Shared mental models and task decomposition

Analyzing shared and team mental models

In order to meet the increasingly complex demands of design in multidisciplinary teams, designers have to interact and thereby to interweave their mental models (MM). Yet, neither is it clear which content of MM should be shared to perform design tasks effectively, nor is the process of the development of shared mental models (SMM) quite understood. The two studies presented in this article were conducted to gain insights into the cognitive processes of designers working together in a team, and to clarify the impact of SMM on team performance. Process-oriented research strategies were applied to groups of mechanical engineering students and to multidisciplinary project teams in the automobile industry. The results indicated that not the SMM of the whole group but the SMM of subgroups were related to group performance. Moreover, this link to performance is only supported by the SMM about team members' skills (SMMteam) and about the process of interaction (SMMprocess). As a conclusion of the latter result, more attention should be paid to the development of common knowledge about group interaction and team members' abilities in the everyday work life of project teams. In addition, observational data showed that motivational aspects like the feeling of competence should be considered when analysing the influence of SMM. Finally, a conceptualisation of the development and interplay of mental submodels is proposed.

Shared mental models (SMM) and Transactive memory systems (TMS) have been advocated as the main team learning mechanisms. Despite multiple appeals for collaboration, research in both these fields has progressed in parallel and little effort has been made to integrate these theories. The purpose of this study was to test the relationship between SMM and TMS in a field setting and examine their influence on various team effectiveness outcomes such as team performance, team learning, team creativity, team members’ satisfaction and team viability. Contextual factors relevant to an organizational setting were tested and these included team size, tenure, country of origin, team reward and organizational support. Based on responses from 41 teams from 7 industries across two countries (US and India), results indicate that team size, country of origin and team tenure impact team performance and team learning. In addition, team reward and organizational support predicted team viability and satisfaction. Results indicated that TMS components (specialization, coordination and credibility) were better predictors of team outcomes than the omnibus TMS construct. In particular, TMS credibility predicted team performance and creativity while TMS coordination predicted team viability and satisfaction. SMM was measured in two different ways: an average deviation index and a 6-item scale. Both methods resulted in a conceptually similar interpretation although average deviation indices provided slightly better results in predicting effectiveness outcomes. TMS components moderated the relationship between SMM and team outcomes. Team performance was lowest when both SMM and TMS were low. However, contrary to expectations, high levels of SMM did not always result in effective team outcomes

Although shared team mental models are believed to be important to team functioning, substantial interstudy differences in the manner in which mental models are operationalized has impeded progress in this area. We use meta-analysis to cumulate 23 independent studies that have empirically examined shared mental models (SMMs) in relation to team process and performance and test three aspects of measurement as potential moderators: elicitation method, structure representation, and representation of emergence. Results indicate the way in which SMMs are measured and represented at the team level of analysis reveal meaningful distinctions in observed relationships. Specifically, shared mental model operationalization impacts the observed relationship between SMMs and team process; importantly, only methods that model the structure or organization of knowledge are predictive of process. Conversely, while the magnitude of the relationship differed across measurement method, SMMs were positively related to team performance regardless of the manner of operationalization. In summary, knowledge structure is predictive of team process, and both knowledge content and structure are predictive of team performance.

