Internal Mental Models Research Articles

Supporting risk management through cyberspace: An adaptive network model simulating AI coach effects by inducing adherence to guidelines in neonatal medical protocols

In this article, it is shown how second-order adaptive agent-based network models can be used to support a medical team in healthcare institutions to adhere to specific Neonatal Hypoglycemia and Neonatal Hyperbilirubinemia treatment guidelines through the integration of an Artificial Intelligence (AI) Virtual Coach. The proposed AI Coach is designed to provide timely interventions and correct deviations when lapses in the health care practitioner’s internal mental model occur. Through simulating three different scenarios, the internal dynamics of these mental models, adaptive changes of these mental models (learning and forgetting), and the interaction between health care practitioners and the world is shown when: (1) There is perfect adherence to guidelines, (2) There is imperfect adherence to guidelines and (3) There is both perfect and imperfect adherence to guidelines alongside interventions of the AI Coach in the latter case.

Reimagining the role of technology in sport officiating: how artificial intelligence (AI) supports the design and delivery of ecologically dynamic development processes

ABSTRACT Purpose/Rationale Sport officials operate within settings that dynamically change and shift. While they gather, synthesise, and store experiences related to task, performer, and environmental constraints, their internal mental models of judgement and decision-making individually evolve as they perform in different contexts. However, while a large body of work in psychology and behavioural economics has attempted to capture the way humans make decisions, there is a growing realisation among researchers, evaluators, and educational designers that quality improvement interventions cannot be understood outside of the context in which they occur [Ramaswamy, R., Reed, J., Livesley, N., Boguslavsky, V., Garcia-Elorrio, E., Sax, S., Houleymata D., Kimble L., Parry, G. (2018). Unpacking the black box of improvement. International Journal for Quality in Health Care, 30(suppl_1), 15–19. https://doi.org/10.1093/intqhc/mzy009]. Approach In this futuristic proposal, we put forward our vision of how artificial intelligence technologies can unpack and support the internal collections of cognitive knowledge, context, task goals, and on-field experiences that influence sport officiating development. Findings In what follows, we define what we mean by artificial intelligence and machine learning technologies, briefly highlighting their histories in sport analytics contexts. We outline how education is a promising field for the adoption of artificial intelligence/machine learning technologies and conclude by providing a theoretical case study scenario that describes a potential platform through which perspectives of environment, task and performer expertise might be developed for amateur and elite sport officials. Practical implications Using advanced AI technologies as the basis through which to examine on-field data provides tremendous potential to theoretically tackle the idiosyncrasies of officiating development in a range of sports as it can close the gap between a descriptive analysis (i.e. understanding the interactions undertaken by officials in the presence of others), and a more prescriptive one (i.e. suggesting the actions such officials should have executed). Research contribution We put forward that artificial intelligence technologies can offer sport organisations sophisticated, constructively based help with opening the “black box” of learning related to sport officiating development. By using ecological dynamics as the fundamental framework through which to filter the data collected, statistical information and qualitative ecological outcomes can be linked and managed into understandable and stable development content [Liu, A., Mahapatra, R. P., & Mayuri, A. V. R. (2021). Hybrid design for sports data visualization using AI and big data analytics. Complex & Intelligent Systems, https://doi.org/10.1007/s40747-021-00557-w], that strongly benefits the online development of amateur sport officials.

Towards General Natural Language Understanding with Probabilistic Worldbuilding

Abstract We introduce the Probabilistic Worldbuilding Model (PWM), a new fully symbolic Bayesian model of semantic parsing and reasoning, as a first step in a research program toward more domain- and task-general NLU and AI. Humans create internal mental models of their observations that greatly aid in their ability to understand and reason about a large variety of problems. In PWM, the meanings of sentences, acquired facts about the world, and intermediate steps in reasoning are all expressed in a human-readable formal language, with the design goal of interpretability. PWM is Bayesian, designed specifically to be able to generalize to new domains and new tasks. We derive and implement an inference algorithm that reads sentences by parsing and abducing updates to its latent world model that capture the semantics of those sentences, and evaluate it on two out-of-domain question-answering datasets: (1) ProofWriter and (2) a new dataset we call FictionalGeoQA, designed to be more representative of real language but still simple enough to focus on evaluating reasoning ability, while being robust against heuristics. Our method outperforms baselines on both, thereby demonstrating its value as a proof-of-concept.

