Stimulus Equivalence in AI-Generated Anime Characters: Equivalence Responding Among Voices, Images, and Names

This study analyzed how people identify AI-generated anime characters within a stimulus equivalence framework. Voices, faces, and names for three characters were treated as members of three-member stimulus classes, and the study examined whether untrained relations between faces and names would emerge after arbitrary matching-to-sample (MTS) training from voices to faces and from voices to names. Unlike previous auditory equivalence studies that relied on the lexical content of spoken words, the present study defined auditory classes by acoustic properties of the voice. Text-to-speech voices derived from real speakers were used, and multiple lines of dialogue per character were generated. Thirteen Japanese undergraduates completed the study online; data from nine participants who completed the procedure without major protocol deviations were analyzed. To minimize pre-experimental stereotypes while retaining meaningful stimuli, “correct” voice–face–name combinations were determined individually for each participant by algorithmically selecting the mapping that was least consistent with their pretest choices. Seven of the nine participants showed increased accuracy on untrained Illustration-to-Name and Name-to-Illustration tests only after both Voice-to-Illustration and Voice-to-Name training, indicating the emergence of stimulus equivalence between faces and names. Two participants did not show equivalence despite additional training, suggesting individual differences in the extent to which acoustic features support conditional stimulus control. These findings extend the analysis of stimulus equivalence to AI-generated anime characters and illustrate a methodological approach for studying the formation of arbitrary relations with meaningful, stereotype-prone stimuli.