Understanding Personality through Patterns of Daily Socializing: Applying Recurrence Quantification Analysis to Naturalistically Observed Intensive Longitudinal Social Interaction Data

Toward a dynamic approach of person perception at zero acquaintance: Applying recurrence quantification analysis to thin slices

Thin-slice methodology has provided us with abundant behavioral streams that self-reported measures would fail to capture, but traditional analytical paradigms in social and personality psychology cannot fully capture the temporal trajectories of person perception at zero acquaintance. At the same time, empirical investigations into how persons and situations jointly predict behavior enacted in situ are scarce, despite the importance of examining real-world behavior to understand any phenomenon of interest. To complement existing theoretical models and analyses, we propose the dynamic latent state-trait model blending dynamical systems theory and person perception. We present a data-driven case study using thin-slice methodology to demonstrate the model. This study provides direct empirical support for the proposed theoretical model on person perception at zero acquaintance highlighting the target, the perceiver, the situation, and time. The results of the study demonstrate that dynamical systems theory approaches can be leveraged to provide information about person perception at zero acquaintance above and beyond that of more traditional approaches. CLASSIFICATION CODE: 3040 (Social Perception & Cognition).

Network Analysis of Time Series: Novel Approaches to Network Neuroscience

In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as "network neuroscience." In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, "intrinsic manifold" from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.