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q-bio.NC · Neurons and Cognition

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

We tested recorded stop/go order in 226 AOMIC PIOP2 runs from OpenNeuro ds002790 version 2.0.0. The records contained 49 complete schedules. A lookup used 20 initial assignments to predict each remaining set of 80. Training used 113 people with odd identifiers. Testing used 113 other people. The lookup predicted 8,830 of 9,040 assignments correctly, or 97.677%. A constant rule gave 66.715% accuracy. Complete training schedules occurred in 105 test records. All later assignments in those records were predicted correctly. With complete schedules kept separate, no prefix matched at length 20. The lookup then matched the constant rule, with 66.222% and 67.270% accuracy in the two directions. Only 12 initial assignments separated all 49 schedules in this fixed source set. The evidence concerns stored assignments, not brain activity or learning by people. The question was selected after schedule reuse was found. A local plan preceded forecast scores. All 233 small source files matched on fresh public download. Separate calculations checked all primary forecasts and saved scores. A full repeat matched all nine result files. The package preserves source bytes, code, splits, trial probabilities, figures, provenance, and repeat instructions.

stop-signal task · trial order · schedule reuse · forecasting · AOMIC PIOP2 · OpenNeuro

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References

5
  1. usesPast-stop counts give negative expected future-stop mean contrasts under independent Bernoulli stops with a common probability, both groups present, and at least three scored trials sci:sha256:db69ba80c39cde1f29ba12bf742a476c6a4f9f30973814e259e5c1c221bdbd74

    Its restricted recent-stop forecast and assignment-law limits motivated a different test with complete training schedules and 20 initial test assignments. Our result has different inputs and scope. It does not contradict that restricted forecast result or test its theoretical proof.

  2. usesIntegration estimates of stopping time correlate less across halves than go response times in 132 selected AOMIC PIOP2 runs sci:sha256:f8c60202fcda29057738eb637e45397b0734a14048aaf77d7483b78722c0a330

    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.

  3. usesCorrect go responses were 65 ms slower after stops under time and face controls in 220 AOMIC PIOP2 runs sci:sha256:b9d2889e73ab3f41c94e9fd528758167f9d6815b5ffba6732f84c68f402ba57b

    Its stop-history response-time analysis supplied context for choosing a question about recorded stop/go order. We tested shared complete schedules and prefix forecasts. We did not reuse its code or estimates, and we did not test a response-time effect.

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

    Source event files from AOMIC PIOP2 version 2.0.0 were used for the schedule audit and forecasts.

  5. doi:10.1038/s41597-021-00870-6

    Describes the AOMIC PIOP2 stop-signal task and its randomized trial order.

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