Crew Resource Management (CRM) training focuses on situation awareness, communication skills, teamwork, task allocation, and decision making. More recently, an interest in cognitive skill is beginning to appear in relation to CRM. One aspect of cognitive skill that has been examined in a variety of team domains is the notion of overlapping or mental models among teammates. While a growing amount of evidence on the relationship between shared mental models and team performance exists, only limited research has focused on the role that shared mental models have-in crew resource management. The purpose of this paper is to provide researchers and practitioners an understanding of the shared mental model construct and the role of shared mental models in team performance, as well as to encourage additional research on this topic within the aviation domain. Crew Resource Management and Shared Mental Models Human error is a major factor in aviation accidents. As a result, pilot training has shifted fiom an emphasis on purely technical skills, to a combination of both technical and teamwork skills (Reynolds & Rhoades, 2004). These training programs have a variety of names, but the most common is Crew Resource Management (CRM). is currently required by all 185 International Civil Aviation Orgauization members, is incorporated into each of the US military branches, and is gaining steady support outside aviation in industries as diverse as nuclear power producers and medical practitioners (American Psychological Association, 2005; Flin, Meams, & O ' C o ~ o r , 2002). Typically, three main skill clusters are targeted: communication, team building and workload management. Within these broad categories, however, content may vary to include: adaptability, assertiveness, communication, leadership, mission analysis, situational awareness, forward planning, risk assessment, group dynamics, stress and coping techniques, and how to monitor automated equipment (Naval Education and Training Command, 2003). The Federal Aviation Administration (FAA) (2004) suggests that CRM training focuses on situation awareness, communication skills, teamwork, task allocation, and decision making within a comprehensive m e w o r k of standard operating procedures (SOP) @. l). Under the topic of team building, the FAA notes that 'This topic includes interpersonal relationships and practices. Effective leadership/followership and interpersonal relationships are key concepts to be stressed. Cunicula can also include recognizing and dealing with diverse personalities and operating styles (p. l2). One area not emphasized is team cognition. The goal of this paper is to foster an understanding of the impact of team cognition in promoting effective team work, and to suggest the addition of team cognition, particularly shared mental models, as a focus of CRM. To accomplish this, we begin by discussing the evolution of CRM. Next, we discuss one aspect of team cognition, shared mental models, and how both implicit communication and team performance can be linked to shared mental models. This discussion includes a review of current research on shared mental models, as well as methods of measuring and training such models. We conclude by suggesting that future research and training incorporate shared mental models. Evolution of Kern (200 1) suggested that the roots of can be found in a 1951 U.S. Air Force Inspector General's report which analyzed data fiom 7518 major accidents between 1948 and 195 1, and found that poor teamwork and

: The goal of this project was to advance our understanding of the mental model convergence process. Five technical objectives were established to achieve this goal. Two phases of experimentation were undertaken to address the objectives. The first was a quasi-experiment where teams of students undertaking semester-long projects completed questionnaires at four time periods during the semester. The second were laboratory behavioral simulations where teams of three students completed two simulation sessions on progressively difficult tasks. The objectives and the corresponding findings are summarized. First, a model of mental model convergence was developed by integrating existing theory from literatures such as shared mental models, project teams, group development, information processing, information sharing, and transactive memory. This model depicts a three-phase approach to mental model convergence. Specifically, team members (1) orient themselves to the team and its task, (2) differentiate their own personal mental models from the mental models of their team mates, and (3) integrate these differing perspectives. The second objective was to explore the way in which individually-held mental models converge among team members to become shared. Three methods were examined for measuring mental model convergence to address the third objective. The first two were described in the previous paragraph. The third was examined using data collected at the end of the simulation. Team members completed questionnaires comprised of existing scales designed to measure team perceptions about constructs such as goal clarity, cooperation, and team skills. The interrater agreement among member responses was used to score convergence in team members' mental models. The fourth and fifth objectives were to confirm that multiple mental models function simultaneously and to determine how shared mental models regarding teamwork impact team performance.

We investigated whether a shared mental model is present in elite ice hockey and handball teams. In total, 231 male players participated in the study. Shared mental models were found to exist. Relationships between shared mental models and coaching efforts to develop a general training shared mental model and an opponent-specific model were explored. The relationship between role clarity and shared mental model, general training shared mental model, and opponent-specific model was positive. The shared mental model is a useful construct for analyzing elite team practice and coaching behavior. Coaches and sport psychologists should be aware that establishing a shared mental model in elite teams is essential in facilitating performance.

Improving IS development teams' performance during requirement analysis in project—The perspectives from shared mental model and emotional intelligence

Large corporations increasingly use multinational teams to integrate their global operations. To perform this complex task efficiently, team members need to develop shared mental models (SMMs), i.e. an organized understanding of the knowledge base they are sharing. In multinational teams, the heterogeneity of team members makes SMM formation especially challenging. While previous research has investigated the influence of different diversity factors on SMMs, the impact of language differences has surprisingly been neglected so far. To address this important gap we investigate how different elements of the language barrier impede the formation of different types of SMMs. Based on 84 semi-structured interviews with team leaders, members and senior managers of 15 multinational teams in three German automotive corporations we develop a model showing how pragmatic and paraverbal barriers between team members obstruct SMMs about roles, responsibilities and interaction patterns and how shortcomings in lexical, syntactical and phonetic proficiency impede SMMs about team members' preferences, strengths, weaknesses as well as values and attitudes. These findings integrate linguistic and psychological theories with management studies and complement our understanding of the antecedents of SMMs in multinational teams. This is of crucial importance since SMMs have been established as important prerequisites for team performance.

