Predicting the outcome of observed actions is fundamental for efficient interpersonal interactions. This is evident in interceptive sports, where predicting the future ball trajectory could make apart success and fail. We quantitatively assessed the predictive abilities of non-trained adults intercepting thrown balls in immersive virtual reality. Participants performed better when they could see the complete throwing action in addition to the ball flight, and they were able to move toward the correct direction when the ball flight was occluded. In both cases, performance varies with the individual motor style of the thrower. These results prove that humans can effectively predict the unfolding of complex full-body actions, with no need to extensively practice them, and that such predictions are exploited online to optimize interactive motor performance. This suggests that humans hold a functional knowledge of how actions recurrent in the human motor repertoire map into the changes brought to the environment.

Maselli, A., De Pasquale, P., Lacquaniti, F., D'Avella, A. (2022). Interception of virtual throws reveals predictive skills based on the visual processing of throwing kinematics. ISCIENCE, 25(10), 105212 [10.1016/j.isci.2022.105212].

Interception of virtual throws reveals predictive skills based on the visual processing of throwing kinematics

Lacquaniti, Francesco
Conceptualization
;
2022

Abstract

Predicting the outcome of observed actions is fundamental for efficient interpersonal interactions. This is evident in interceptive sports, where predicting the future ball trajectory could make apart success and fail. We quantitatively assessed the predictive abilities of non-trained adults intercepting thrown balls in immersive virtual reality. Participants performed better when they could see the complete throwing action in addition to the ball flight, and they were able to move toward the correct direction when the ball flight was occluded. In both cases, performance varies with the individual motor style of the thrower. These results prove that humans can effectively predict the unfolding of complex full-body actions, with no need to extensively practice them, and that such predictions are exploited online to optimize interactive motor performance. This suggests that humans hold a functional knowledge of how actions recurrent in the human motor repertoire map into the changes brought to the environment.
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore BIO/09
English
Biological sciences
behavioral neuroscience
neuroscience
Maselli, A., De Pasquale, P., Lacquaniti, F., D'Avella, A. (2022). Interception of virtual throws reveals predictive skills based on the visual processing of throwing kinematics. ISCIENCE, 25(10), 105212 [10.1016/j.isci.2022.105212].
Maselli, A; De Pasquale, P; Lacquaniti, F; D'Avella, A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/308655
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