Exploring the antecedents that affect the team innovation performance can better promote the organization to research the potential factors to enhance the organizational innovation competitiveness. Drawing upon information processing theory, we develop a moderated mediation model to examine the relationship between team pro-social rule breaking and team innovation performance. A three-wave field study is constructed from two large manufacturing enterprises from 82 team leaders and their 382 subordinates in Shanghai, China. The results reveal that team pro-social rule breaking is positively related to team innovation performance through team reflexivity. In addition, the relationship between team pro-social rule breaking and team innovation performance via team reflexivity is positive only when team learning orientation is high. The implications, limitations, and future research directions of these findings are discussed.

Massive studies have explored biological motion (BM) crowds processing for their remarkable social significance, primarily focused on uniformly distributed ones. However, real-world BM crowds often exhibit hierarchical structures rather than uniform arrangements. How such structured BM crowds are processed remains a subject of inquiry. This study investigates the representation of structured BM crowds in working memory (WM), recognizing the pivotal role WM plays in our social interactions involving BM. We propose the group-based ensemble hypothesis and test it through a member identification task. Participants were required to discern whether a presented BM belonged to a prior memory display of eight BM, each with distinct walking directions. Drawing on prominent Gestalt principles as organizational cues, we constructed structured groups within BM crowds by applying proximity and similarity cues in Experiments 1 and 2, respectively. In Experiment 3, we deliberately weakened the visibility of stimuli structures by increasing the similarity between subsets, probing the robustness of results. Consistently, our findings indicate that BM aligned with the mean direction of the subsets was more likely to be recognized as part of the memory stimuli. This suggests that WM inherently organizes structured BM crowds into separate ensembles based on organizational cues. In essence, our results illuminate the simultaneous operation of grouping and ensemble encoding mechanisms for BM crowds within WM.

Ingroup favoritism and intergroup discrimination can be mutually reinforcing during social interaction, threatening intergroup cooperation and the sustainability of societies. In two studies (N = 880), we investigated whether promoting prosocial outgroup altruism would weaken the ingroup favoritism cycle of influence. Using novel methods of human-agent interaction via a computer-mediated experimental platform, we introduced outgroup altruism by (i) nonadaptive artificial agents with preprogrammed outgroup altruistic behavior (Study 1; N = 400) and (ii) adaptive artificial agents whose altruistic behavior was informed by the prediction of a machine learning algorithm (Study 2; N = 480). A rating task ensured that the observed behavior did not result from the participant\'s awareness of the artificial agents. In Study 1, nonadaptive agents prompted ingroup members to withhold cooperation from ingroup agents and reinforced ingroup favoritism among humans. In Study 2, adaptive agents were able to weaken ingroup favoritism over time by maintaining a good reputation with both the ingroup and outgroup members, who perceived agents as being fairer than humans and rated agents as more human than humans. We conclude that a good reputation of the individual exhibiting outgroup altruism is necessary to weaken ingroup favoritism and improve intergroup cooperation. Thus, reputation is important for designing nudge agents.

Voluntary participation is a central yet understudied aspect of collaboration. Here, we model collaboration as people\'s voluntary choices between joining an uncertain public goods provisioning in groups and pursuing a less profitable but certain individual option. First, we find that voluntariness in collaboration increases the likelihood of group success via two pathways, both contributing to form more optimistic groups: pessimistic defectors are filtered out from groups, and some individuals update their beliefs to become cooperative. Second, we reconcile these findings with existing literature that highlights the detrimental effects of an individual option. We argue that the impact of an outside individual option on collaboration depends on the \"externality\" of loners - the influence that those leaving the group still exert on group endeavors. Theoretically and experimentally, we show that if collaboration allows for flexible group formation, the negative externality of loners remains limited, and the presence of an individual option robustly aids collaborative success.

Innovation is challenging, and theory and experiments indicate that groups may be better able to identify and preserve innovations than individuals. But innovation within groups faces its own challenges, including groupthink and truncated diffusion. We performed experiments involving a game in which people search for ideas in various conditions: alone, in networked social groups, or in networked groups featuring autonomous agents (bots). The objective was to search a semantic space of 20,000 nouns with defined similarities for an arbitrary noun with the highest point value. Participants (N = 1875) were embedded in networks (n = 125) of 15 nodes to which we sometimes added 2 bots. The bots had 3 possible strategies: they shared a random noun generated by their immediate neighbors, or a noun most similar from among those identified, or a noun least similar. We first confirm that groups are better able to explore a semantic space than isolated individuals. Then we show that when bots that share the most similar noun operate in groups facing a semantic space that is relatively easy to navigate, group performance is superior. Simple autonomous agents with interpretable behavior can affect the capacity for creative discovery of human groups.

