Mental Models Research Articles

Intentionality, Pygmalion, and Cognitive Fun Redirects Elementary School Children in Pull-Out Neural Enrichment

This study describes an academic enrichment intervention for 15 ‘high risk’ children who were pulled out of General Education classrooms in Bangalore India for 40 minutes four-times a week during a typical school year. Could a brain-based intervention reduce achievement gaps that showed up for these children in terms of below-grade academic outcomes and impoverished social and emotional wellbeing? The null hypothesis stated that a teacher’s mental model that shifted to brain-based methodology would have no perceivable effect on (i) children’s academic achievement or (ii) their social and emotional engagement. This study was grounded in teacher education literature involving mental models that illuminate classroom management techniques. Subjects were drawn from second grade students (mean 6.7 years; n = 15). Study was an opportunistic quasi-experimental design reflecting school life across India. Qualitative ethnographic data using grounded theory were triangulated with quantitative measures that best account for observed outcomes. Findings highlight significant academic, and social and emotional growth, which dramatically reduced the achievement gap for all participants.

Strong-ties and weak-ties rationalities: toward a mental model of the consequences of kinship intensity

There is growing evidence of the connection between variations in kinship intensity and cross-cultural differences in psychological traits. Contributing to this literature on kinship intensity, we put forward a mental model to explain the enduring connection between ancestral niche and psychological traits. Our model posits that two primary orientations or dispositions—strong-ties and weak-ties rationalities—have co-evolved with our ancestral niches to perpetuate—by internalizing and reproducing—the social structure (such as preferences for certain attitudes, values, and beliefs) of the ancestral niche. The findings from 1,291 participants across four societies—China, India, Taiwan, and the United States—support our hypothesis that strong-ties (weak-ties) rationalities, when activated, will endorse strong-tie (weak-ties) values and beliefs. This proposed model contributes to the toolbox of cultural and cross-cultural psychology in a twofold sense: First, in addition to the index of kinship intensity, it offers a measure of kin-based rationality as another predictor of psychological traits; second, it renders intelligible the niche and rationality disconnect prevalent in the globalizing era.

Predictive Analytics for Mental Health: Machine Learning Approaches in the Tech Industry

Mental health concerns in the technology sector have become increasingly prominent, drawing attention due to the industry's high-pressure environment and intense workloads. This study investigates the application of machine learning techniques to predict mental health conditions among technology industry professionals. The research utilizes a comprehensive dataset, analyzing factors such as work-life balance, job satisfaction, workplace environment, and individual well-being. The study implements and evaluates several Machine Learning approaches, including Logistic Regression, Random Forest, Decision Tree Classifier, KNN, Gradient Boosting Classifier, XGB Classifier, to determine the most effective method for predicting mental health outcomes. The results highlight the potential of predictive analytics in early identification, enabling organizations to implement proactive strategies to enhance mental wellness and improve employee productivity. Additionally, this investigation emphasizes the necessity of incorporating mental health support mechanisms in technology-driven workplaces to foster a more positive work environment. The paper concludes with a discussion of the study's limitations and potential avenues for improving mental health prediction models in the future.

Mental Health Education Evaluation Model Based on Emotion Recognition Algorithm

Mental Health Education Evaluation Model Based on Emotion Recognition Algorithm

Influence of Mental Model-derived Themes on the Quality of Radiological Emergency Response by HAZMAT Technicians: Insights from a Case Study.

This paper investigates the link between gaps in emergency responders' notions of mental model regarding radiation and risk and their effectiveness in responding to radiological incidents. Particularly, this work focused on exploring themes that emerged in prior work related to improper understanding and application of knowledge concepts related to radiation risks and Radiological Dispersal Device (RDD) scenarios (Leek et al., 2024b). The research uses a quantitative approach to correlate various thematic elements, such as responders' confidence levels, comprehension, and application of radiation risk principles, with the quality of the emergency response score gained through a virtual reality simulation. The results underscore a strong effect of responders' confidence level on response quality scores. Additionally, the study identifies that improper understanding of knowledge concepts and incorrect application of radiation risk and RDD concepts are factors that detract from the quality of response, especially the tendency to overestimate health risks associated with a 25-rem (0,25 Sv) dose and to misapply principles of radiation risk. The implications of this research are significant for the development and refinement of training programs for hazardous materials (HAZMAT) technicians and other emergency responders. The findings suggest the need for a comprehensive review of current training methodologies to address the identified deficiencies that had impacts on the quality of response. The findings provide a foundation for reshaping training priorities and operational readiness, driving the development of training that is both grounded in empirical evidence and that directly addresses the knowledge gaps influencing response quality. The methodological framework developed and employed, including the quality scoring system and the Expected Mental Model State (EMMS) Diagnostic Matrix, also hold potential for broader application in future investigations, extending to diverse types of responders and emergency scenarios.

