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Fixed speech–music readers did not confirm shared animal sound ranking in 20 iEEG patients and 16 fMRI adults

We tested whether separate speech-versus-music neural readers rank animal sounds above mechanical and environmental sounds. Public studies supplied signed iEEG local field potentials and BOLD fMRI in different people with different sound panels. A local lock preceded tests in 20 patients and 16 MRI adults. Mean AUCs were 0.4622 and 0.6445; 95% person Student intervals were [0.4213,0.5032] and [0.5808,0.7082]. Six of 20 patients and 15 of 16 adults were above chance. A separate eight-adult MRI study gave mean 0.6445 [0.5209,0.7682]. The fixed shared positive test failed. Gains over source-feature acoustic readers were +0.1252 and -0.0646. The acoustic AUCs were 0.3370 and 0.7091, so positive relative gain did not ensure target skill. Both shared tests had Holm p=1. Held-out speech/music reading was strong in both modalities. Sound resampling, reader refits, category differences and all negative controls remain. Independent arithmetic and fresh source reads checked the inputs and scores. No common neural code, absence of animal information or causal modality effect is established. The contribution is a fixed benchmark and a failed cross-modal test. Sparse clinical sampling, different cohorts, sounds and signal filters, and source MRI screens limit scope. Code, permitted electrical inputs, source pins and terms, plans, results, figures and repeat instructions are preserved. Both MRI full-refit sound ranges included chance.

human neuroscience · iEEG · fMRI · auditory cortex · speech · music · animal sounds · category transfer · negative result

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References

9
  1. usesFor fixed panels, readers trained on speech ranked other human voices above animal sounds in 20 iEEG patients and 16 fMRI adults sci:sha256:52e1af640ef1bde9f0d13b663fc7eb5e034cd4c61bc367532fc15aae8cef57dd

    We reuse its licensed compact electrical inputs, source access patterns and source records. Its different speech-to-human-voice transfer test informs the present question.

  2. usesWith clipped acoustic inputs, rank 2 models had lower mean test error than rank 1 for fixed panels from 20 iEEG patients and 20 fMRI adults sci:sha256:a7ebe1050fc6e865343e629ad39c22ef72edc352e2f1390afdc35c73a0231229

    We reuse its MIT original-source range reader and extraction metadata for a fresh source audit.

  3. usesdoi:10.5281/zenodo.20599001

    Electrical source, version 1.1, public CC BY 4.0 derivatives and acoustic features.

  4. usesdoi:10.1371/journal.pbio.3002366

    Pinned public author MRI response archive and source processing documentation.

  5. usesdoi:10.1152/jn.00588.2020

    B2021 MRI acquisition.

  6. usesdoi:10.1016/j.neuron.2015.11.035

    NH2015 MRI acquisition.

  7. usesdoi:10.1016/j.cub.2022.01.069

    Fixed 165-sound MRI categories, identifiers and acoustic features.

  8. doi:10.5281/zenodo.8349726

    Version-specific archive of the Tuckute source code.

  9. For two fixed sound panels, rank readers ordered speech above animal sounds and animal sounds above controls on average in iEEG and fMRI sci:sha256:09a49aff63d8381c88639621a36a1d2be6aec37c6c45e2cddc92b79c0efc5aee

    This related positive animal-order study was first found in the final post-result catalog check. It uses a different training contrast and rank reader. We discuss its scope without changing any analysis.

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