Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics.

Area of Science:

• Social interaction dynamics
• Dynamical systems theory
• Neuroscience and behavioral science

Background:

• Social interactions involve complex coordination across behaviors, speech, and neurophysiology.
• Traditional aggregate measures of coordination may obscure dynamic, multiscale changes over time.
• Understanding temporal variations in coordination is crucial for social interaction research.

Purpose of the Study:

• To introduce and demonstrate windowed multiscale synchrony as an advanced method for analyzing social coordination.
• To move beyond aggregate measures and capture the temporal evolution of coordination at different scales.
• To provide new tools for investigating the mechanisms and functions of interpersonal synchrony.

Main Methods:

• Utilized wavelet transform to decompose time series data into multiple frequency scales.
• Quantified phase synchronization at each identified scale to assess coordination strength.
• Applied the windowed multiscale synchrony method to simulated and empirical interpersonal data (physiological and neuromechanical).

Main Results:

• Demonstrated the capability of windowed multiscale synchrony to capture dynamic changes in coordination strength over time.
• Showcased how different scales of coordination fluctuate independently during social interactions.
• Validated the method's applicability to complex interpersonal datasets.

Conclusions:

• Windowed multiscale synchrony offers a more nuanced understanding of social coordination than traditional methods.
• This approach can reveal how coordination patterns shift across different temporal scales during interactions.
• The method holds promise for advancing research on interpersonal synchrony using neurophysiological and behavioral data.