Development of a Generalist Competency Framework for Medical Education Leaders in Longitudinal Integrated Curricula (LILIC)

AbstractRecent shifts in health professions education have prompted significant curriculum reforms toward multidisciplinary, longitudinally integrated courses during the pre-clinical and clinical years of medical school curricula. Faculty who are accustomed to managing discipline-specific courses must now be trained to meet the challenges of implementing new integrated courses. Skills and competencies for Leaders in Longitudinal Integrated Curricula (LILIC) must be identified for faculty development efforts and to assist administrators in recognizing the attributes of well-qualified course/curriculum directors for their appointment/hiring decisions. Drawing on the collective expertise of faculty from multiple medical schools, we developed a framework to meet this new need. Herein, we present a shared mental model, a framework, and higher-level attributes important for the development of these curricular leaders. A proposed development process and future directions for this framework are also presented, underscoring its potential impact on faculty development and the broader educational landscape in health professions.

Evaluating What Others Say: The Effect of Accuracy Assessment in Shaping Mental Models of AI Systems

Forming accurate mental models that align with the actual behavior of an AI system is critical for successful user experience and interactions. One way to develop mental models is through information shared by other users. However, this social information can be inaccurate and there is a lack of research examining whether inaccurate social information influences the development of accurate mental models. To address this gap, our study investigates the impact of social information accuracy on mental models, as well as whether prompting users to validate the social information can mitigate the impact. We conducted a between-subject experiment with 39 crowdworkers where each participant interacted with our AI system that automates a workflow given a natural language sentence. We compared participants' mental models between those exposed to social information of how the AI system worked, both correct and incorrect, versus those who formed mental models through their own usage of the system. Specifically, we designed three experimental conditions: 1) validation condition that presented the social information followed by an opportunity to validate its accuracy through testing example utterances, 2) social information condition that presented the social information only, without the validation opportunity, and 3) control condition that allowed users to interact with the system without any social information. Our results revealed that the inclusion of the validation process had a positive impact on the development of accurate mental models, especially around the knowledge distribution aspect of mental models. Furthermore, participants were more willing to share comments with others when they had the chance to validate the social information. The impact of inaccurate social information on altering user mental models was found to be non-significant, while 69.23% of participants incorrectly judged the social information accuracy at least once. We discuss the implications of these findings for designing tools that support the validation of social information and thereby improve human-AI interactions.

"I inherently just trust that it works": Investigating Mental Models of Open-Source Libraries for Differential Privacy

Differential privacy (DP) is a promising framework for privacy-preserving data science, but recent studies have exposed challenges in bringing this theoretical framework for privacy into practice. These tensions are particularly salient in the context of open-source software libraries for DP data analysis, which are emerging tools to help data stewards and analysts build privacy-preserving data pipelines for their applications. While there has been significant investment into such libraries, we need further inquiry into the role of these libraries in promoting understanding of and trust in DP, and in turn, the ways in which design of these open-source libraries can shed light on the challenges of creating trustworthy data infrastructures in practice. In this study, we use qualitative methods and mental models approaches to analyze the differences between conceptual models used to design open-source DP libraries and mental models of DP held by users. Through a two-stage study design involving formative interviews with 5 developers of open-source DP libraries and user studies with 17 data analysts, we find that DP libraries often struggle to bridge the gaps between developer and user mental models. In particular, we highlight the tension DP libraries face in maintaining rigorous DP implementations and facilitating user interaction. We conclude by offering practical recommendations for further development of DP libraries.