Mental models are knowledge structures employed by humans to describe, explain, and predict the world around them. Shared Mental Models (SMMs) occur in teams whose members have similar mental models of their task and of the team itself. Research on human teaming has linked SMM quality to improved team performance. Applied understanding of SMMs should lead to improvements in human-AI teaming. Yet, it remains unclear how the SMM construct may differ in teams of human and AI agents, how and under what conditions such SMMs form, and how they should be quantified. This paper presents a review of SMMs and the associated literature, including their definition, measurement, and relation to other concepts. A synthesized conceptual model is proposed for the application of SMM literature to the human-AI setting. Several areas of AI research are identified and reviewed that are highly relevant to SMMs in human-AI teaming but which have not been discussed via a common vernacular. A summary of design considerations to support future experiments regarding Human-AI SMMs is presented. We find that while current research has made significant progress, a lack of consistency in terms and of effective means for measuring Human-AI SMMs currently impedes realization of the concept.

The rise of team-based structures within organisations has prompted increasing research focused at improving team processes, typically with a view to increasing team performance (e.g., Allen & Hecht, 2004). This expanding team-level focus within the organisational behaviour literature has required researchers to consider the complexities surrounding the conceptualisation and measurement of team-based constructs and phenomena (e.g., team satisfaction; team cohesion; team conflict), including how best to aggregate traditionally individual-level phenomena to the team level (Bliese, 2000; Chan, 1998). Increasing recognition of the role of affect in organisations ( e.g., Ashkanasy & Dorris, 2017; Barsade & Gibson, 2007) has similarly led to a growing cohort of researchers conceptually and empirically considering affect-related constructs at the team level (e.g., Ashkanasy, 2003; Barsade & Gibson, 2012; Cote, 2007). One influential stream within this area is research on group affective tone established by George and her colleagues ( e.g., George, 1990, 2002; George & King, 2007). George (1990) provided some of the earliest empirical evidence for group affective tone by demonstrating that individuals in workgroups tend to experience highly similar levels of state affect. The affective tone of a team has been shown to have significant impact on team functioning. A more positive affective tone has been linked with a number of advantageous team outcomes, including better team cooperation (Barsade, 2002), better coordination (Sy, Cote, & Saavedra, 2005), lower team conflict (e.g., Barsade, 2002), lower absence rates within the team (Mason & Griffin, 2003), and more helping behaviours displayed within the team (Chi, Chung, & Tsai, 2011 ). However, there have also been some counterintuitive findings that suggest the impact of group affective tone on team outcomes is more complex than sometimes theorised. In line with the IPSO model of team effectiveness (Marks, Mathieu, & Zaccaro, 2001) my program of research will consider the interplay of affective input variables of the team (specifically trait affect and emotional intelligence) on the development of group affective tone and discrete emotional tones as an emergent state. I use affect-as­information theory (Schwarz & Clore, 2003) and the emotions-as-social-information model (EASI; Van Kleef, 2009) to guide my propositions regarding the influence of group affective tone on team dynamics ( conflict) and outcomes (team performance and team satisfaction). Finally, my expectations regarding the impact of team conflict on team outcomes are based on Jehn and Bendersky's (2003) contingency theory of the consequences of conflict. My broad research questions are: RQ 1. Under what conditions will team members' positive affect and negative affect converge? RQ2. What are the consequences of group affective tone on team conflict? RQ3. What are the consequences of group affective tone on team performance/ satisfaction? RQ4. To what extent does team emotional intelligence influence the interplay of team conflict and team performance? Three studies were conducted to address these questions. All studies used student samples in order to have a high amount of control over the formation of teams and the tasks they completed. Study 1 involved existing student teams assessed during the completion of a survival decision-making task. It examined the convergence of team members' affect, and whether the consequences of teams' affective tone on experienced conflict and objective performance in the task was dependent on teams' (self-rated) collective emotional intelligence, as well as the role of collective emotional intelligence in determining the effectiveness of team conflict on performance. Study 2 utilised an experimental design of randomly formed university teams, and addressed how the trait affective composition of a team contributed to the affective tone of teams, and whether this link was contingent on teams' self-rated level of emotional intelligence, as well as the impact of collective emotional intelligence and formally imposed display rules on the link between teams' affective tone and performance (both self-rated and objective) in a creative task. Finally, the aim of Study 3 was to take a more fine-grained look at the collective emotions of a team, and investigate the convergence of discrete emotions (e.g., joviality, fear, and hostility) in university teams completing a workplace-based decision-making task, as well as whether the consequences of teams' various emotional tones on experienced conflict and objective performance was dependent on teams' collective emotional intelligence (assessed via a situational judgement test). Results of my program of research have both supported previous research on affective tone and extended knowledge regarding the impact of collective emotional intelligence on team interactions with some counterintuitive findings. In an extension of previous research on affect at the team level, I examined specific emotions and their convergence in short tasks, and demonstrated that specific emotions will have differential influences on team outcomes which are not easily apparent when researchers classify affect as either globally positive or negative in nature. Regarding the role of emotional intelligence in team affectivity, different facets were found to have opposing effects. My research has extended past findings by demonstrating that the awareness facets of emotional intelligence can be harmful to a team's functioning when considering the negative affective tone of the team. When a team is lower in negative affective tone, having high awareness of emotions can be detrimental in terms of both relationship conflict experienced in the team, and objective performance of the team. This finding is in contrast to the majority of affective tone models which predict emotional intelligence will help buffer against the harmful impacts of negative affective tone. However, certain management aspects of emotional intelligence were found to be highly valuable in the interplay between positive affective tone, task conflict, and team performance. Contrary to past theory suggesting the desirability of a highly positive affective tone (e.g., George, 1995), and research demonstrating a simple positive link between positive tone and performance ( e.g., Hmieleski, Cole, & Baron, 2012; Kim & Choi, 2012) my research has challenged the notion that a positive affective tone is universally advantageous. Based on my research, during complex decision-making or creative tasks, teams need to be able to manage their positivity so that it remains functional, rather than making them complacent about their task; providing team-level support for affect-as-information theory (Schwarz & Clore, 2003). The practical implications of my research include the notion that team-level emotional intelligence may be a vital resource for maximising team performance. Managers of teams, in particular, should be aware that a highly positive team atmosphere may not be beneficial unless team members possess the skills to manage that collective positive emotion productively. Team selection which considers the emotional intelligence of potential members to ensure adequate collective levels, or training interventions which aim to increase employees' emotional intelligence are two options for organisations to consider.

