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The current study aims to explore how interdependence affects between-group cooperation or disintegration. In today's globalized world, countries are redefining their characters in the global industrial chain to take the lead in the new technology revolution, based on increased cross-country economic activities and artificial intelligence. The intensive leadership competition has brought about conflicts between countries. In particular, there have been lots of conflicts between great powers, such as the US-China trade war and the Russia-EU gas conflict. The conflict between great powers induces individuals' ingroup solidarity(e.g., nationalism and populism) and prejudice towards outgroup (e.g., discrimination). However, these countries also have a high degree of economic interdependence. Why do leadership competition make individuals support to break the established intergroup cooperation, and how does intergroup interdependence affect their personal decisions? The present study is based on previous research investigating the preemptive strike, and we will adopt the loss aversion theory(Tversky & Kahneman,1991) to explain it. Moreover, the interdependence theory (Gerpott et al., 2017; Columbus et al., 2021) is also included. We used two of five dimensions of interdependence(power and future interdependence, Kelley et al., 2003), which may help us learn how loss aversion influences group interdependence and preemptive strike. This study applies the interdependence theory to the topic of between-group conflict. This research could offer essential insights into the role of loss aversion in intergroup conflict.

Supplemental material, sj-doc-1-spp-10.1177_19485506231172330 for Identity and Institutions as Foundations of Ingroup Favoritism: An Investigation Across 17 Countries by Giuliana Spadaro, James H. Liu, Robert Jiqi Zhang, Homero Gil de Zúñiga and Daniel Balliet in Social Psychological and Personality Science

This is a pre-registration for the project " Changes in Americans’ Levels of Cooperation Over Time: A Cross-Temporal Meta-Analysis ", including an overview of the research questions and hypotheses, as well as the methods and analysis plan that will be used in the study.

