Physical characteristics of digital characters influence group categorization and recognition of affective states

Ethnic bias in social group categorization and recognition of affective states persist in diverse countries like Canada, potentially affecting interactions with minority groups. With the growing use of digital characters (DCs) across various settings, it becomes crucial to explore whether these biases extend to virtual environments to mitigate these issues. This study created and validated 16 realistic DCs to examine how individuals perceive their physical characteristics while investigating the effects of ethnic biases. 112 participants from the majority group (White) completed a two-part online task in which they were asked to perceive in the 16 DCs 1) physical attributes in a neutral state such as phenotype (Black, White, Latin American, or Asian), gender, age, and realism, and 2) four affective states expressed by DCs (pain, anger, sadness, or neutral), as well as components associated with them (intensity, valence, and arousal). Participants categorized White DCs more accurately than Asian and Latin American DCs, and faster than Latin American DCs. The latter were also categorized less accurately and slower than the two other minority groups (Asian and Black DCs). Furthermore, the anger facial expression on Asian DCs was the least recognized among all other affective states and phenotypic groups. Thus, an attenuated own-phenotype bias emerged in contexts with multiple phenotypes, where very similar or very different physical characteristics contribute to efficient categorization. This study contributes to a finer understanding of how different phenotypic groups are perceived in virtual environments and introduces newly created digital characters that could be used for studies in human-agent interactions.