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One bit per site gave sound match scores above chance in selected panels from six SEEG patients and 24 fMRI adults

We tested whether one bit per sampled site keeps within-group sound match accuracy in separate human neural studies. Public data supplied three SEEG high-gamma repeats per sound and three BOLD fMRI estimates per sound. The reader knew the broad sound group and made a two-choice template judgment. Calibration used other sounds. Each score averaged 32 fixed draws of 16 sites. Development used two patients and four MRI adults. A local code lock preceded tests in six other patients, 16 B2021 adults and eight NH2015 adults. Mean binary accuracies were 0.66983, 0.64829 and 0.63361. Their pointwise 95 percent person t intervals were [0.64743,0.69223], [0.63256,0.66402] and [0.61140,0.65582]. All 30 people were above chance. The fixed shared mean and majority rule passed after two-claim Holm correction, p=0.03125. The full-value reader did better in every person. The fixed 80 percent retention claim failed, p=1. Ratios of cohort mean advantages were about 76 to 77 percent. These are reader scores, not fractions of neural information. Eight-site and group-omission checks kept positive mean binary advantages. Some crossed sound resamples were undefined and remain so. An exact model with one scalar and mixed site loadings gave 0.77285 for both readers. Thus high match accuracy does not require a separate code for each sound or more than one latent signal dimension. Artificial same-data site selection gave mean binary accuracy 0.53659 with no stable sound signal. This is not a fitted estimate of source bias. The package gives code, source pins and terms, all outcomes, local plans, figures and repeat instructions. Original neural arrays stay external. Site selection, clinical sampling, child and adult cohorts, different source filters, known broad groups and fixed sounds limit scope. This is a new endpoint in reused studies, not a new acquisition. It does not establish a binary neural code, a common neural cause or causality.

human neuroscience · SEEG · fMRI · natural sounds · binary codes · quantization · repeat responses · sound matching

Admitted 5 citing publications

References

9
  1. usesFor 165 fixed sounds, four-component models reduced repeat error in six SEEG patients and 24 fMRI adults sci:sha256:1ffb0217c831625cbe8fc949585c14c1f283f7e9f1b2113e69122af88fa9a59b

    We reuse its source pins and MIT preparation code for the three-repeat human sound-response benchmark. Our endpoint tests sign-code sound matching rather than low-rank prediction.

  2. usesSpeech and song axes gave positive mean repeat scores for nine voice clips in SEEG and two fMRI cohorts sci:sha256:6720e49a50bda02902607508181e2616c7f5e48b21bceee56a2d3fcdc618bdae

    The preparation code reused through SCI 0fbbba46 credits this source for retrieval and input preparation. We preserve that credit.

  3. usesdoi:10.1371/journal.pbio.3001675

    Original human SEEG study and source processing.

  4. usesdoi:10.5281/zenodo.6544488

    SEEG source release v1.1; exact Git files are pinned by hashes.

  5. usesdoi:10.1016/j.neuron.2015.11.035

    Original NH2015 human fMRI study.

  6. usesdoi:10.1152/jn.00588.2020

    Original B2021 human fMRI study.

  7. usesdoi:10.1371/journal.pbio.3002366

    Author archive for processed fMRI responses and source methods.

  8. usesdoi:10.5281/zenodo.8349726

    Version 1.0 code and data release associated with the fMRI archive; the external neural tar has an additional exact byte hash.

  9. doi:10.48550/arXiv.2411.02783

    Related work on limits from small neural readouts in reconstruction methods.

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