Evidence map›Paper›PMID 41166722›Full record

ArticleJournal of cognitive neuroscience2026

Categorization of and Adaptation to Human Voice and Musical Instruments: A Passive Listening EEG Study.

Zi Gao, Andrew J Oxenham

Abstract read
In one paragraph

Article in Journal of cognitive neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Zi GaoUniversity of Minnesota-Twin Cities.ORCID 0009-0000-2508-4448
Andrew J OxenhamUniversity of Minnesota-Twin Cities.ORCID 0000-0002-9365-1157

Funding

Supplement for temporal bone histological tissue scanning equipmentR01DC012262 · NIDCD · UNIVERSITY OF MINNESOTA · PI OXENHAM, ANDREW J. · 2013 to 2023
$4.7M
NIDCD NIH HHS R01 DC012262NIH HHS R01 DC012262University of Minnesota Doctoral Dissertation Fellowship
6 · The paper itself

Abstract

The human voice is a highly socially relevant auditory stimulus, which has been shown to have a special status, both perceptually and neurally. Perceptual studies have revealed adaptation effects in the behavioral categorization of sounds as either human voice or musical instruments. The current study measured evoked responses using EEG to voice and instrument sounds under passive listening to explore the neural underpinnings of both categorization and context effects. In Experiment 1, vowel utterances (/a/, /o/, /u/, and /i/) and instrumental tones (bassoon, horn, saxophone, and viola) were presented with equal probability in a random sequence. The two sound categories were found to produce reliably distinguishable EEG responses at latencies of between 70 and 280 msec. In Experiment 2, an MMN paradigm resulted in mixed evidence for early neural categorization, with an MMN observed for rare instrumental tones embedded in a random sequence of four different vowels, but no significant MMN for a rare vowel embedded in a random sequence of four different instrumental tones. In Experiment 3, ambiguous voice-instrument morphs were used to show that brain responses could be used to predict the context (voice or instrument sound) in which the morphed sounds were presented. The results show that neural correlates of both categorization (voice vs. nonvoice) and context effects can be observed in EEG responses under passive listening conditions.

Indexed as

Adaptation, PhysiologicalAuditory PerceptionEvoked Potentials, AuditoryMusicSpeech PerceptionVoiceAcoustic StimulationAdultElectroencephalographyFemaleHumansMaleReaction TimeYoung Adult

Identifiers

PMID41166722
PMCPMC12991013

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.