A Self-Modeling Network Model Addressing Controlled Adaptive Mental Models for Analysis and Support Processes

In this paper, a self-modeling mental network model is presented for cognitive analysis and support processes for a human. These cognitive analysis and support processes are modeled by internal mental models. At the base level, the model is able to perform the analysis and support processes based on these internal mental models. To obtain adaptation of these internal mental models, a first-order self-model is included in the network model. In addition, to obtain control of this adaptation, a second-order self-model is included. This makes the network model a second-order self-modeling network model. The adaptive network model is illustrated for a number of realistic scenarios for a supported car driver.

Modeling learner-controlled mental model learning processes by a second-order adaptive network model.

Learning knowledge or skills usually is considered to be based on the formation of an adequate internal mental model as a specific type of mental network. The learning process for such a mental model conceptualised as a mental network, is a form of (first-order) mental network adaptation. Such learning often integrates learning by observation and learning by instruction. For an effective learning process, an appropriate timing of these different elements is crucial. By controlling the timing of them, the mental network adaptation process becomes adaptive itself, which is called second-order mental network adaptation. In this paper, a second-order adaptive mental network model is proposed addressing this. The first-order adaptation process models the learning process of mental models and the second-order adaptation process controls the timing of the elements of this learning process. It is illustrated by a case study for the learner-controlled mental model learning in the context of driving a car. Here the learner is in control of the integration of learning by observation and learning by instruction.

Modeling the development of internal mental models by an adaptive network model

Mental models play a crucial role in individual and organizational learning. The adaptive mental processes involved in the development of mental models are addressed here by integrating psychological and neurological theories on mental models and the learning involved. An adaptive network model has been designed for these processes and used for simulations addressing a case study of learning to drive a car. The developed model may be valuable for different ends, like in improving individual and organizational learning, in designing virtual pedagogical agents, enhancing driver safety, and self-cardriving systems.

An adaptive network model covering metacognition to control adaptation for multiple mental models

Learning processes can be described by adaptive mental (or neural) network models. If metacognition is used to regulate learning, the adaptation of the mental network becomes itself adaptive as well: second-order adaptation. In this paper, a second-order adaptive mental network model is introduced for metacognitive regulation of learning processes. The focus is on the role of multiple internal mental models, in particular, the case of visualisation to support learning of numerical or symbolic skills. The second-order adaptive network model is illustrated by a case scenario for the role of visualisation to support learning multiplication at the primary school.

Brainatomy: A Novel Way of Teaching Sphenoid Bone Anatomy With a Simplified 3-Dimensional Model

The diagnosis and treatment of diseases at and around the sphenoid bone demands throughout understanding of its anatomy in 3-dimensional (3-D) space. However, despite the complex anatomic nature of the sphenoid bone, the current educational resources for its 3-D anatomy are insufficient for fast and long-term retention of the anatomic relationships. To provide a simplified 3-D model of the sphenoid bone that anyone can easily learn and recall as an internal mental model. Various studies on the anatomy of the sphenoid bone were analyzed. The collected data included the shape, foramina, canals, fissures, and minute details of the sphenoid bone. The gained detailed knowledge was subsequently used to create a 3-D model of the sphenoid bone with the help of 3-D computer software. A live lecture was given with this same software and simultaneously recorded with a microphone and a computer-screen recorder. A novel approach in lecturing, building the sphenoid bone from the scratch in a piecemeal fashion, was utilized. The sphenoid bone was recreated as an horizontally elongated box without a superior and posterior wall. All its foramina, canals, and fissures are visually easy to follow. Understanding its neuroanatomic terminologies based on their anatomic nature and relationships is enhanced. This simplified 3-D model, along with the video lecture, will enhance the efficiency of studying sphenoid bone anatomy. The educational resources of this study can be obtained by medical students, radiologists, neurologists, neurosurgeons, neuroscientists, or anyone else seeking for fundamental understanding of sphenoid bone anatomy.