Enhancing Conversations in Migrant Counseling Services: Designing for Trustworthy Human-AI Collaboration

Migrants often face a complex situation for navigating services and administrative processes to transition and integrate into a new environment. They rely on guidance from service advisors and informal peers like other migrants to assist them. Service advisors try to understand, explore, and resolve migrant concerns through conversational interaction, while often seeking advice from other domain experts. The emergence of artificial intelligence (AI)-mediated communication holds the potential for enhancing communication between migrants and service advisors while leveraging the expertise of service advisors and facilitating migrants' autonomy in the integration process. Our research investigates the social context, mental models, and challenges encountered in guidance counseling for migrants in Finland, while examining what role AI-mediated communication can play in supporting the process. Based on our findings, we propose design implications for improving shared understanding between service advisors and migrants through salient summarization of conversations and creating collaborative visual narratives. Our findings also indicate the crucial role of leveraging peer-based knowledge between (and within) service advisors, domain experts, and migrants, which can be facilitated by AI-based tools. Our exploratory research offers insights into human-centered AI integration in the migration process, as well as the opportunities and challenges of using AI-mediated communication to improve the migrant experience.

The impact of COVID-19 on routine child immunisation in South Africa

BackgroundThe COVID-19 pandemic disrupted immunisation programs worldwide, reversing gains that had brought vaccine-preventable diseases largely under control. This study explored the impact of COVID-19 on the uptake of routine child immunisation services in South Africa.MethodsWe conducted qualitative research using in-depth interviews with 51 purposively selected parents/caregivers of children below the age of five who missed or delayed one or more scheduled immunisation doses in 2020–2022 and with 12 healthcare providers who provided public immunisation services during the pandemic.ResultsDuring the pandemic lockdowns, most caregivers perceived the risk of their child being infected with COVID-19 during a clinic visit as more salient than the risk of missing immunisation doses. Caregivers reported minimal exposure to routine immunisation communication, as well as shortages of routine vaccines for children at public health facilities, healthcare workers experienced anxiety and burnout. There was a post-pandemic shift to more active decision-making about immunisation, which had previously been an almost automatic behaviour, leading some caregivers to delay vaccinating their children. There was also evidence of a “bad vaccine” mental model among some caregivers regarding COVID vaccinations, which could lead to doubts about the safety of routine childhood vaccinations.DiscussionThe shift from default to active decision-making highlights a risk that routine immunisation will backslide in future pandemics. Governments should build resilient health systems at all levels and communicate clearly about the benefits and availability of RIs and the safety of vaccinations in general, alongside supply-side interventions.ConclusionRoutine immunisation is widely accepted in South Africa, driven by generational norms and provider recommendations. During the COVID-19 pandemic, many caregivers faced the dilemma of balancing COVID-19 exposure risk with the risk of their child developing a deadly VPD, leading to missed RI visits. This shift to active decision-making highlights a future pandemic risk. Governments should build resilient health systems and focus on understanding and engaging procrastinating and doubtful caregivers. Clear communication about RI benefits and vaccine safety is crucial, as misinformation can lead to distrust in new vaccines.

MCNN-CMCA: A multiscale convolutional neural networks with cross-modal channel attention for physiological signal-based mental state recognition

Human mental state recognition (MSR) has significant implications for human-machine interactions. Although mental state recognition models based on single-modality signals, such as electroencephalogram (EEG) or peripheral physiological signals (PPS), have achieved encouraging progress, methods leveraging multimodal physiological signals still need to be explored. In this study, we present MCNN-CMCA, a generic model that employs multiscale convolutional neural networks (CNNs) with cross-modal channel attention to realize physiological signals-based MSR. Specifically, we first design an innovative cross-modal channel attention mechanism that adaptively adjusting the weights of each signal channel, effectively learning both intra-modality and inter-modality correlation and expanding the channel information to the depth dimension. Additionally, the study utilizes multiscale temporal CNNs for obtaining short-term and long-term time-frequency features across different modalities. Finally, the multimodal fusion module integrates the representations of all physiological signals and the classification layer implements sparse connections by setting the mask weights to 0. We evaluate the proposed method on the SEED-VIG, DEAP, and self-made datasets, achieving superior results compared to existing state-of-the-art methods. Furthermore, we conduct ablation studies to demonstrate the effectiveness of each component in the MCNN-CMCA and show the use of multimodal physiological signals outperforms single-modality signals.

Simplify, Consolidate, Intervene: Facilitating Institutional Support with Mental Models of Learning Management System Use

Measuring instructors' adoption of learning management system (LMS) tools is a critical first step in evaluating the efficacy of online teaching and learning at scale. Existing models for LMS adoption are often qualitative, learner-centered, and difficult to leverage towards institutional support. We propose depth-of-use (DOU): an intuitive measurement model for faculty's utilization of a university-wide LMS and their needs for institutional support. We hypothesis-test the relationship between DOU and course attributes like modality, participation, logistics, and outcomes. In a large-scale analysis of metadata from 30000+ courses offered at Virginia Tech over two years, we find that a pervasive need for scale, interoperability and ubiquitous access drives LMS adoption by university instructors. We then demonstrate how DOU can help faculty members identify the opportunity-cost of transition from legacy apps to LMS tools. We also describe how DOU can help instructional designers and IT organizational leadership evaluate the impact of their support allocation, faculty development and LMS evangelism initiatives.

