Proceedings of the Annual Meeting of the Cognitive Science Society

We describe a recurrent neural network model of rate effects on the syllable-initial voicing distinction, specified by voiceonset-time (VOT). The stimuli were stylized /bi/ and /pi/ syllables covarying in VOT and syllable duration. Network performance revealed a systematic rate effect: as syllable duration increases, the category boundary moves toward longer VOT values, mirroring human performance. Two factors underlie this effect: the range of training stimuli with each VOT and syllable duration, and their frequency of occurrence. The latter influence was particularly strong, consistent with exemplar-based accounts of human category formation.