Accurate Mental Models Research Articles

Influence of Virtual Reality on High School Students' Conceptions of Cells

Cells are central to the study of biology, yet many learners have difficulties understanding the abstract yet fundamental foundation of life. Research suggests that students' conceptions of cells are reinforced by current biology learning materials, which represent cells as two dimensional, highly ordered, and mostly empty. These models also inaccurately represent the number, location, and size of organelles. We examine the effect of an inquiry-based three-dimensional virtual reality (VR) game on high school students' mental models of cells. Students reported that the game was more interactive and engaging than traditional ways of learning about cells and attributed an improved understanding of cells to their game experience. Students' post drawings of cells depicted more types of organelles, increased density of organelles, and additional complexity than their pretest drawings, indicating a movement towards more accurate mental models of cells. However, students' scores did not improve on their factual knowledge about cells between the pretest and the post-game biology assessments. We discuss the implications of incorporating game-based approaches and new technologies such as VR into biology education.

Team Performance in Dynamic Settings: Evaluating Shared Team Mental Model Similarity & Accuracy

Teamwork in dynamic conditions relies on shared task mental models—knowledge structure that enables teammates to understand their task and to anticipate their work demands. Shared task mental model are either accurate or similar. Consensus exists that shared task mental model (STMM) accuracy, rather than STMM similarity is important for team performance. I examine conceptual and measurement issues underlying these findings and propose a study examining the complementary role of STMM accuracy and similarity to team performance. Forty-four (44) 3-member teams, engaged in a command and control task, performance was assessed over three scenarios, creating 132 data points. Multilevel regressions and dominance analysis of the data indicated both STMM accuracy and similarity reflected unique aspects of cognition and each predicted team performance scores. The discussion focused on the implications of these findings firstly to the measurement of shared task mental models, and to future conceptualization of shared task mental models

Playing to learn or to win? The role of students’ competition preference on self-monitoring and learning outcome when learning with a serious game

ABSTRACT This study investigates how students’ competition preference as a trait variable, in conjunction with students’ state self-monitoring, affects learning outcome when learning with a serious game. In particular, it is investigated whether competition preference moderates the relation between self-monitoring and learning outcome. For the study, 86 students (M age = 13.39, SD age = 0.58) played a serious game to learn about the entrepreneurship of craft enterprises. Competition preference and self-monitoring were assessed using self-report questionnaires. Learning outcome was assessed in terms of students’ mental model accuracy (MMA) by using structural assessments of knowledge before and after gameplay. The results show that competition preference significantly and negatively predicts learning outcome, whereas self-monitoring serves as a significant positive predictor. By testing different percentiles of students’ competition preference, our findings further indicate that the positive relation between self-monitoring and learning outcome occurs only for students with low to moderate competition preference. These results underline the necessity to take into account students’ individual preferences when designing serious games and similar interactive learning environments.

“Mind” matters!

PurposeGiven the urgency in taking climate action, the purpose of this paper is to propose a conceptual framework to drive corporate environmental responsibility (CER) through environmentally responsible decision making (ERDM) by incorporating two interventions, the accuracy of mental models (MM) of the key decision makers and green nudging.Design/methodology/approachRelevant theories in management, cognition and behavioral sciences are studied and leaned on to build this conceptual framework.FindingsOur mind creates MM about the real world to illustrate what we think about the world and about how it works. MM are clouded with biases and misconceptions. These MM have a tremendous impact on our behavior. The authors present how increasing the accuracy of the MM of environmental phenomena leads to efficient sensemaking and directs an ERDM thereby contributing to the environmental responsibility gestures of an organization. Green nudges attack the choice architecture of the decision maker toward ERDM.Research limitations/implicationsThis framework contributes to the literature on corporate social responsibility. It advances the theories at the intersection of business, economy and natural environment. The framework built with assumptions opens the scope for future research and empirical testing.Practical implicationsThis framework contributes to practice by recommending implementable and sustainable interventions. The inaccuracies found may become the base for a sector-wise training program. Due to mimetic isomorphism, driving CER may reap policy implications.Originality/valueThis multi-level conceptual framework is the first to propose individual level drivers of organizational level outcome CER through MM of environmental phenomena of key decision makers and green nudging. The paper offers complementary interventions.

