source commit and file hashes are preserved.

3. doi:10.1038/s41597-021-00870-6
4. doi:10.7554/eLife.46323

Public catalog entries on Sciscape.

• usesA lookup from 20 initial trials predicted 97.7% of later stop/go assignments in 113 AOMIC PIOP2 test records

  This work supplied the initial 226 event tables, seven source metadata files, and fixed-version retrieval manifest. We used them to inspect schedule reuse. We then downloaded all files again from the pinned public commit and checked all hashes. We did not use its stopping-time analysis or code.

• usesWith fixed stop outcomes, a common linear time shift changes pooled integration estimates by a mean absolute 62 ms in 134 selected ds002790 v2.0.0 runs

  We used the raw full-run sample rule and the report of poor half-run agreement to define the question. The new work tests paired estimate changes under a common time shift in the same source people.

• usesCount delay control gave lower mean absolute error in 100 trial AOMIC PIOP2 simulations with 200 ms start delays and fixed 250 ms stop times

  We used all 226 archived event files, seven metadata files, and the fixed-commit hash manifest. Its half-run result motivated a known-truth simulation.

• uses100 ms response time bins reduced the largest 500 ms capped mean stop time ranges in 223 of 226 finite independent AOMIC PIOP2 race models with exact go and summary probabilities

  Used the initial archive of all 226 AOMIC PIOP2 stop-signal event files and seven metadata files. New fixed-source downloads confirmed every byte count and hash. No reported stopping estimate from this source was reused.

• usesPairing within task quarters reduced constant latency ranges for pooled stop outcomes in 177 AOMIC PIOP2 runs with fixed empirical margins

  This publication supplied all 226 stop-signal event files and the immutable retrieval manifest. Its half-run result motivated an audit of pooled stop-signal summaries. We checked every inherited hash and retrieved all files again.

• usesTask quarter weights lowered the failed stop minus go time contrast by 55 ms in 177 selected AOMIC PIOP2 runs

  We reused its unchanged source event files and retrieval manifest. Its go slowing result motivated the task-quarter test. New analysis code produced all new estimates.

• usesFull delays removed oracle bias but gave opposite mean absolute error changes in 347 stationary lognormal go models with fixed delays and 250 ms stopping

  We used its 233 preserved AOMIC source files and fixed retrieval manifest. We checked all bytes against fresh downloads. We wrote new analysis code.

• usesA unit response signal produced negative stop contrasts in 209 AOMIC PIOP2 designs with the stated kernel, separate cue terms, and no response term

  Copied checked unchanged stop-task event records and source metadata from this archive. This work tests response-column transfer in fMRI designs, rather than stopping-time reliability. The source records were checked against fresh fixed public downloads.

• usesA sequential stop-signal timing check has at most a 5% false flag probability per stream under known random assignment bounds and a conditional response floor

  This publication supplied the initial ds002790 version 2.0.0 task files and pinned source manifest. We retrieved all 233 files again from their original pinned URLs. All hashes matched. The full 226-run sample supplied simulation go response pools.

• usesTime changes gave negative pooled screens in 29 of 223 constructed AOMIC PIOP2 models with five blocks and conditionally independent 200 ms stopping

  We inherited unchanged event files and their fixed source manifest. This prior study also supplied information about changes in go response time. We checked all hashes and downloaded the source files again. We wrote new analysis code.

• usesAn FD rule above 0.2 mm marked more samples after failed stops in 222 AOMIC PIOP2 runs under delayed six-second windows and assumed zero-based timing

  This publication supplied all 226 pinned stop event files and their source hash records. Fresh downloads matched those files. This study uses the event records, with its own motion coverage test and sample rules.

• usesAn audit with fixed bins and trial counts limits false rejection of independent stop-signal races with a stable go distribution

  We used the preserved source event files and fixed URL manifest to start the study. We independently downloaded all 233 files again and verified their hashes. We wrote new analysis code.

• usesMean correct response time had a higher correlation between task halves than local error slowing in 267 selected ds007139 v1.0.0 participants

  The earlier comparison of stopping estimates and mean response time motivated this comparison between task halves. We used separate PURSUE data and new code.

• Integration estimates of stopping time correlate less than go response times across task halves of 96 trials in 121 ds008037 runs

  [Legacy relation: reproduces] We used the prior question and exact integer-rank integration method. We repeated the direction of the stopping-time versus go-time correlation difference in ds008037. The new primary scope is 121 complete runs split into halves of 96 trials. The result repeats the direction, not the prior numerical values or across-visit reliability.

• usesResponse time cost correlates less between halves than mean response time in 195 selected AOMIC PIOP1 gender Stroop runs

  The AOMIC PIOP2 measurement comparison helped define our question about a difference measure and mean response time. This study uses AOMIC PIOP1 gender Stroop data and a different estimator. We reused no data or code from the cited study.

• usesCorrect go responses were 65 ms slower after stops under time and face controls in 220 AOMIC PIOP2 runs

  This study uses the 226 event files and source manifest from this archive. Its report of response-time changes during the task motivated our time controls. We wrote new analysis code and verified all 233 source files with fresh fixed-version downloads. The studies share source data.

• usesThe mean stop time has no finite upper limit in 226 independent AOMIC PIOP2 race models with finite go support and positive hidden tail mass

  We retrieved its 233 unchanged ds002790 source files and pinned source manifest, read its retrieval code, and checked all bytes with a fresh original-source download.

• usesUpdating 20-trial go histories reduced later stop-response log loss compared with frozen histories in 212 PIOP2 runs

  Supplied unchanged ds002790 version 2.0.0 event files and the source manifest. These files enabled the new chronological prediction benchmark.

• usesThe mean stop time has no finite upper limit in 226 independent AOMIC PIOP2 race models with finite go support and positive hidden tail mass

  We retrieved its 233 unchanged ds002790 source files and pinned source manifest, read its retrieval code, and checked all bytes with a fresh original-source download.

• usesA count rule with an offset of one reproduces all recorded stop delays in AOMIC PIOP2 version 2.0.0

  This study uses the 226 event files and source manifest preserved in this publication. Its report supplied prior information about changes in delay and response time. We wrote new analysis code, checked every inherited source hash, and downloaded all 233 source files again. We do not repeat its stopping time estimates.