Search papers, labs, and topics across Lattice.
This paper introduces a human-centered AI framework for modeling consumer aesthetic perceptions by integrating subjective evaluations with domain-specific and computer vision-based features. The framework jointly models human-derived (consumer and designer) and machine-extracted features to link model outcomes to interpretable design features. The authors demonstrate how perceptual features, design patterns, and consumer interpretations contribute to aesthetic evaluations, enabling better understanding and anticipation of consumer taste.
Finally, a framework that bridges the gap between abstract consumer taste and concrete design features, offering product teams a way to anticipate aesthetic preferences and explore design alternatives more effectively.
Understanding and modeling consumers'stylistic taste such as"sporty"is crucial for creating designs that truly connect with target audiences. However, capturing taste during the design process remains challenging because taste is abstract and subjective, and preference data alone provides limited guidance for concrete design decisions. This paper proposes an integrated human-centered computational framework that links subjective evaluations (e.g., perceived luxury of car wheels) with domain-specific features (e.g., spoke configuration) and computer vision-based measures (e.g., texture). By jointly modeling human-derived (consumer and designer) and machine-extracted features, our framework advances aesthetic assessment by explicitly linking model outcomes to interpretable design features. In particular, it demonstrates how perceptual features, domain-specific design patterns, and consumers'own interpretations of style contribute to aesthetic evaluations. This framework will enable product teams to better understand, communicate, and critique aesthetic decisions, supporting improved anticipation of consumer taste and more informed exploration of design alternatives at design time.