Timelines That Shape the Future

Simply collecting and looking at data to reveal the bigger picture of complex systems is no longer enough. How our internal mental models perceive the physical world affects how we interact with our environment and shape the future.Creating timelines as a way to see a larger picture of interlocking systems—and one's own relation to them.

The team as a secure base: Promoting resilience and competence in child and family social work

This paper outlines research findings on the relevance of the Secure Base model (Schofield & Beek, 2014) for developing supportive teams in child and family social work. When the social work team functions as a secure base, this can help workers cope with the emotional demands of the role. The concept of the secure base comes from attachment theory (Bowlby, 1969) in which our relationships with significant others, who are available, sensitive to our needs and reliable, provide us with a secure base to return to when life is stressful and provide us with comforting internal mental models when we are physically away from them. This ‘secure base for exploration’ reduces anxiety and enables us to engage with the world, consider the internal world of others (empathy) and remain resilient when life is stressful. Using data from 52 phone interviews with child and family social workers across eight local authorities in the UK, we show how the Secure Base model has relevance for emotion regulation and resilience for child and family social workers. Data were analysed using Theoretical Thematic Analysis (Braun & Clarke, 2006). In the context of the emotional demands of social work, our data indicate that the supervisors and teams provide a work related secure base across five dimensions by behaving in ways which instil these beliefs: Availability -‘People are there for me’; Sensitivity - ‘My feelings are manageable’; Acceptance - I don't always have to be strong’; Cooperation - ‘I can work with others to find a solution’; Team belonging - ‘I am valued and I belong’. Implications for practice are proposed to help supervisors and team members reflect on beliefs and behaviours which can help provide a secure base for their teams.

The building performance gap: Are modellers literate?

One of the most discussed issues in the design community is the performance gap. In this research, we investigate for the first time whether part of the gap might be caused by the modelling literacy of design teams. A total of 108 building modellers were asked to comment on the importance of obtaining and using accurate values for 21 common modelling input variables, from U-values to occupancy schedules when using dynamic simulation to estimate annual energy demand. The questioning was based on a real building for which high-resolution energy, occupancy and temperature data were recorded. A sensitivity analysis was then conducted using a model of the building (based on the measured data) by perturbing one parameter in each simulation. The effect of each perturbation on the annual energy consumption given by the model was found and a ranked list generated. The order of this list was then compared to that given by the modellers for the same changes in the parameters. A correlation analysis indicated little correlation between which variables were thought to be important by the modellers and which proved to be objectively important. k-means cluster analysis identified subgroups of modellers and showed that 25% of the people tested were making judgements that appeared worse than a person responding at random. Follow-up checks showed that higher level qualifications, or having many years of experience in modelling, did not improve the accuracy of people’s predictions. In addition, there was no correlation between modellers, with many ranking some parameters as important that others thought irrelevant. Using a three-part definition of literacy, it is concluded that this sample of modellers, and by implication the population of building modellers, cannot be considered modelling literate. This indicates a new cause of the performance gap. The results suggest a need and an opportunity for both industry and universities to increase their efforts with respect to building physics education, and if this is done, a part of the performance gap could be rapidly closed. Practical application: In any commercial simulation, the modeller will have to decide which parameters must be included and which might be ignored due to lack of time and/or data, and how much any approximations might perturb the results. In this paper, the judgment of 108 modellers was compared against each other. The results show that the internal mental models of thermal modellers disagree with one another, and disagree with the results of a validated thermal model. The lessons learnt will be of great utility to modellers, and those educating the next generation of modellers.

