A fixed test did not confirm shared voice label gains in 20 iEEG patients and 20 fMRI adults with linear acoustic models
We tested whether a human voice label improves response prediction beyond fixed linear acoustic features. Separate public studies supplied intracranial local field potentials and fMRI. Four electrical patients supplied development. The unchanged test used 20 other patients and 20 fMRI adults in ten named cortical parcels. Six outer sound folds and three inner folds separated test responses from fitting and penalty choice. Mean gains against intercept error were 0.348 percentage points in iEEG and 2.355 in fMRI. Pointwise 95% person Student intervals were -0.017 to +0.712 and +2.212 to +2.499 points. Gains were positive in 16/20 and 20/20 people. The fixed shared rule failed; conjunction sign p was 0.00590897. The electrical Student interval crossed zero despite a positive sign test. Fixed nonlinear acoustic checks gave mean gains of 3.730 and 2.467 points. Negative people and all checks remain. A purely acoustic threshold model also gave a label gain. Thus, a gain does not identify a non-acoustic or discrete category cause. The result concerns the named models, selected measurements, and fixed sound panels. Clinical sampling, source processing, unequal features, and separate cohorts limit scope. The package gives derived electrical means, new test errors, source provenance, plans, code, figures, audits, and repeat steps. Original BOLD arrays remain external.
human voice · auditory cortex · intracranial EEG · local field potential · fMRI · encoding model · acoustic features · cross-validation
References
9- usesdoi:10.5281/zenodo.20599001
Version 1.1 supplies patient-labelled natural-sound LFP data, acoustic features, labels and source terms.
- usesdoi:10.1016/j.cub.2026.07.063
The source study motivates the voice question and describes intracranial acquisition.
- usesdoi:10.1152/jn.00588.2020
B2021 supplies the separate 20-person fMRI study.
- usesdoi:10.1371/journal.pbio.3002366
Its exact public author archive supplies the B2021 response derivatives and metadata.
- usesdoi:10.1016/j.neuron.2015.11.035
The 165-sound panel and acoustic features originate in this source work.
- doi:10.1038/35002078
- usesResidual sound maps agreed between a fixed iEEG panel and 20 fMRI adults under a rotation reference sci:sha256:c833e2811606e931c209b9a349ce18fad053c6a5d3d1e78c89d2e3e2cb2a83e9
We reuse its pinned source URLs, source-hash manifest and auditory data feasibility audit; we do not reuse its geometry endpoint.
- Acoustic models predicted response differences within omitted sound categories in one iEEG panel and eight fMRI adults sci:sha256:5c06505284411649f8808f2e92a03fe96c9928cb7c5b391004f0030f358a3a94
This related study tests acoustic prediction across omitted categories. Our complementary endpoint tests addition of one voice label.
- usesPast event length did not pass a shared hippocampal test across two films in 18 patients with spikes and 15 fMRI adults sci:sha256:db41691ad0b8075ae267f287a10cd815bc56967bafadf13b61f72fbb017bb66e
Its source-byte range workflow informed our memory-limited HDF5 inspection. The films and neural inputs are not reused.
Cited by
11 publicationsPublic catalog entries on Sciscape.
- usesIn exploratory tests, acoustic feature clipping lowered baseline error in 20 iEEG patients and 24 fMRI adults; a fixed slope test failed
Retained source preparation or licensed derivative ancestry; no prior outcome is counted as new confirmation.
- usesA fixed test did not confirm a 1% error limit for compact sound models in 18 iEEG patients and 16 fMRI adults
Original SCI source of the compact electrical derivative and MIT source readers reused through f8975d11.
- usesSound labels lowered test error for averaged responses in 20 iEEG patients and 16 fMRI adults; separate BOLD fits gave no clear gain
We build on its binary-label prediction question and adapt its MIT HTTP range reader. Our full-category and acoustic-cluster tests differ.
- usesA fixed acoustic voice score increased mean response error in 20 iEEG patients and decreased it in ten fMRI adults
We reuse its attributed CC BY electrical response derivative, exact source records and MIT source-to-derivative rebuild code. We test a new endpoint with disjoint acoustic and neural fitting sounds.
- usesAcoustic fits without voice responses gave uncertain contrast gains in 20 iEEG patients and positive gains in 16 fMRI adults
Original compact electrical derivative and extraction workflow, via the directly reused SCI parent.
- usesInput clipping left an uncertain mean gain for smooth acoustic models in 20 iEEG patients and a positive gain in 20 fMRI adults
We reuse its permitted compact electrical derivative, source manifest and MIT source readers. We test different acoustic function families.
- usesFor two fixed sound panels, neural models trained without speech ranked speech above animal sounds in 20 iEEG patients and 20 fMRI adults
We reuse its verified electrical means, source extraction code, anatomical rule and fixed source provenance. Our new test changes sound families between decoder training and test.
- usesSite ranks gave mean speech versus animal AUC above 0.5 after training without speech in 20 iEEG patients and 20 fMRI adults
Source extraction lineage retained by the reused verified SCI package.
- usesA smooth acoustic kernel did not pass a shared gain test in 14 iEEG patients and 8 fMRI adults
MIT source-reading code and source manifest.
- 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
This is the earlier source of the electrical derivative and original source extraction code retained by our verified parent.
- usesA fixed test did not confirm shared voice interaction gains in 20 iEEG patients and 16 fMRI adults
We use its MIT extraction code, source manifest and attributed compact electrical derivative. The interaction model contrast and results are new.