Why and how people cooperate with specific others – even in the presence of the possibility of being exploited – has received intensive research for several decades (Spadaro et al., 2020). Research findings from biology, sociology, and psychology demonstrate that cooperation is far more widespread than initially suggested by evolutionary theory (Axelrode, 1984; 2006). Early applications of evolutionary theory to understand human cooperation emphasized that people should preferentially cooperate with genetically related others (i.e., kin). Yet, humans cooperate with many non-genetically related others – even those whom they have no prior knowledge about and potential for future interaction. In recent years, partner selection has been proposed as one of the important mechanisms for facilitating cooperation (Aktipis, 2004). Partner selection provides individuals with the freedom to choose partners with whom they can initiate cooperative relations, which may enable the ability for cooperators to assort and avoid costly interactions with non-cooperators, thereby contributing to the evolution of cooperation (Barclay, 2016; Eisenbruch & Roney, 2017; Slomin & Garbarino, 2008; Smith & Apicella, 2020). However, for partner selection to promote cooperation, individuals need to be able to successfully identify social partners that are willing and able to work together to ensure mutual benefit (Barclay, 2013; Chen & Goh, 2019; Li, Yang, Wu, & Wang, 2014; Noë & Hammerstein, 1994). To be effective, partner selection needs to involve “selective intelligence’’ (i.e., accurate discrimination between valuable and non-valuable partners), and be sensitive to individual differences (i.e., what is a good choice for one individual, might not be for another; Ai, Zhong, Yan & Gu, 2014) and situational differences (i.e., what is a valuable choice in one setting might not be the best choice in a different setting; Clark, Green & Simons, 2019). However, there remain open questions about a) Which trade-offs do people make when selecting partners for situations that afford the expression of different partner traits, such as warmth (i.e., morality and sociability) and competence; and b) Do individuals adjust their partner choices based on the demand of different tasks. Most social interactions that people experience afford access to social signals that can be used to make inferences about (hidden) partner’s qualities, such as traits, goals, and intentions (Tamir & Thornton, 2018). Several theories have been developed to explain how individuals use these (social) signals to make inferences about other’s traits (e.g., social signaling theory; Conelly, Certo, Ireland, & Reutzel, 2011; Spence, 1973; multilayered framework of social cognition; Tamir & Thorton, 2018). Social signaling theory proposes that individuals (i.e., senders) engage in activities or behavior to persuade the other (i.e., receiver) they possess some unobservable quality, such as trustworthiness, friendliness, competence, etc. (Gambetta & Przepiorka, 2014). Meaning, if individuals (i.e., senders) are conveying information about their traits, intents or goals, others (i.e., receivers) should be able to use these social signals to infer about partner’s hidden qualities (e.g., “Are they trustworthy?”). This process can be viewed as a signal detection problem that our ancestors repeatedly faced in the ancestral past (Oda, Naganawa, Yamauchi, Yagamata, & Matsumoto-Oda, 2009; Tooby & Cosmides, 1996), where optimally solving for this problem would result in better partner selection. To identify signals indicative of one’s traits, studies have used different stimuli, such as: (a) pictures (Barclay & Lalumiere, 2006; Jaeger et al., preprint; Olivola, Funk & Todorov, 2014; Todorov, Baron & Oosterhof, 2008), (b) video- and audio-recordings (Bartlett, Edmunds, Belpaeme, Thill & Lemaignan, 2019; Oda et al., 2009), (c) interactive face-to-face (fTf) or video meetings, and (d) behavior in economic games (DeSteno et al., 2012). Past research has found that several partner’s verbal and non-verbal behaviors correlate with perceived warmth, including smiling, maintaining eye contact, body orientation towards the other, backchanneling (i.e. visual or vocal signals communicating social presence, paying attention or listening), keeping close proximity and mirroring (i.e. copying the behavior of an interaction partner). Compared to warmth, competence is more difficult to infer from non-task related behaviors, as they are more salient in task-oriented settings. It is assumed that individuals might use signals related to power, confidence, and dominance as an approximation of one’s competence. However, until now research trying to automatically analyze multimodal data to predict one’s person perceptions on warmth and competence is non-existent. Hence, another aim of this study is to (a) examine which cues are most predictive of person perception (e.g., warmth, competence), (b) if these signals lead to accurate trait judgments of interaction partners. Social Signal Processing and Partner Selection The field of social signal processing aims to understand and model social interactions by automatically capturing social signals of different modalities in order to predict decisions or behaviors. The field of social signal processing two outstanding benefits to the science of social interactions; (1) addressing limitations of previous qualitative (i.e., observational) research, by decreasing the subjectivity linked to subjective biases and differences between expert evaluations (Nasir et al., 2017), and (b) introducing machine learning solutions to understanding social behavior. The latter benefit is visible in recent research where computational approaches are being used to predict future decisions and behavior (Joel et al., 2017; Nasir et al., 2017). Currently, there is no work that uses a social signaling approach to predict partner choice or collaborative behavior based on multi-modal direct and indirect information collected prior, during and after an interaction. Therefore, an additional aim of this project is to develop a model that uses subjective information about each participant (i.e. personality traits) and objective information extracted during interactions between participants (i.e. social signals) to predict: (a) partner selection for conversation (approach/avoidance behavior), (b) partner selection for a specific task, (c) (individual) cooperative behavior, (d) mutual outcome, (e) collaborative performance, and (f) intent for future engagement.

We investigate how personality traits are activated in situations per the domain-specific situational affordance (DSSA; De Vries et al., 2016) and how people select themselves into these situations with a diary survey study.

Pre-registration of the meta-analysis

This document outlines the objectives, hypotheses, design and analytic strategy of an investigation on age differences in perceived personal costs and prosocial behavior during the COVID-19 pandemic.

This project examined whether anger and disgust vary depending on the costs imposed by others' moral violations, and whether they differentially correspond with direct and indirect aggressive tendencies. Findings based on the datasets included in this project are described in Molho, Tybur, Güler, Balliet, and Hofmann (in press), Psychological Science.

Initially formulated by Mischel (1977), the situational strength hypothesis has had an important influence on the person-situation debate. The situational strength hypothesis posits that the way personality translates into behavior depends on the strength of the situation. Specifically, strong situations – compared to weak situations – restrict behavioral variance and thus prevent personality from being expressed in behavior. Lately, however, researchers have started to question the restricted behavioral variance assumption, noting that behavioral variance does not seem to be constrained to a greater degree in strong compared to weak situations (Keeler et al., 2019). The current meta-analysis takes two approaches to test the situational strength hypothesis using economic game data. Specifically, we examine whether the situations hypothesized to be stronger based on theoretical grounds (1) are indeed perceived as having higher situational strength by independent raters and (2) whether there is lower behavioral variance in these situations. This allows us to examine whether empirical tests of situational strength produce restricted variance patterns consistent with Mischel’s hypothesis, that is, whether situations hypothesized and/or perceived as strong, respectively, indeed restrict behavioral variance.