How romantic partners interact with each other during a conflict influences how they feel at the end of the interaction and is predictive of whether the partners stay together in the long term. Hence understanding the emotions of each partner is important. Yet current approaches that are used include self-reports which are burdensome and hence limit the frequency of this data collection. Automatic emotion prediction could address this challenge. Insights from psychology research indicate that partners' behaviors influence each other's emotions in conflict interaction and hence, the behavior of both partners could be considered to better predict each partner's emotion. However, it is yet to be investigated how doing so compares to only using each partner's own behavior in terms of emotion prediction performance. In this work, we used BERT to extract linguistic features (i.e., what partners said) and openSMILE to extract paralinguistic features (i.e., how they said it) from a data set of 368 German-speaking Swiss couples (N = 736 individuals) who were videotaped during an 8-minutes conflict interaction in the laboratory. Based on those features, we trained machine learning models to predict if partners feel positive or negative after the conflict interaction. Our results show that including the behavior of the other partner improves the prediction performance. Furthermore, for men, considering how their female partners spoke is most important and for women considering what their male partner said is most important in getting better prediction performance. This work is a step towards automatically recognizing each partners' emotion based on the behavior of both, which would enable a better understanding of couples in research, therapy, and the real world.

Many processes in psychology are complex, such as dyadic interactions between two interacting partners (e.g., patient-therapist, intimate relationship partners). Nevertheless, many basic questions about interactions are difficult to investigate because dyadic processes can be within a person and between partners, they are based on multimodal aspects of behavior and unfold rapidly. Current analyses are mainly based on the behavioral coding method, whereby human coders annotate behavior based on a coding schema. But coding is labor-intensive, expensive, slow, focuses on few modalities, and produces sparse data which has forced the field to use average behaviors across entire interactions, thereby undermining the ability to study processes on a fine-grained scale. Current approaches in psychology use LIWC for analyzing couples' interactions. However, advances in natural language processing such as BERT could enable the development of systems to potentially automate behavioral coding, which in turn could substantially improve psychological research. In this work, we train machine learning models to automatically predict positive and negative communication behavioral codes of 368 German-speaking Swiss couples during an 8-minute conflict interaction on a fine-grained scale (10-seconds sequences) using linguistic features and paralinguistic features derived with openSMILE. Our results show that both simpler TF-IDF features as well as more complex BERT features performed better

Conflict communication represents a basic process for the quality of intimate relationships, which is fundamental to well-being over the lifespan. This study investigates the temporal unfolding of different relational perspectives during a conflict situation by monitoring pronoun use in young, middleaged, and old couples within the theoretical framework of Gottman's phases of conflict. Our results reveal different trajectories of "I"-, "you"-, and "we"-talk over a conflict conversation in both partners. These trajectories differ between females and males. Furthermore, "you"-talk and "we"-talk differed among the age groups over time. Understanding the temporal dynamics of marital communi-cation as reflected by pronoun use seems promising for a better understanding of conflict related processes in couples over the lifespan. MONITORING PRONOUNS IN CONFLICTS 2 AbstractConflict communication represents a basic process for the quality of intimate relationships, which is fundamental for well-being over the lifespan. In this study the temporal unfolding of different relational perspectives during a conflict situation is investigated by monitoring pronoun use in young, middle-aged and old couples within the theoretical framework of Gottman's phases of conflict. Our results reveal different trajectories of "I"-, "you"-, and "we"-talk over a conflict conversation in both partners. These trajectories differ between females and males. Furthermore, "you"-talk and "we"-talk differed between age groups over time. Understanding the temporal dynamics of marital communication as reflected by pronoun use seems promising for a better understanding of conflict related processes in couples over the lifespan.

In dyadic interaction, a verbal focus on one individual (“you-talk,” “I-talk”), rather than on the couple (“we-talk”) has predominantly been linked to dysfunctional relationship processes. However, context differences in these links have not yet been systematically examined. Is it functional to asymmetrically focus on one partner during support interactions but problematic during conflict? Does a high level of couple-focus represent a resource across contexts? In this preregistered study, we investigated dyad-level pronoun use (we-/I-/you-talk) and their link to situational relationship functioning (SRF) across three interaction tasks (one conflict, two dyadic coping tasks) within couples ( N = 365). More specifically, we examined associations of couple-means, i.e. pronoun use as a shared resource/vulnerability between partners, and couple-differences, i.e. functional/dysfunctional asymmetric pronoun use with observed interaction positivity and relationship climate. Results revealed both context differences and similarities. Asymmetric partner-focus (i.e. you-talk) was dysfunctional in conflict, whereas asymmetric partner- and self-focus (i.e., you-talk/I-talk; focus on the stressed partner) were functional in dyadic coping. Beyond asymmetry, you-talk (couple-mean) showed consistent negative associations with SRF in all tasks studied. We-talk (couple-mean) was positively linked to SRF, but only in conflict interactions. In conflict, couple-focus thus represented a shared resource that can buffer from dysfunctional conflict interaction characterized by partner-focus. In line with conceptual frameworks, the dyadic coping results emphasize the importance of focusing on the partner in need. The study corroborates the prospect of pronoun use as a context-specific indicator of relationship functioning. Gender differences, implications for future research and possible interventions are discussed.

Objectives This study investigated linear and nonlinear age effects on language use with speech samples that were representative of naturally occurring conversations. Method Using a corpus-based approach, we examined couples’ conflict conversations in the laboratory. The conversations, from a total of 364 community-dwelling German-speaking heterosexual couples (aged 19–82), were videotaped and transcribed. We examined usage of lower-frequency words, grammatical complexity, and utterance of filled pauses (e.g., äh [“um”]). Results Multilevel models showed that age effects on the usage of lower-frequency words were nonsignificant. Grammatical complexity increased until middle age (i.e., 54) and then declined. The utterance of filled pauses increased until old age (i.e., 70) and then decreased. Discussion Results are discussed in relation to cognitive aging research.