TY - GEN
T1 - An Attempt to Visualize and Quantify Speech-Motion Coordination by Recurrence Analysis
T2 - 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019
AU - Kodama, Kentaro
AU - Shimizu, Daichi
AU - Sekine, Kazuki
N1 - Funding Information:
We would like to thank Prof. Rick Dale for providing us with meaningful advice and the useful R code to conduct the joint recurrence analyses. We are also grateful to two Japanese professional rappers, Darthreider and TKdakurobuchi, for collaborating with us and participating in our experiment.
Publisher Copyright:
© Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019.All rights reserved.
PY - 2019
Y1 - 2019
N2 - Recently, cognitive science researchers have revealed that human cognition involves the body and is a kind of self-organization phenomenon emerging from dynamic interaction across body-brain-environment. Some of the data obtained from such cognitive, behavioral, or physiological activities are often complicated in terms of non-stationarity and nonlinearity. Researchers have proposed several analytical tools and frameworks. Recurrence analysis is one of the nonlinear data analyses developed in nonlinear dynamics. It has been applied to various research fields, including cognitive science, for language (categorical) data or motion (continuous) data. However, most previous studies have applied recurrence methods individually to categorical or continuous data. We aimed to integrate these methods to investigate the relationship between speech (categorical) and motion (continuous) directly. To do so, we added temporal information (a time stamp) to categorical data and applied the joint recurrence analysis methods to visualize and quantify speech-motion coordination during a rap performance. Our pilot study suggested the possibility of visualizing and quantifying it.
AB - Recently, cognitive science researchers have revealed that human cognition involves the body and is a kind of self-organization phenomenon emerging from dynamic interaction across body-brain-environment. Some of the data obtained from such cognitive, behavioral, or physiological activities are often complicated in terms of non-stationarity and nonlinearity. Researchers have proposed several analytical tools and frameworks. Recurrence analysis is one of the nonlinear data analyses developed in nonlinear dynamics. It has been applied to various research fields, including cognitive science, for language (categorical) data or motion (continuous) data. However, most previous studies have applied recurrence methods individually to categorical or continuous data. We aimed to integrate these methods to investigate the relationship between speech (categorical) and motion (continuous) directly. To do so, we added temporal information (a time stamp) to categorical data and applied the joint recurrence analysis methods to visualize and quantify speech-motion coordination during a rap performance. Our pilot study suggested the possibility of visualizing and quantifying it.
KW - Quantification
KW - Rap
KW - Recurrence Analysis
KW - Speech-Motion Coordination
KW - Visualization
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M3 - Conference contribution
AN - SCOPUS:85105201014
T3 - Proceedings of the 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019
SP - 2031
EP - 2037
BT - Proceedings of the 41st Annual Meeting of the Cognitive Science Society
PB - The Cognitive Science Society
Y2 - 24 July 2019 through 27 July 2019
ER -