Evidence map›Paper›PMID 42718450›Full record

ArticleFrontiers in psychology2026

From perceptual rule transformation to listener attribution judgments: a blind-listening experiment on AI-generated, human-AI collaborative, and human-composed music.

Junsong Chang, Yueqi Jing

Abstract read
In one paragraph

Article in Frontiers in psychology, 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.

Junsong ChangSchool of Music, Xinxiang University, Xinxiang, China.
Yueqi JingSchool of Korean Music Theory, Chung-Ang University, Dongjak-gu, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study examines listeners' creator attribution judgments and aesthetic evaluations of AI-generated, human-AI collaborative, and human-composed music under blind-listening conditions. Within a framework of personal compositional style modeling, compositional experience was transformed into executable sampling constraints through natural-language interaction. This process is conceptualized in the present study as "perceptual rule transformation" and was used to generate 18 melodic excerpts. Seventy-one participants with music training completed tasks involving creator attribution judgment, attribution confidence rating, and aesthetic evaluation. The results partially supported H1: a statistically significant but very small association was observed between the actual compositional condition and listeners' attribution judgments, χ

Indexed as

aesthetic evaluationAI musicblind listeningcreator attribution judgmentendogenous perceptual biashuman–AI collaboration

Identifiers

PMID42718450
PMCPMC13553347

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.