Mental Model Development in Multimedia Learning: Interrelated Effects of Emotions and Self-Monitoring

Learners’ emotions and metacognitive self-monitoring play a crucial role in mental model development, particularly in the context of multimedia learning. However, learning-centered emotions and self-monitoring have been investigated largely without accounting for their dynamic interrelations. In this study, the effects of both learner-state variables on mental model development were investigated, by modeling their interrelations over time during a multimedia learning episode. For this, 108 undergraduate students (Mage = 22.79, SDage = 3.42) were engaged in a multimedia learning environment to learn practical money skills. Learning-centered emotions of enjoyment, boredom, and frustration were repeatedly collected using self-reports. Learners’ self-monitoring was assessed using behavioral data in terms of time spent on accessing specific information in the multimedia environment. Mental model development was operationalized by assessing learners’ mental model accuracy (MMA) in pre- and post-tests, by using assessments of structural knowledge. Regarding the dynamic interrelations, panel models with the repeated measures revealed positive direct and indirect paths from earlier stages of self-monitoring to later stages of enjoyment. Conversely, negative direct and indirect paths emerged from earlier stages of boredom and frustration to later stages of self-monitoring. Regarding the effects of all variables on mental model development, a path model analysis with aggregated values revealed that enjoyment was unrelated to post-test MMA, whereas boredom negatively predicted post-test MMA. Additionally, frustration negatively predicted self-monitoring, which positively predicted post-test MMA. Finally, pre-test MMA was a negative predictor of boredom and positively predicted post-test MMA. The results demonstrate that the dynamic interrelations between different learning-centered emotions and self-monitoring can diverge in multimedia learning. In addition, this study provides insights into the joint effects and the relative importance of emotions and self-monitoring for mental model development in multimedia learning.

Learning to Form Accurate Mental Models

A cycle of prediction, comparison, and feedback supports spatial learning in geoscience.

How are multifactorial beliefs about the role of genetics and behavior in cancer causation associated with cancer risk cognitions and emotions in the US population?

People who believe that cancer has both genetic and behavioral risk factors have more accurate mental models of cancer causation and may be more likely to engage in cancer screening behaviors than people who do not hold such multifactorial causal beliefs. This research explored possible health cognitions and emotions that might produce such differences. Using nationally representative cross-sectional data from the US Health Information National Trends Survey (N=2719), we examined whether endorsing a multifactorial model of cancer causation was associated with perceptions of risk and other cancer-related cognitions and affect. Data were analyzed using linear regression with jackknife variance estimation and procedures to account for the complex survey design and weightings. Bivariate and multivariable analyses indicated that people who endorsed multifactorial beliefs about cancer had higher absolute risk perceptions, lower pessimism about cancer prevention, and higher worry about harm from environmental toxins that could be ingested or that emanate from consumer products (Ps<.05). Bivariate analyses indicated that multifactorial beliefs were also associated with higher feelings of risk, but multivariable analyses suggested that this effect was accounted for by the negative affect associated with reporting a family history of cancer. Multifactorial beliefs were not associated with believing that everything causes cancer or that there are too many cancer recommendations to follow (Ps>.05). Holding multifactorial causal beliefs about cancer are associated with a constellation of risk perceptions, health cognitions, and affect that may motivate cancer prevention and detection behavior.

Mental models accurately predict emotion transitions

Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

Similarity and accuracy of mental models formed during nursing handovers: A concept mapping approach

BackgroundShared mental models are crucial for constructing mutual understanding of the patient’s condition during a clinical handover. Yet, scant research, if any, has empirically explored mental models of the parties involved in a clinical handover. ObjectivesThis study aimed to examine the similarities among mental models of incoming and outgoing nurses, and to test their accuracy by comparing them with mental models of expert nurses. DesignA cross-sectional study, exploring nurses’ mental models via the concept mapping technique. Participants40 clinical handovers. Data collectionData were collected via concept mapping of the incoming, outgoing, and expert nurses’ mental models (total of 120 concept maps). Similarity and accuracy for concepts and associations indexes were calculated to compare the different maps. ResultsAbout one fifth of the concepts emerged in both outgoing and incoming nurses’ concept maps (concept similarity=23%±10.6). Concept accuracy indexes were 35%±18.8 for incoming and 62%±19.6 for outgoing nurses’ maps. Although incoming nurses absorbed fewer number of concepts and associations (23% and 12%, respectively), they partially closed the gap (35% and 22%, respectively) relative to expert nurses’ maps. The correlations between concept similarities, and incoming as well as outgoing nurses' concept accuracy, were significant (r=0.43, p<0.01; r=0.68 p<0.01, respectively). Finally, in 90% of the maps, outgoing nurses added information concerning the processes enacted during the shift, beyond the expert nurses’ gold standard. Discussion and conclusionsTwo seemingly contradicting processes in the handover were identified. “Information loss”, captured by the low similarity indexes among the mental models of incoming and outgoing nurses; and “information restoration”, based on accuracy measures indexes among the mental models of the incoming nurses. Based on mental model theory, we propose possible explanations for these processes and derive implications for how to improve a clinical handover.