The effects of shared mental models on the relationship between episodic team behavioral processes and performance were investigated, while teams were using an experimentally stimulated construction project planning program. The results indicated that episodic team processes made positive contributions to the team performance. Furthermore, a hierarchical linear regression indicated that the convergence of shared teamwork mental models moderated the effects of team processes on team performance. Specifically, the positive impact of team processes on performance was found to be improved for those teams who shared more similar teamwork mental models than for teams who hold fewer similar teamwork mental models. Potential implications and relevant impacts on future research are discussed.

With the rapid advancement of artificial intelligence (AI), especially the widespread adoption of large language models (e.g., ChatGPT), the role of AI in team collaboration is undergoing profound transformation. Prior research suggests that improvements in team effectiveness largely depend on the knowledge complementarity among team members and the development of shared mental models. However, the underlying mechanisms through which these factors operate in human–AI teams remain insufficiently understood. Grounded in shared mental model theory and transactive memory system theory and informed by a cognitive complementarity framework within human–AI teams, this study develops a cognitive mechanism model for team collaboration. A 2 (team type: human–human vs. human–AI) × 2 (knowledge complementarity: high vs. low) × 2 (team climate: positive vs. negative) between-subjects experimental design was used to examine how knowledge complementarity affects team effectiveness and member satisfaction via shared mental models, with a focus on moderating effects. A total of 128 participants were recruited to complete a collaborative promotional writing task with the theme “modern communication of classical Western artistic ideals.” The participants co-created a promotional text (within 300 words) with either a human teammate or an AI system powered by the DeepSeek architecture. The results indicate that in human–AI teams, high levels of knowledge complementarity significantly enhance both team effectiveness and member satisfaction. Shared mental models serve as a mediating mechanism, and a positive team climate further amplifies these effects. These findings contribute to a deeper understanding of cognitive structures in human–AI interactions and offer theoretical and practical guidance for the deployment of large language models in collaborative tasks.

Exploring Shared Mental Models of Surgical Teams in Video-Assisted Thoracoscopic Surgery Lobectomy

Past research suggests that knowledge diversity is utilized during creative processes, divergent and convergent thinking, to come up with novel and useful solutions. Whereas research examining the relationship between knowledge diversity and process outcomes has been mixed, differentiating knowledge into mental models may help explain team creative processes and outcomes. Herein, two types of mental models, complementary and shared mental models, are related to team creative processes. Complementary mental models are those knowledge structures held by team members that have unique role-specific task-relevant content. Shared mental models are those knowledge structures held by team members that contain similar content. These mental models provide a finer-grained perspective of knowledge and show how each team member's knowledge may contribute to divergent and convergent creative processes and outcomes. Further, a framework relating mental models and creative processes is presented and potential facilitation approaches are discussed.