Multilevel behavioral synchronization in a joint tower-building task

Human to Human sensorimotor interaction can only be fully understood by modeling the patterns of bodily synchronization and reconstructing the underlying mechanisms of optimal cooperation. We designed a tower-building task to address such a goal. We recorded upper body kinematics of dyads and focused on the velocity profiles of the head and wrist. We applied Recurrence Quantification Analysis to examine the dynamics of synchronization within, and across the experimental trials, to compare the roles of leader and follower. Our results show that the leader was more auto-recurrent than the follower to make his/her behavior more predictable. When looking at the cross-recurrence of the dyad, we find different patterns of synchronization for head and wrist motion. On the wrist, dyads synchronized at short lags, and such pattern was weakly modulated within trials, and invariant across them. Head motion instead, synchronized at longer lags and increased both within and between trials: a phenomenon mostly driven by the leader. Our findings point at a multilevel nature of human to human sensorimotor synchronization, and may provide an experimentally solid benchmark to identify the basic primitives of motion, which maximize behavioral coupling between humans and artificial agents

Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa

Recurrence quantification analysis is a widely used method for characterizing patterns in time series. This article presents a comprehensive survey for conducting a wide range of recurrence-based analyses to quantify the dynamical structure of single and multivariate time series, and to capture coupling properties underlying leader-follower relationships. The basics of recurrence quantification analysis (RQA) and all its variants are formally introduced step-by-step from the simplest auto-recurrence to the most advanced multivariate case. Importantly, we show how such RQA methods can be deployed under a single computational framework in R using a substantially renewed version our crqa 2.0 package. This package includes implementations of several recent advances in recurrence-based analysis, among them applications to multivariate data, and improved entropy calculations for categorical data. We show concrete applications of our package to example data, together with a detailed description of its functions and some guidelines on their usage.Comment: Describes R package crqa v. 2.0: https://cran.r-project.org/web/packages/crqa

Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa

Recurrence quantification analysis is a widely used method for characterizing patterns in time series. This article presents a comprehensive survey for conducting a wide range of recurrence-based analyses to quantify the dynamical structure of single and multivariate time series, and to capture coupling properties underlying leader-follower relationships. The basics of recurrence quantification analysis (RQA) and all its variants are formally introduced step-by-step from the simplest autorecurrence to the most advanced multivariate case. Importantly, we show how such RQA methods can be deployed under a single computational framework in R using a substantially renewed version our crqa 2.0 package. This package includes implementations of several recent advances in recurrence-based analysis, among them applications to multivariate data, and improved entropy calculations for categorical data. We show concrete applications of our package to example data, together with a detailed description of its functions and some guidelines on their usage

Personality Expression in Body Motion Dynamics:Enactive, Embodied and Complex Systems Perspectives

We explored personality expression through body motion using enactive/complex systems perspectives. We invited 105 adults (aged 18-33, 70% women) to talk for 15-minutes about three self-referencing topics (introduction, bodily perception/sensory life, socio-emotional life). A video frame-by-frame differentiation method provided time-series to perform Recurrence Quantification Analysis (RQA), extracting four measures (Determinism/Entropy/Laminarity/MeanLine). Multilevel models linked Big-Five traits (IPIP-NEO-120) to embodied dynamics. Neuroticism predicted lower determinism and fluctuating dynamics when talking about bodily perception/sensory life and socioemotional life; less complexity and stability when talking about socioemotional life, and post-task negative affect. Extraversion predicted regular/deterministic dynamics when talking about bodily perception/sensory life. Conscientiousness predicted less deterministic and more variability. Agreeableness predicted low post-task negative affect. The results are discussed integrating enactive/complexity, and personality perspectives