P.1.l.022 - Mental resilience, perceived health and immune status

This paper outlines research findings on the relevance of the Secure Base model (Schofield & Beek, 2014) for developing supportive teams in child and family social work. When the social work team functions as a secure base, this can help workers cope with the emotional demands of the role. The concept of the secure base comes from attachment theory (Bowlby, 1969) in which our relationships with significant others, who are available, sensitive to our needs and reliable, provide us with a secure base to return to when life is stressful and provide us with comforting internal mental models when we are physically away from them. This ‘secure base for exploration’ reduces anxiety and enables us to engage with the world, consider the internal world of others (empathy) and remain resilient when life is stressful. Using data from 52 phone interviews with child and family social workers across eight local authorities in the UK, we show how the Secure Base model has relevance for emotion regulation and resilience for child and family social workers. Data were analysed using Theoretical Thematic Analysis (Braun & Clarke, 2006). In the context of the emotional demands of social work, our data indicate that the supervisors and teams provide a work related secure base across five dimensions by behaving in ways which instil these beliefs: Availability -‘People are there for me’; Sensitivity - ‘My feelings are manageable’; Acceptance - I don't always have to be strong’; Cooperation - ‘I can work with others to find a solution’; Team belonging - ‘I am valued and I belong’. Implications for practice are proposed to help supervisors and team members reflect on beliefs and behaviours which can help provide a secure base for their teams.

Surface and deep structures in graphics comprehension.

Comprehension of graphics can be considered as a process of schema-mediated structure mapping from external graphics on internal mental models. Two experiments were conducted to test the hypothesis that graphics possess a perceptible surface structure as well as a semantic deep structure both of which affect mental model construction. The same content was presented to different groups of learners by graphics from different perspectives with different surface structures but the same deep structure. Deep structures were complementary: major features of the learning content in one experiment became minor features in the other experiment, and vice versa. Text was held constant. Participants were asked to read, understand, and memorize the learning material. Furthermore, they were either instructed to process the material from the perspective supported by the graphic or from an alternative perspective, or they received no further instruction. After learning, they were asked to recall the learning content from different perspectives by completing graphs of different formats as accurately as possible. Learners' recall was more accurate if the format of recall was the same as the learning format which indicates surface structure influences. However, participants also showed more accurate recall when they remembered the content from a perspective emphasizing the deep structure, regardless of the graphics format presented before. This included better recall of what they had not seen than of what they really had seen before. That is, deep structure effects overrode surface effects. Depending on context conditions, stimulation of additional cognitive processing by instruction had partially positive and partially negative effects.

Leaders and Heroes: Modern Day Archetypes

This paper explores the salience of archetypes through modern day idealization of leaders as heroes. The body of research in evolutionary psychology and ethology provide support for archetypal theory and the influence of archetypes (Hart & Brady, 2005; Maloney, 1999). Our idealized self is reflected in archetypes and it is possible that we draw on archetypal themes to compensate for a reduction in meaning in our modern day work life. Archetypal priming can touch a person’s true self and result in increased meaning in life. According to Faber and Mayer (2009), drawing from Jung (1968), an archetype is an, “internal mental model of a typical, generic story character to which an observer might resonate emotionally” (p. 307). Most contemporary researchers maintain that archetypal models are transmitted through culture rather than biology, as Jung originally argued (Faber & Mayer, 2009). Archetypes as mental models can be likened to image schemas, foundational mind/brain structures that are developed during human pre-verbal experience (Merchant, 2009). Such schemas are recurring within cognitive processes and establish patterns of understanding and reasoning. Archetypes can provide an operative paradigm or schema in which an individual can experience the world, be compelled to action, and provide a model for behavior. Maloney (1999) cites the growing body of research from several fields that support the premise that the mind is innately structured. Early knowledge persistently and consequentially shapes both subjective experience and behavior. With mental models, an individual has a framework with which to operate, understand, and explore. Archetypes are expressed in a number of ways, especially through myths, cultural stories, and tribal lore (Jung, 1968). Archetypes are generic story characters, but often represent key roles in narratives, that possess familiar and consistent traits and converge around central life themes (Faber & Mayer, 2009; Jung, 1968). A study by Faber and Mayer (2009, p. 314) demonstrates that people can reliably indentify individual archetypes in rich media sources. In addition, Maloney (1999) found that an individual considering a hypothetical long-term relationship with an archetypal image, including anthropomorphic and non-anthropomorphic images, can evoke affective responses, as was theorized by Jung. Such studies support the premise that an “archetype itself is never directly expressed in a symbol or image, but rather, it guides mental use of images and symbols to conform to certain themes or motifs that are found everywhere” (Hart & Brady, 2005). Archetypal themes such as quests, attachments, and conflict are robustly relevant in adult cognition. There is a saliency to archetypal images that can elicit certain responses. The 1 Shadraconis: Leaders and Heroes: Modern Day Archetypes

