Evidence map›Paper›PMID 40644344›Full record

ArticleSocial cognitive and affective neuroscience2025

Neural correlates of evaluative bias against artificial intelligence-labeled versus human-labeled artworks.

Wenyu Zhang, Cong Xie, Liuqing Jiang, Li Yang, Zhe Hu, Ning Hao

Abstract read
In one paragraph

Article in Social cognitive and affective neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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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

6 authors.

Wenyu ZhangShanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China.
Cong XieShanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China.
Liuqing JiangKey Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention, Hefei Normal University, Hefei, 230601, China.
Li YangKey Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention, Hefei Normal University, Hefei, 230601, China.
Zhe HuKey Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention, Hefei Normal University, Hefei, 230601, China.
Ning HaoShanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China.ORCID 0000-0001-6784-6228

Funding

Humanity and Social Science Foundation of Ministry of Education of China 24YJA190004
6 · The paper itself

Abstract

The rapid advancement of generative artificial intelligence (AI) has enabled machines to produce creative outputs, such as artworks, that rival human creations. However, despite these technological achievements, public acceptance and valuation of AI-generated creative products remain uncertain. This study investigates the bias against AI-generated artworks by examining both behavioral and neural responses using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Across two pre-registered studies (total N = 125), participants evaluated paintings labeled as either human-created or AI-generated, with the artworks being objectively identical. Behavioral results revealed a consistent bias against AI-labeled paintings, with participants rating them lower in different evaluative dimensions. Neural data from EEG indicated relatively higher P300 amplitudes and alpha power during the evaluation of human-labeled paintings, suggesting potentially greater attentional allocation and cognitive engagement. The fNIRS results further demonstrated increased activity in the right angular gyrus and stronger functional connectivity between the inferior frontal gyrus and angular gyrus for human-labeled artworks, which may indicate deeper semantic and emotional processing. These findings highlight that biases against AI-generated artworks are not only evident in explicit evaluations but also manifest in underlying neural processes, reflecting a persistent preference for human creativity. The study underscores the importance of addressing public biases to foster acceptance and trust in AI technologies.

Indexed as

Artificial IntelligenceBrainCreativityPaintingsAdultBrain MappingElectroencephalographyFemaleHumansMaleSpectroscopy, Near-InfraredYoung Adultalgorithm aversionbiasEEGfNIRSgenerative AI

Identifiers

PMID40644344
PMCPMC12342175

What OpenQuestion holds

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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.