The effects of virtualness on teamwork behavioral components: The role of shared mental models

Virtual teams are an essential part of work organizations. They help organizations utilize skills and expertise regardless of where they are located. The virtual team literature suggests that these teams often experience process losses compared to their face-to-face counterparts. In this paper we propose that as virtualness increases, a team's shared mental models become more complex; this limits the effectiveness of particular teamwork behaviors: mutual performance monitoring, backup behavior and adaptation. We identify specific factors that are reflected in increased mental model complexity of virtual teams. Further we examine the moderating role that the accuracy and similarity of these shared mental models as well as the level of virtualness has on the relation between complexity and teamwork behaviors. Finally, we examine the effects of training inventions on mental model accuracy. To this end, we review the existing literature to develop a model and specific propositions.

Impacts of behavioral engagement and self-monitoring on the development of mental models through serious games: Inferences from in-game measures

The present study investigated the potential of serious games for the acquisition of complex cognitive skills by assessing learners’ mental model development, operationalized as an increase in Mental Model Accuracy (MMA). Furthermore, we assessed behavioral engagement and self-monitoring as two specific engagement types within the gameplay process and analyzed their impact on mental model development. German undergraduate students (N = 97) played a serious game developed to foster practical money skills. We obtained pre- and post-gaming measures of MMA to analyze the development of mental models by applying a structural assessment method. Unobtrusive measures of behavioral engagement and self-monitoring were obtained by computerized collection of participants’ in-game activities. Although we did not find a significant increase in overall MMA through playing, the degree of self-monitoring had a significant and positive effect on post-gaming MMA, even beyond the effect of initial MMA. Behavioral engagement had no impact on mental model development; however, it was positively related to self-monitoring behavior. The results are discussed in light of findings from research on self-regulated learning and controversial notions regarding the effect of behavioral engagement in serious games. In addition to insights into gameplay processes that affect mental model development through serious games, the present study also has practical implications in stressing the importance for game designers to provide learners with the opportunity to engage in self-monitoring behavior while playing a serious game.

Evaluation of the Design Features of Interactive Sea-Level Rise Viewers for Risk Communication

ABSTRACTInteractive sea-level rise viewers (ISLRVs) are digital tools used to communicate about impacts of sea-level rise (SLR) and support decision-making. This study characterizes how ISLRVs communicate about SLR-related risks and provide decision-making support. It identifies key themes about fostering accurate mental models of SLR processes, informing about inundation likelihood, communicating about related social and ecological risks, and providing features users can apply to specific tasks. We present a framework for understanding this type of communication tool that designers can use to develop robust ISLRVs that can support audiences’ understanding and decision-making needs, and contribute to enhancement of coastal resiliency.

ConfChem Conference on Interactive Visualizations for Chemistry Teaching and Learning: Research into Practice—Visualizing the Molecular World for a Deep Understanding of Chemistry

A deep understanding of chemistry requires internal visualization of structures and processes at the molecular level to rationalize observable chemical and physical properties, and make sense of chemistry symbolism and mathematical relationships. The paper argues that accurate mental models of the molecular world are needed to address the student misconceptions described in the literature, and describes the potential and limitations of the VisChem animations to develop useful mental models. A constructivist learning design has been developed and evaluated as a best-practice strategy for effectively using the VisChem resources for learning. This communication summarizes one of the invited papers to the Interactive Visualizations for Chemistry Teaching and Learning ACS CHED Committee on Computers in Chemical Education online ConfChem held from May 8 to June 4, 2015. The lively discussions that followed addressed the difficulty of showing the crucial role of ion hydration without producing cognitive overload...

Trusting the (ro)botic other

How may human agents come to trust (sophisticated) artificial agents? At present, since the trust involved is non-normative, this would seem to be a slow process, depending on the outcomes of the transactions. Some more options may soon become available though. As debated in the literature, humans may meet (ro)bots as they are embedded in an institution. If they happen to trust the institution, they will also trust them to have tried out and tested the machines in their back corridors; as a consequence, they approach the robots involved as being trustworthy ("zones of trust"). Properly speaking, users rely on the overall accountability of the institution. Besides this option we explore some novel ways for trust development: trust becomes normatively laden and thereby the mechanism of exclusive reliance on the normative force of trust ( as-if trust) may come into play - the efficacy of which has already been proven for persons meeting face-to-face or over the Internet (virtual trust). For one thing, machines may evolve into moral machines, or machines skilled in the art of deception. While both developments might seem to facilitate proper trust and turn as-if trust into a feasible option, they are hardly to be taken seriously (while being science-fiction, immoral, or both). For another, the new trend in robotics is towards coactivity between human and machine operators in a team (away from making robots as autonomous as possible). Inside the team trust is a necessity for smooth operations. In support of this, humans in particular need to be able to develop and maintain accurate mental models of their machine counterparts. Nevertheless, the trust involved is bound to remain non-normative. It is argued, though, that excellent opportunities exist to build relations of trust toward outside users who are pondering their reliance on the coactive team. The task of managing this trust has to be allotted to human operators of the team, who operate as linking pin between the outside world and the team. Since the robotic team has now been turned into an anthropomorphic team, users may well develop normative trust towards them; correspondingly, trusting the team in as-if fashion becomes feasible.