Causal inference to scope environmental impact assessment of renewable energy projects and test competing mental models of decarbonization

Causal inference to scope environmental impact assessment of renewable energy projects and test competing mental models of decarbonization

Disjunction and access to knowledge: Educational implications

In classical logic, it is possible to derive ‘either p or q’ from ‘p’ (where p and q are sentences with any content). This is a cognitive problem, since people often tend not to make inferences of that kind. This paper analyzes the solution the theory of mental models gives for this problem. Based on that solution, the paper proposes to use tasks with inferences such as that mentioned in the educational context. The idea is that those tasks can allow assessing certain learnings. In particular, they can reveal in some cases whether students understand the relations between the semantic contents assigned to p and q. This is because, following the theory of mental models, whenever q implies p, the inference must be accepted.

Building and Breaking the Chain: A Model of Reward Prediction Error Integration and Segmentation of Memory.

Prediction errors drive reinforcement learning and organize episodic memory into distinct contexts, but do these effects interact? Here, we review the roles of midbrain dopamine, the locus coeruleus, and the hippocampus in event cognition to propose and simulate the theoretical influence of two prediction error signals in integrating versus segmenting events in memory. We suggest that signed reward prediction errors can build mental models of reward environments, increasing the contextual similarity (integration) of experiences with stronger, more stable reward expectations. On the other hand, unsigned reward prediction errors can signal a new model of the environment, generating a contextual shift (segmentation) between experiences that crossed them. We moreover predicted that these differences in contextual similarity give rise to distinct patterns of temporal-order memory. We combined these ideas in a computational model to account for a seemingly paradoxical pattern of temporal-order memory where greater representational distance helps order memory within context but impairs it across contexts. We found that simulating signed reward prediction error integration and unsigned reward prediction error segmentation differentially enabled the model to perform associative chaining, which involved reactivating items between two tested probes to assist with sequential retrieval. In summary, our simulations provide a unifying explanation for the varied ways that neuromodulatory systems may alter event cognition and memory.

Kind, Firm, and Fair Medical Regulation: A Systems Approach and Six Ethical Principles

ABSTRACT Background: Regulatory processes can be stressful, anxiety provoking, and complicated for the health professional involved, the institutions they work for, and for the regulators. Methods: This paper uses a case study to describe a systems approach to comprehensively identify the agents (ie: the complainant, the health professional concerned, the regulator dedicated to the case, the institution, the healthcare profession, the public, and the regulatory authority) and 6 ethical issues that emerge during the regulatory process: potential to cause harm, not in the best interest of a person, affects autonomy, unjust, not truthful, and loss of trust. Results: A systems approach lends to a mental model in which there are dynamic interactions across these elements accompanied by positive and negative feedback loops that maintain overall stability of the whole system. Conclusions: The process can be further refined and kinder to those concerned by developing a sensitive approach to improve assessment of risks, using feedback to improve the regulatory process, adapting transparent procedures, not causing undue delays to the process, embedding regulatory activities in a patient safety system, minimizing individual blame, using non-legalistic, non-threatening and respectful communications, and directing the different actors to a support network for guidance and advice.

“I will raise my hand and say ‘I over-trust Autopilot’. I use it too liberally” – Drivers’ reflections on their use of partial driving automation, trust, and perceived safety

