Dynamical Correlation: A New Method to Quantify Synchrony - PowerPoint Presentation

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Slide1

Dynamical Correlation: A New Method to Quantify Synchrony

Siwei

Liu

1,

Yang Zhou

1

, Richard Palumbo

2

, & Jane-Ling Wang

1

1

UC Davis;

2

University of Rhode IslandSlide2

Motivating Study

Physiological synchrony between romantic partners during nonverbal conditions

30

Minutes Total

15 Minutes

Face to Face

15 Minutes

Back to Back

N=16Slide3

Electrodermal Activity (EDA) from Two CouplesSlide4
Slide5

Multilevel Modeling?

Assumes a universal model

Random effects are normally distributed

Violations lead to biased estimates

Difficult to converge with small sample size - (Bell et al., 2008, 2010; Maas

& Hox, 2004, 2005)

Within Dyad

Between DyadSlide6
Slide7

Time Series Analysis?

Vector Autoregressive Model (VAR)

Cointegration

Relation

y

1

~

I(1)

y

2

~ I(1)

y

1

-2*y

2

~ I(0)

StationaritySlide8

Dynamical Correlation

Functional data analysis (Ramsay & Silverman, 2005)

Longitudinal data

: Observations taken from a set of smooth curves or functions, which are realizations of an underlying stochastic process

Functional Regression

Functional principle component analysisFunctional clusteringDynamical correlation Similarity in the shape of two curves, range = [-1,1]

Nonparametric – no functional form neededNo assumption on distribution of subject-level estimatesPopulation-level inferencesSlide9

Dynamical Correlation between X(t) and Y(t)

Define the standardized curve

where

Dynamical correlation is defined as:

Compare to Pearson correlation:

(

1

)

(

2

)Slide10

Simulation Example ISlide11

Simulation Example IISlide12

Synchrony in EDA

Back-to-Back Condition

Face-to-Face Condition

Random pairs in face-to-face condition

Romantic partners synchronized their EDA during nonverbal interactions, but only when they were able to see each other.

Synchrony was not due to shared experience.Slide13

Extensions

Other variables

Parent-child interactions

Positive affect and negative affect

Derivatives and lagsLinks to DFMLinks to Granger causality

Matrix of dynamical correlationPrincipal component analysisLimitationsRequire intensive dataNo true subject-level estimates

 Functional multilevel model (Li, Root, &

Shiffman, 2006)