People frequently participate in interdependent tasks (i.e., tasks in which the outcome of one person is reliant on the other person's actions), in which people can behave in ways that benefit others (i.e., cooperate) to achieve mutually beneficial outcomes in daily life. The ability to select appropriate cooperative partners for these tasks is essential to achieve successful outcomes. Yet, little is known about individual partner preferences for interdependent tasks and whether these preferences change in response to situational affordances of the task (i.e., which traits can affect task outcomes). Here, we report four studies (N = 1021) that investigate the relationship between partner preference, person perceptions, and partner selection in interdependent tasks that afford the expression of warmth- or competence-related traits to affect outcomes. Over four studies, participants were randomly assigned to an interdependent task affording for warmth- or competence-related traits, then rated the most important traits in a partner (Study 1–4), evaluated potential partners' warmth and competence (Study 3–4), and selected partners (Study 3–4). Overall, participants strongly prefer warmth-related traits in a partner, but partner preferences also vary depending on task affordance. Specifically, people demonstrated a stronger preference for partner trustworthiness in tasks affording warmth-related traits and preferred highly competent partners in tasks affording competence-related traits. Additionally, preferences for partner traits strengthened the relationship between the perceived partner trait afforded by the situation and partner selection. We discuss these findings in relation to theories of partner selection and cooperation, as well as the implications of these results to develop tools and interventions to help people optimize their partner selections.

Evolutionary theory suggests that partner selection – the ability to identify and prefer-entially interact with individuals willing (warmth) and able (competence) to work towards mutual benefits – is a key driver of cooperative behavior. However, partner selection is complex, requiring the integration of various information, such as impression formation and task affordances. Despite its importance, there is limited research on the effect of these factors on partner selection for cooperative tasks. Thus, this paper investigates how person perceptions (warmth and competence), task affordances, and facial and acoustic nonverbal behavior inform partner selection for cooperative tasks. For this purpose, we asked participants to select partners for a task that either expressed warmth- or competence-related traits. Participants had a 3-minute (online) conversation with up to five individuals, reported their evaluations, selected partners for the task, and then engaged in the task. Results indicate that person perceptions guide partner selection, with each trait being more predictive in relevant tasks. Additionally, we found that the perceptions of warmth, but not competence, can be predicted by facial and acoustic cues during con¬versations. Lastly, we find that in the context of online social interactions, individuals were more cooperative towards selected participants than unselected. We discuss these impli¬cations in the context of the theory of partner selection and offer insights on how these results can be used in future efforts for designing socially intelligent artificial systems that support partner selection decisions.

As intelligent technology and applications have become an integral part of nearly all aspects of people's daily lives, many intelligent systems have been designed to help people navigate the complex space of social interactions. One prominent strategy for such intelligent support is providing meaningful Ad Hoc Interventions (ADI), e.g., through timely notifications. An alternative is Technology-Supported Reflection (TSR), e.g., by offering information about activities in one's past for personal insights. In contrast to straight-up interventions, the aim of the latter strategy is not to directly augment human skills but instead support learning and personal growth over time. However, while TSR has seen widespread interest in applications in some areas, such as physical fitness and mental health, its use for improving human social interactions has not yet been systematically explored. Concretely, it is currently unclear 1) what forms of self-reflection systems intend to support, 2) how their different technological components (e.g., data collection, information integration) are involved in providing support, and 3) what common limitations and design challenges they face. In this article, we present the results of a systematic literature review focusing on these questions to provide a structured foundation for targeted research. Concretely, we identified and analysed a collection of 23 relevant papers, each describing a system deploying TSR to support humans with elements of social interactions.We constructed a framework with a set of features to comprehensively describe and analyze the systems that support self-reflection, including their application domains, how they fit into the existing design framework, how they facilitate learning through reflection, how adaptive they are to individual users, and how they were evaluated. Finally, we propose a direction for designing systems that support individual's social interactions through self-reflection in an adaptive manner.

Enabling computer-based applications to display intelligent behavior in complex social settings requires them to relate to important aspects of how humans experience and understand such situations. One crucial driver of peoples' social behavior during an interaction is the interdependence they perceive, i.e., how the outcome of an interaction is determined by their own and others' actions. According to psychological studies, both the nonverbal behavior displayed by Motivated by this, we present a series of experiments to automatically recognize interdependence perceptions in dyadic face-to-face negotiations using these sources. Concretely, our approach draws on a combination of features describing individuals' Facial, Upper Body, and Vocal Behavior with state-of-the-art algorithms for multivariate time series classification. Our findings demonstrate that differences in some types of interdependence perceptions can be detected through the automatic analysis of nonverbal behaviors. We discuss implications for developing socially intelligent systems and opportunities for future research.