Back

EEG-based analyses reveal different temporal processing patterns in aesthetic evaluation

Hou, L.; Zhang, G.; Li, X.; Tang, D.; Parviainen, T.; Cong, F.; Karkkainen, T.

2025-12-15 neuroscience
10.64898/2025.12.12.693903 bioRxiv
Show abstract

BackgroundExisting studies estimate harmony values using the color harmony model, but how these values are reflected in distinct neural stages (early automatic vs. late evaluative) and shape subjective preferences remains unexamined. MethodsTo fill this gap, we recorded behavioral and event-related potential data (N=30 adults, age: 29.20{+/-}2.38 years old) to examine whether the predictions of color harmony theory regarding harmonious vs. disharmonious color combinations align with neural activity from a color perception paradigm. ResultsBehavioral results showed that two-color combinations received higher aesthetic pleasantness ratings than three-color combinations, while the ratings for color harmony were relatively smaller than for disharmony. Univariate ERP analysis revealed that P1 amplitude for harmonious color was significantly higher than for disharmonious color, particularly when three colors were present. In contrast, the amplitude and latency of P2 were significantly impacted by the number of colors. Additionally, multivariate pattern analysis further demonstrated that neural activity reliably differentiated between harmonious vs. disharmonious color combinations and two-color vs. three-color combinations during both P1 and P2 time window. ConclusionsThese findings indicate that harmonious versus disharmonious color combinations subtly influenced early visual processing, while the number of colors used in the design had a stronger influence on aesthetic judgments.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.