Experimental Data on Cognitive Processing Bias in AI-Generated Art Evaluation

564 bytes of tabular data from three experiments investigating bias in aesthetic evaluation. The data, published by Yongquan Wang on figshare under CC-BY-4.0, examines how negative bias toward AI-generated art emerges during controlled versus automatic cognitive processing. Results indicate the bias was smaller under automatic processing conditions and was mitigated by providing interpretive semantic cues.

Use Cases

• Analyzing the effect of creator labels (AI vs. human) on aesthetic ratings based on the experimental manipulation described.
• Investigating the relationship between cognitive processing modes (automatic vs. controlled) and bias magnitude as outlined in the study.
• Testing the mitigating effect of interpretive semantic cues on bias against AI-generated art, a key finding mentioned in the description.
• Replicating or extending analysis of the Pleasure-Interest Model of Aesthetic Liking (PIA model) framework applied to AI art perception.

Strengths

• Data is from three controlled experiments, suggesting a structured experimental design.
• Findings are tied to a specific theoretical model (Pleasure-Interest Model of Aesthetic Liking).
• The dataset is openly licensed under CC-BY-4.0.

Limitations

• The dataset is very small (564 bytes), indicating extremely limited scope or summary-level data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for statistical modeling.

Provenance

Source

figshare

Collection Method

Data collected from three psychology experiments where creator labels for AI-generated artworks were manipulated.

Freshness

Last updated 2026-06-03 04:26:49; freshness should be verified.