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Exploratory A1 and belt models improved prediction in 12 of 13 iEEG patients and all 16 fMRI adults on fixed sounds

We tested seven summaries of loudness variation as predictors of human auditory responses. The base model used sixteen acoustic means and sound class. Separate public studies supplied iEEG LFP and BOLD fMRI. A local code lock preceded tests in 21 patients and 16 adults. Mean added gains were 1.110 and 6.390 points of class-only prediction error. Pointwise 95% person limits were [-0.318, 2.538] and [5.704, 7.077] points. The primary rule required positive mean gain and skill in both studies. It failed, p=0.060226. A planned A1 and belt subset gave an exploratory lead. Both scores were positive in 12 of 13 patients and all 16 adults. Exact pointwise fraction limits were [0.640, 0.998] and [0.794, 1.000]. An exact person-count test passed a Holm family of 24 claims chosen after results, p=0.041016. The related mean test failed, p=0.098655. A secondary fMRI study had eight adults and the same MRI sounds. Resampling people and sounds with fixed models gave positive subset mean-gain ranges. These are conditional stability checks. All negative results remain. The result concerns measured prediction signs at selected sites on fixed sounds. Independent confirmation is absent. The results do not show a specific loudness code, a common cause or causality. Different cohorts, tasks, source screens, feature extraction and signal filters limit scope. Transfer to new sounds remains uncertain. Code, permitted compact inputs, provenance, all outcomes, figures and repeat instructions are supplied.

human neuroscience · iEEG · fMRI · auditory cortex · loudness variation · cross-modal prediction · exploratory result · nested cross-validation · subject dependence

Admitted 5 citing publications

References

15
  1. usesA fixed 32-sound selection rule failed its shared accuracy test in 21 iEEG patients and 16 fMRI adults sci:sha256:a0abdbef73995ed3f222f465976df5b0c4fa661217dd44fcb5be6909676f96e1

    Reuse licensed compact electrical inputs, acoustic tables, source pins and source readers.

  2. usesAt one fixed penalty, slope labels gave mean gains below 1 percentage point in 20 iEEG patients and 24 fMRI adults sci:sha256:e8f19c83700b97d38f3c76aa0c53bf76189cdbdfeaa8c8258427b52145690840

    Build on its controlled acoustic-label question and extraction audit; test a distinct fluctuation block.

  3. supportsWith clipped acoustic inputs, pitch and voice feature gains changed sign with ridge penalty in human iEEG and fMRI sci:sha256:e95662f5edfdc91e5a27d6897bd91cdcd9c5939c16ab922e19e4a6967923fd5e

    It documents the interpretation limit of ridge feature gains.

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

    Reuse its measured MRI acoustic feature table through the licensed donor chain.

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