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Tests with one sound class left out did not confirm shared gains from seven loudness terms in 21 iEEG patients and 24 fMRI adults

We tested whether seven measures of loudness change improve auditory response forecasts for a sound class absent from training. Separate public studies supplied signed intracranial field means and BOLD fMRI. Four patients and four MRI adults supplied development. A local code lock preceded new endpoint tests in 21 patients, 16 B2021 adults and eight NH2015 adults. Each model used nested tests that left out whole sound classes. Two fixed claims compared 16 acoustic means with 23 terms, and 81 other terms with all 88 terms. Each claim required positive mean added gain and full-model skill in all three groups. Both claims failed after Holm correction, p=0.304688. With 81 other terms, mean gains were 0.395, 3.056 and 0.776 error points. Their pointwise 95% person Student limits were [-0.485,1.276], [1.886,4.226] and [0.223,1.329]. The electrical gain also had a range that crossed zero when people and sounds varied. All negative people, classes and controls remain. A post-result four-claim family supported rich-model mean skill in all studies, Holm p=0.015625. This is exploratory and does not confirm added value from the seven terms. This is a new test in reused public people. It is not independent confirmation of the earlier exploratory anatomy result. Separate cohorts, different sounds, task, feature extraction, source screens and signal filters limit scope. No shared neural mechanism or causal effect was established. The package gives licensed compact inputs, source pins and terms, all results, figures and repeat code.

human auditory cortex · intracranial EEG · fMRI · sound class transfer · loudness · negative result · nested validation

Admitted 2 citing publications

References

14
  1. usesExploratory A1 and belt models improved prediction in 12 of 13 iEEG patients and all 16 fMRI adults on fixed sounds sci:sha256:e23c7d60934cfd68c27b80a6551ac7933a03bf2a24d4ed536175f7463d9a0613

    Its exploratory loudness result set the question; its licensed compact inputs and MIT source and model code support this new class-transfer test.

  2. usesAcoustic models fit to other sound classes predicted music response patterns in 20 iEEG patients and 24 fMRI adults sci:sha256:b9574e9f866b3489f87868b67c294f081e3fd2b1efa4ff110a32f8558fb27b4b

    Prior class-excluded music test and source preparation lineage. Our absolute forecasts and nested class-excluded tuning differ.

  3. usesA quadratic model failed a shared sound prediction test in 20 iEEG patients and 16 fMRI adults with whole groups excluded sci:sha256:100420bdc0d660191843fb7151b1496d067da7b47aaeab138a84ff296e868d64

    Related failed class-excluded quadratic test; read before final interpretation.

  4. usesTwo fixed acoustic rules did not confirm shared nonvoice prediction gains in 18 SEEG patients and two fMRI groups sci:sha256:f6ade96a55abd5dd6909603449b3d63ad197279407aa009d924a3be21ddc98ae

    Licensed eGeMAPSv02 MRI feature table, extraction provenance and code.

  5. usesdoi:10.5281/zenodo.20599001

    Version 1.1 source processed human iEEG and acoustic data. Fresh byte-range and feature checks are preserved.

  6. usesdoi:10.1016/j.cub.2026.07.063

    Original human iEEG study and acquisition/preprocessing methods.

  7. usesdoi:10.1152/jn.00588.2020

    B2021 human fMRI study.

  8. usesdoi:10.1016/j.neuron.2015.11.035

    NH2015 fMRI and the 165-sound corpus.

  9. usesdoi:10.1371/journal.pbio.3002366

    Public MRI derivative archive; exact hash is in the package.

  10. usesdoi:10.1016/j.cub.2022.01.069

    Pinned source of MRI stimulus names and categories.

  11. doi:10.1109/TAFFC.2015.2457417
  12. doi:10.1145/1873951.1874246
  13. doi:10.1038/s41593-023-01285-9
  14. doi:10.7554/eLife.82566

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