Introduction: Partially automated cars are on the road. Trust in automation and perceived safety are critical factors determining use of automation. Background: Drivers misuse partially automated driving systems. Misuse is associated with mis-calibrated trust in the automation. Research gap: Little is known about the factors impacting the perceived safety when using partial driving automation. Research objective: The main objective of the present study is to provide a comprehensive driver perspective on the psychological aspects of automation use pertaining to trust in automation, perceived safety, and its relationship with use of automation. Method: Semi-structured interviews (n = 103) were conducted with users of partially automated driving systems. Supplemented with content analysis, natural language processing (NLP) techniques were applied to perform automatic text processing. Guided seed-term analysis was conducted to identify the number of occurrences of the subcategories in the dataset. Main results: We identified human operator-related, automation-related, and environmental factors of trust and perceived safety. The identified factors were more strongly associated with perceived safety than with trust. Participants with physical and visual impairments reported to feel safer using the automation compared to driving manually. Neurotic behavior during manual driving contributed to lower trust and perceived safety using the automation. A correct mental model of the capabilities and limitations of the automation did not guarantee proper automation use. A novel conceptual, process-oriented model, titled PTS-a (predicting trust in and perceived safety of automation use), synthesizes the results of the data analysis. Informed by the cognition-leads-to-emotions approach, the model posits that trust as cognition precedes perceived safety as affective construct. Trust and perceived safety determine how human operators (mis-, dis-)use the automation. Future research: We recommend future research to perform experimental studies to identify cognitive-related thoughts and beliefs pertaining to trust in automation and perceived safety to contribute to the operationalization of these constructs, and unravel the nature of their relationship.

Is “No News is Good News” Enough? A Thematic Analysis Exploring Clinical Reasoning Assessment in Pediatric Residency Programs

ObjectiveClinical reasoning (CR) includes numerous essential skills for clinicians, but how these skills are assessed in pediatric residency training is not well described. This study aimed to explore pediatric residency program leader perspectives on clinical reasoning assessment and identification of trainee deficiencies in this area. MethodsTaking a social constructionist worldview, we conducted a thematic analysis of 20 semi-structured interviews with pediatric residency program leaders. Interviews explored how pediatric residency programs assess CR and how deficiencies are identified. Recruitment and analysis continued iteratively until thematic sufficiency was reached. Member checking enhanced trustworthiness of the results. ResultsParticipants noted a perceived lack of a shared mental model for CR assessment between program leaders and clinical supervisors. Four themes were generated to highlight CR assessment in pediatric residency programs: 1) Clinical supervisors escalate concerns about behaviors representing symptoms of CR deficits rather than diagnosing CR competency deficiencies and that CR assessment requires: 2) an outward display of autonomous decision-making, 3) psychologically safe environments for inquiry, and 4) longitudinal, individualized observation. Elements of pediatric residency programs that impede CR assessment were identified, including family-centered rounds and team-based clinical care. ConclusionsThis study identified key components necessary for CR assessment and barriers that may lead to missed identification of deficiencies. While no single solution can create an ideal environment for CR assessment, this study identifies elements for enhancing assessment opportunities for early identification of deficiencies.

Fostering dialogue on health risks of intensive livestock farming in the Netherlands: Combining insights from conversation analysis with the mental model approach of risk communication

Fostering dialogue on health risks of intensive livestock farming in the Netherlands: Combining insights from conversation analysis with the mental model approach of risk communication

Uncertain reflective functioning, anxious attachment, and cumulative impact of early relational trauma as predictors of severity in borderline personality disorder

ABSTRACT Background: The mentalization model of borderline personality disorder (BPD) utilizes a developmental psychopathology lens, emphasizing an unstable or reduced capacity to mentalize—stemming from early attachment disruptions and relational trauma—as the core feature of BPD. While the empirical evidence for the proposed intersections between mentalizing, attachment, and trauma and the development and manifestations of borderline personality is still limited, this knowledge is essential for developing effective assessments and interventions. Aim: To examine mentalizing, attachment, and early relational trauma as predictors of the severity of symptoms in BPD. Materials and Methods: The sample included 60 individuals diagnosed with BPD, aged 18–45 years, recruited from inpatient and outpatient services in a tertiary care mental health center. Participants completed measures of mentalizing (Reflective Functioning Questionnaire, Interpersonal Reactivity Index, and Reading the Mind in the Eyes test), attachment (Attachment Style Questionnaire), early relational trauma (Complex Trauma Questionnaire), and symptom severity (Borderline Symptom List). Results: The majority of the participants reported experiences of polyvictimization (93.3%) with the most common traumas being psychological abuse (93.3%), neglect (91.7%), and rejection (90.0%). Regression analyses revealed that uncertain reflective functioning, anxious attachment style, and the cumulative impact of trauma together predicted 53.1% of the variance in BPD symptom severity. Conclusion: Impaired mentalizing, characterized by inflexible understanding of mental states, is a critical target for therapeutic interventions in BPD. The significant role of anxious attachment and the necessity of assessing the perceived impact of traumatic experiences highlight the importance of trauma-informed and mentalization-based interventions for this vulnerable group.