Monitoring and team performance: The mediating role of Task mental model accuracy

In this paper, we ask how monitoring behaviors relates to team performance. Previously, monitoring behaviors has demonstrated both positive and negative effects to team performance. We proposed that the positive effect of monitoring behaviors on team performance will be through the development of shared task mental models accuracy. This was tested in a simulation of emergency response involving twenty-two (22) student teams. We used multilevel modelling to test direct effects of monitoring behavior on team performance and indirect effects through task mental model accuracy. Monitoring progress towards goals was not a significant predictor of team performance but of task mental model accuracy, which in turn predicted team performance. The test of indirect effects using the difference in coefficient approach with bootstrapped estimates revealed a significant indirect effect of monitoring progress towards goals on team performance through task mental model accuracy. We discuss our findings with reference to ...

When shared cognition leads to closed minds: Temporal mental models, team learning, adaptation and performance

In this study we examined the moderating effect of temporal mental model accuracy on the relationship between temporal mental model similarity and team learning. Further, we investigated the mediating mechanism of team adaptation in the relationship between team learning and performance. The study was conducted in a management simulation involving 68 teams (319 individuals). We collected data at three time points. The results showed that when accuracy is high, temporal mental model similarity is not significantly related to team learning; whereas, when accuracy is low, the more similar the team members' mental models are, the less they engage in learning behaviors. This suggests that sharing an inaccurate mental model leads to closed minds. In addition, we found team adaptation to mediate the relationship between team learning and performance. These findings emphasize the importance of temporal mental models in predicting team learning, and the importance of team learning for team adaptation and performance.

The Effect of a Structured Method on Mental Model Accuracy and Performance in a Complex Task

In comparison to their performance with normative standards or even simple heuristics, humans do not perform well in complex decision-making. The application of systems thinking to help people to understand and handle interdependent and complex systems is proposed as a means of improving this poor performance. The aim of this study is to investigate the effect of a generic systems thinking method, i.e., a structured method, on performance. A laboratory experiment was conducted using a dynamic and complex simulation task. The results demonstrated that subjects provided with a structured method achieved a higher performance. In addition, mental model accuracy had a significant effect on performance, as already shown by several previous studies. The results of our study provide a way of teaching subjects how to improve their performance when coping with complex systems in general. This has implications for education in the fields of complex systems and system dynamics.

Exploratory information searching in the enterprise: A study of user satisfaction and task performance

No prior research has been identified that investigates the causal factors for workplace exploratory search task performance. The impact of user, task, and environmental factors on user satisfaction and task performance was investigated through a mixed methods study with 26 experienced information professionals using enterprise search in an oil and gas enterprise. Some participants found 75% of high‐value items, others found none, with an average of 27%. No association was found between self‐reported search expertise and task performance, with a tendency for many participants to overestimate their search expertise. Successful searchers may have more accurate mental models of both search systems and the information space. Organizations may not have effective exploratory search task performance feedback loops, a lack of learning. This may be caused by management bias towards technology, not capability, a lack of systems thinking. Furthermore, organizations may not “know” they “don't know” their true level of search expertise, a lack of knowing. A metamodel is presented identifying the causal factors for workplace exploratory search task performance. Semistructured qualitative interviews with search staff from the defense, pharmaceutical, and aerospace sectors indicates the potential transferability of the finding that organizations may not know their search expertise levels.

Human-Animal Teams as an Analog for Future Human-Robot Teams: Influencing Design and Fostering Trust

Our work posits that existing human-animal teams can serve as an analog for developing effective human-robot teams. Existing knowledge of human-animal partnerships can be readily applied to the HRI domain to foster accurate mental models and appropriately calibrated trust in future human-robot teams. Human-animal relationships are examined in terms of the benefiting roles animals can play in enabling effective teaming, as well as the level of team interdependency and team communication, with the goal of developing applications in future human-robot teams.

Leveraging Features of Human—Technology Teams to Support Mental Models in Future Soldier—Robot Teams

The future vision of military robotics is one in which robots will serve as integrated members of Soldier—robot teams. Robots will possess capabilities that will transition their role from functional tools to working teammates. Because robots and Soldiers will be deployed in environments characterized by uncertainty, complexity, and violence, it is imperative that Soldiers have accurate mental models of what their robotic teammates can do, cannot do, and will likely do. In this paper, we present the conclusions of a review into metaphors for facilitating accurate mental models of robotic teammates. Emphasis was placed on investigating existing human—technology teams (i.e., human teaming with automated systems including autopilot in cockpits, driver assistance systems, and personal assistant applications among others) for features that can support accurate mental models for Soldiers in future Soldier—robot teams.