Visual attention priming based on crossmodal expectations

Abstract

Humans perceive the world using five senses. Research results suggest that this multisensorial perception may be of fundamental importance for development and learning, as well as for creating cognitive capabilities. Moreover, humans have the capacity to create intersensorial expectations to guide attention and perception. We are interested in comprehending how these capabilities may improve robot perception. In this line of research, we present a cross-modal perceptual architecture that can segment objects based on visual-auditory sensorial cues, construct an associative sound-object memory, and create visual expectations of objects (attentional priming) using a sound recognition algorithm.