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The Pearson correlation measures how two continuous signals co-vary over time and indicate the linear relationship as a number between -1 (negatively correlated) to 0 (not correlated) to 1 (perfectly correlated). Time Lagged Cross Correlation (TLCC) & Windowed TLCC.

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To follow along, feel free to download the sample extracted face data and the Jupyter notebook containing all the example codes. To illustrate, the metrics are calculated using sample data in which smiling facial expressions were extracted from a video footage of two participants engaging in a 3 minute conversation (screenshot below). In this article, I survey the pros and cons of some of the most common synchrony metrics and measurement techniques including the Pearson correlation, time lagged cross correlation (TLCC) and windowed TLCC, dynamic time warping, and instantaneous phase synchrony. However, the term synchrony can take on many meanings as there are various ways to quantify synchrony between two signals. Synchrony between individuals has been observed in numerous domains including bodily movement ( Ramseyer & Tschacher, 2011), facial expressions ( Riehle, Kempkensteffen, & Lincoln, 2017), pupil dilations ( Kang & Wheatley, 2015), and neural signals ( Stephens, Silbert, & Hasson, 2010). In psychology, synchrony between individuals can be an important signal that provides information about the social dynamics and potential outcomes of social interactions.

Airplanes flying in synchrony, photo by Gabriel Gusmao on Unsplash

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