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Movement kinematics affect action prediction: Comparing human to non-human point-light actions

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Stadler,  Waltraud
Department Psychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department of Sport and Health Science, TU Munich, Germany;

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Springer,  Anne
Department Psychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department of Sport and Exercise Psychology, University of Potsdam, Germany;

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Parkinson,  Jim
Max Planck Research Group Music Cognition and Action, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Institute of Cognitive Neuroscience, University College London, United Kingdom;

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Prinz,  Wolfgang
Department Psychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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引用

Stadler, W., Springer, A., Parkinson, J., & Prinz, W. (2012). Movement kinematics affect action prediction: Comparing human to non-human point-light actions. Psychological Research, 76(4), 395-406. doi:10.1007/s00426-012-0431-2.


引用: https://hdl.handle.net/11858/00-001M-0000-000F-E80F-1
要旨
The influence of movement kinematics on the accuracy of predicting the time course of another individual’s actions was studied. A human point-light shape was animated with human movement (natural condition) and with artificial movement that was more uniform regarding velocity profiles and trajectories (artificial condition). During brief occlusions, the participants predicted the actions in order to judge after occlusion whether the actions were continued coherently in time or shifted to an earlier or later frame. Error rates and reaction times were increased in the artificial compared to the natural condition. The findings suggest a perceptual advantage for movement with a human velocity profile, corresponding to the notion of a close interaction between observed and executed movement. The results are discussed in the framework of the simulation account and alternative interpretations are provided on the basis of correlations between the velocity profiles of natural and artificial movements with prediction performance.