BACKGROUND: Systematic biases in group decision making (i.e., group biases) may result in suboptimal decisions and potentially harm patients. It is not well known how impaired group decision making in patient care may affect medical training. This study aimed to explore medical residents\' experiences and perspectives regarding impaired group decision making and the role of group biases in medical decision making.
METHODS: This study used a qualitative approach with thematic analysis underpinned by a social constructionist epistemology. Semi-structured interviews of medical residents were conducted at a single internal medicine residency program. Residents were initially asked about their experiences with suboptimal medical decision making as a group or team. Then, questions were targeted to several group biases (groupthink, social loafing, escalation of commitment). Interviews were transcribed and transferred to a qualitative data analysis software. Thematic analysis was conducted to generate major themes within the dataset.
RESULTS: Sixteen interviews with residents revealed five major themes: (1) hierarchical influence on group decision making; (2) group decision making under pressure; (3) post-call challenges in decision making; (4) interactions between teamwork and decision making; and (5) personal and cultural influences in group decision making. Subthemes were also identified for each major theme. Most residents were able to recognize groupthink in their past experiences working with medical teams. Residents perceived social loafing or escalation of commitment as less relevant for medical team decision making.
CONCLUSIONS: Our findings provide unique insights into the complexities of group decision making processes in teaching hospitals. Team hierarchy significantly influenced residents\' experiences with group decision making-most group decisions were attributed to consultants or senior team members, while lower ranking team members contributed less and perceived fewer opportunities to engage in group decisions. Other factors such as time constraints on decision making, perceived pressures from other staff members, and challenges associated with post-call days were identified as important barriers to optimal group decision making in patient care. Future studies may build upon these findings to enhance our understanding of medical team decision making and develop strategies to improve group decisions, ultimately leading to higher quality patient care and training.

\"Felt understanding\" is a crucial determinant of positive interpersonal and intergroup relationships. However, the question of why felt understanding shapes intergroup relations has been neglected. In a pre-registered test of the process in intergroup relations with a sample from East Asia, we manipulated felt understanding (understood versus misunderstood by an outgroup) in an experimental study (N = 476). The results supported the expectation that felt understanding would lead to a more positive intergroup orientation and action intention. The results of parallel mediation analyses showed that felt understanding indirectly predicted intergroup outcomes through felt positive regard, intergroup overlap, and outgroup stereotypes. Furthermore, the results of post-hoc sequential mediation analyses indicated that felt understanding indirectly predicted intergroup outcomes sequentially through felt positive regard and intergroup overlap, followed by outgroup stereotypes.

OBJECTIVE: Problem-Based Learning (PBL) relies on self-directed learning in small groups in the presence of a tutor. While the effectiveness of PBL is often attributed to the dynamics of group function, change in group function over time and factors influencing group function development are less understood. This study aims to explore the development of PBL group function over time to better understand the factors that give rise to high-functioning groups.
METHODS: We examined time-function graphs of group function and conducted semi-structured focus group discussions in 2023 with medical students enrolled in a PBL curriculum. Students reflected on their experiences in four different PBL groups, creating time-function graphs to characterize development of group function over 8-12-week periods. We analyzed graphs and transcripts in a staged approach using qualitative description and direct content analysis, sensitized by two frameworks: Tuckman\'s Stages of Group Development and the Dimensions of PBL Group Function.
RESULTS: Three archetypes of PBL group function development were identified: Slow Shifters, Fast Flippers, and Coasters. (1) Slow Shifters were characterized by a complex and extended pattern of growth consistent with Tuckman\'s model, typically occurring amongst inexperienced groups, or groups faced with a novel task. (2) Fast Flippers were characterized by abrupt state changes in group function arising from internal or external disruptions. (3) Coasters were characterized by plateaus, where maintenance of group function was a frequently cited challenge. Abrupt changes and plateaus occurred more among mature groups and groups with significant PBL experience.
CONCLUSIONS: PBL group function varies over time in 3 different patterns. Classic Tuckman\'s stages are apparent among inexperienced groups, or groups facing novel tasks, whereas experienced groups often face abrupt change or plateaus. PBL educators and students should consider the need for novelty and disruption in more experienced groups to incite growth.

Groups often outperform individuals in problem-solving. Nevertheless, failure to critically evaluate ideas risks suboptimal outcomes through so-called groupthink. Prior studies have shown that people who hold shared goals, perspectives, or understanding of the environment show similar patterns of brain activity, which itself can be enhanced by consensus-building discussions. Whether shared arousal alone can predict collective decision-making outcomes, however, remains unknown. To address this gap, we computed interpersonal heart rate synchrony, a peripheral index of shared arousal associated with joint attention, empathic accuracy, and group cohesion, in 44 groups (n = 204) performing a collective decision-making task. The task required critical examination of all available information to override inferior, default options and make the right choice. Using multidimensional recurrence quantification analysis (MdRQA) and machine learning, we found that heart rate synchrony predicted the probability of groups reaching the correct consensus decision with >70% cross-validation accuracy-significantly higher than that predicted by the duration of discussions, subjective assessment of team function or baseline heart rates alone. We propose that heart rate synchrony during group discussion provides a biomarker of interpersonal engagement that facilitates adaptive learning and effective information sharing during collective decision-making.

Bias in advanced heart failure therapy allocation results in inequitable outcomes for minoritized populations. The purpose of this study was to examine how bias is introduced during group decision-making with an interprofessional team using Breathett\'s Model of Heart Failure Decision-Making. This was a secondary qualitative descriptive analysis from a study focused on bias in advanced heart failure therapy allocation. Team meetings were recorded and transcribed from four heart failure centers. Breathett\'s Model was applied both deductively and inductively to transcripts (n = 12). Bias was identified during discussions about patient characteristics, clinical fragility, and prior clinical decision-making. Some patients were labeled as \"good citizens\" or as adherent/non-adherent while others benefited from strong advocacy from interprofessional team members. Social determinants of health also impacted therapy allocation. Interprofessional collaboration with advanced heart failure therapy allocation may be enhanced with the inclusion of patient advocates and limit of clinical decision-making using subjective data.