TY - JOUR
T1 - Interception of virtual throws reveals predictive skills based on the visual processing of throwing kinematics
AU - Maselli, Antonella
AU - De Pasquale, Paolo
AU - Lacquaniti, Francesco
AU - d'Avella, Andrea
N1 - Funding Information:
The authors would like to thank Aishwar Dhawan for his help in the preparation of the virtual characters used in the Unity project, and Maura Mezzetti for her help with the statical analysis. This work was supported by European Union’s Horizon 2020 research and innovation programme under grant agreement No 644727 .
Funding Information:
The authors would like to thank Aishwar Dhawan for his help in the preparation of the virtual characters used in the Unity project, and Maura Mezzetti for her help with the statical analysis. This work was supported by European Union's Horizon 2020 research and innovation programme under grant agreement No 644727. A.M. and A.dA. conceived and designed the experiment. A.M. implemented the Unity project and the experimental setup. A.M. and P.DP. conducted the experiments. P.DP. and A.M. analyzed the data. All authors discussed and interpreted the results. A.M. wrote the article and all authors provided inputs to its final version. The authors declare no competing interests.
Publisher Copyright:
© 2022 The Authors
PY - 2022/10/21
Y1 - 2022/10/21
N2 - 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.
AB - 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.
KW - behavioral neuroscience
KW - Biological sciences
KW - neuroscience
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U2 - 10.1016/j.isci.2022.105212
DO - 10.1016/j.isci.2022.105212
M3 - Article
AN - SCOPUS:85139858309
SN - 2589-0042
VL - 25
JO - iScience
JF - iScience
IS - 10
M1 - 105212
ER -