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Lorilei Alley, Alexandra Schmid, Katja Doerschner; Neo's Spoon and Newton's Apples: Prediction of rigid and non-rigid deformations of materials. Journal of Vision 2017;17(10):224. doi: 10.1167/17.10.224.
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© ARVO (1962-2015); The Authors (2016-present)
Throughout life we acquire complex knowledge about the properties of objects in the world. This knowledge allows us to efficiently predict future events (e.g. whether a falling porcelain cup will shatter) and is critical for survival (e.g. predicting if a snake will strike). Vision research is only beginning to understand the mechanisms underlying such complex predictions. We conducted a study to investigate whether the visual system makes predictions about the kinematics of materials based on object shape and surface properties. Stimuli were computer- rendered familiar objects (teacup, chair, spoon, jelly etc.) that we hypothesised would generate strong expectations about their material kinematics when dropped from a height (whether they would shatter, wobble, splash, bounce, etc.). Control stimuli were novel unfamiliar 3D shapes rendered with the familiar objects' surface properties. Utilizing a 'violation of expectation' paradigm, on each trial we showed a static view of the object, followed by a video sequence of the object falling and impacting the ground. The motion was either 'congruent' with the object and material, behaving as expected (e.g. a falling teacup shattered), or 'incongruent', where the kinematics violated potential predictions (e.g. a falling teacup wrinkled like cloth). In a 'static' condition, different observers viewed only the first static frame. Observers used a scale to rate each video clip on 4 adjectives: 'hard', 'gelatinous', 'heavy', and 'liquid'. We analysed whether congruency of the motion affected how fast observers performed the ratings. We computed a predictability score for the 'congruent' outcome by comparing ratings between this condition and the static condition. Stimuli with high predictability scores should generate larger surprise effects (i.e. longer RTs), and this is exactly what was found (r =.483, p < .001). Our results demonstrate for the first time that kinematic properties are an integral part of the visual system's representation of material qualities.
Meeting abstract presented at VSS 2017
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