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Conversations between people are where, among other things, stressors are amplified and attenuated, conflicts are entrenched and resolved, and goals are advanced and thwarted. What happens in dyads’ back-and-forth exchanges to produce such consequential and varied outcomes? Although numerous theories in communication and in social psychology address this question, empirical tests of these theories often operationalize conversational behavior using either discrete messages or overall features of the conversation. Dynamic systems theories and methods provide opportunities to examine the interdependency, self-stabilization, and self-organization processes that manifest in conversations over time. The dynamic dyadic systems perspective exemplified by the articles in this special issue (a) focuses inquiry on the turn-to-turn, asynchronous exchange of messages between two partners, (b) emphasizes behavioral patterns within and the structural and temporal organization of conversations, and (c) adapts techniques used in analysis of intensive longitudinal data to identify and operationalize those dynamic patterns. As an introduction to the special issue, this paper describes a dynamic dyadic systems perspective on conversation and discusses directions for future research, such as applications to humancomputer interaction, family communication patterns, health care interventions, and group deliberation. 

Abstract This article articulates conceptual and methodological strategies for studying the dynamic structure of dyadic interaction revealed by the turn-to-turn exchange of messages between partners. Using dyadic time series data that capture partners’ back-and-forth contributions to conversations, dynamic dyadic systems analysis illuminates how individuals act and react to each other as they jointly construct conversations. Five layers of inquiry are offered, each of which yields theoretically relevant information: (a) identifying the individual moves and dyadic spaces that set the stage for dyadic interaction; (b) summarizing conversational units and sequences; (c) examining between-dyad differences in overall conversational structure; (d) describing the temporal evolution of conversational units and sequences; and (e) mapping within-dyad dynamics of conversations and between-dyad differences in those dynamics. Each layer of analysis is illustrated using examples from research on supportive conversations, and the application of dynamic dyadic systems analysis to a range of interpersonal communication phenomena is discussed. 

This study demonstrates how sequence analysis, which is a method for identifying common patterns in categorical time series data, illuminates the nonlinear dynamics of dyadic conversations by describing chains of behavior that shift categorically, rather than incrementally. When applied to interpersonal interactions, sequence analysis supports the identification of conversational motifs, which can be used to test hypotheses linking patterns of interaction to conversational antecedents or outcomes. As an illustrative example, this study evaluated 285 conversations involving stranger, friend, and dating dyads in which one partner, the discloser, communicated about a source of stress to a partner in the role of listener. Using sequence analysis, we identified three five-turn supportive conversational motifs that had also emerged in a previous study of stranger dyads: discloser problem description, discloser problem processing, and listener-focused dialogue. We also observed a new, fourth motif: listener-focused, discloser questioning. Tests of hypotheses linking the prevalence and timing of particular motifs to the problem discloser’s emotional improvement and perceptions of support quality, as moderated by the discloser’s pre-interaction stress, offered a partial replication of previous findings. The discussion highlights the value of using sequence analysis to illuminate dynamic patterns in dyadic interactions.