title: Crossmodal Statistical Binding of Temporal Information and Stimuli Properties Recalibrates Perception of Visual Apparent Motion.
abstract: Recent studies of brain plasticity that pertain to time perception have shown that fast training of temporal discrimination in one modality, for example, the auditory modality, can improve performance of temporal discrimination in another modality, such as the visual modality. We here examined whether the perception of visual Ternus motion could be recalibrated through fast crossmodal statistical binding of temporal information and stimuli properties binding. We conducted two experiments, composed of three sessions each: pre-test, learning, and post-test. In both the pre-test and the post-test, participants classified the Ternus display as either "element motion" or "group motion." For the training session in Experiment 1, we constructed two types of temporal structures, in which two consecutively presented sound beeps were dominantly (80%) flanked by one leading visual Ternus frame and by one lagging visual Ternus frame (VAAV) or dominantly inserted by two Ternus visual frames (AVVA). Participants were required to respond which interval (auditory vs. visual) was longer. In Experiment 2, we presented only a single auditory-visual pair but with similar temporal configurations as in Experiment 1, and asked participants to perform an audio-visual temporal order judgment. The results of these two experiments support that statistical binding of temporal information and stimuli properties can quickly and selectively recalibrate the sensitivity of perceiving visual motion, according to the protocols of the specific bindings.
authors.affiliation: Department of Psychology and Beijing Key Laboratory of Behavior and Mental Health, Peking University Beijing, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Psychology and Beijing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijing, China; Key Laboratory of Machine Perception (Ministry of Education), Peking UniversityBeijing, China.
authors.forename: Lihan
authors.initials: L
authors.lastname: Chen
keywords: Ternus display; intersensory binding; interval; statistical learning; temporal structure