Challenges in managing a multi-sectoral health promotion program

Purpose– The purpose of this paper is to investigate program management teams' views on issues and challenges in managing a large, multi-sectoral child health promotion program in Sweden.Design/methodology/approach– In total, 17 participants representing two autonomous program management teams, one strategic and one operational, were interviewed. Analysis of interview data was complemented with reviews of program documents.Findings– Program management teams identified important issues concerning the program's formal structure, goals, role distribution, and change and dissemination processes, but lacked a shared mental model of the situation. Inter and intra group communication, long- and short-term strategic planning were further areas in need of improvement. While issues and challenges might seem to be agreed upon by the program's change agents, closer inspection reveals variation in key characteristics as well as in perspectives on solutions.Originality/value– Health promotion programs are challenging. Researchers trying to understand program success have focused on particular interventions, contextual factors and program recipients. Less research has focused on the internal processes of teams tasked with wide-ranging change mandates and the effects such processes can have on program outcomes. This study contributes to a deeper understanding on internal processes and mental models of change agent teams.

How do we use the past to predict the future in oculomotor search?

A variety of findings suggest that when conducting visual search, we can exploit cues that are statistically related to a target’s location. But is this the result of heuristic mechanisms or an internal model that tracks the statistics of the environment? Here, connections are made between the two explanations, and four models are assessed to probe the mechanisms underlying prediction in search. Participants conducted a simple gaze-contingent search task with five conditions, each of which consists of different combinations of 1st and 2nd order statistics. People’s exploration behaviour adapted to the statistical rules governing target behaviour. Behaviour was most consistent with a model that represents transitions from one location to another, and that makes the underlying assumption that the world is dynamic. This assumption that the world is changeable could not be overridden despite task instruction and nearly 1h of exposure to unchanging statistics. This means that while people may be suboptimal in some experimental contexts, it may be because their internal mental model makes assumptions that are adaptive in a complex, changeable world.

A model-based approach for assessment and motivation

Representations support learning and instruction in many ways. Two key aspects of representations are discussed in this paper. First we briefly review the research literature about cognition and processing internal mental models. The emphasis is on the role that mental models play in critical reasoning and problem solving. We then present a theoretically-grounded rationale for taking internal mental representations into account when designing and implementing support for learning. The emphasis is on interaction with meaningful problems. Lastly, we present research that has led to a conceptual framework for integrating models into learning environments that includes technologies for formative assessment and motivation.

VisWeek Keynote Address

This talk will discuss the role of visual thinking in scientific discovery and technological invention. Visual thinking uses picture-like representations as internal mental models or as external depictions such as diagrams. The first part of the talk will analyze the role of visual thinking in 100 great discoveries and 100 great inventions. The second part will discuss the contribution of visual thinking to developing new theories in the social sciences based on advances in cognitive science. Cognitive-affective mapping is a new technique for visualizing the role of emotion in social cognition. EMPATHICA is a new graphical system for resolving conflicts by increasing empathy using cognitive-affective maps.

Are People Successful at Learning Sequences of Actions on a Perceptual Matching Task?

We report the results of an experiment in which human subjects were trained to perform a perceptual matching task. Subjects were asked to manipulate comparison objects until they matched target objects using the fewest manipulations possible. An unusual feature of the experimental task is that efficient performance requires an understanding of the hidden or latent causal structure governing the relationships between actions and perceptual outcomes. We use two benchmarks to evaluate the quality of subjects' learning. One benchmark is based on optimal performance as calculated by a dynamic programming procedure. The other is based on an adaptive computational agent that uses a reinforcement-learning method known as Q-learning to learn to perform the task. Our analyses suggest that subjects were successful learners. In particular, they learned to perform the perceptual matching task in a near-optimal manner (i.e., using a small number of manipulations) at the end of training. Subjects were able to achieve near-optimal performance because they learned, at least partially, the causal structure underlying the task. In addition, subjects' performances were broadly consistent with those of model-based reinforcement-learning agents that built and used internal models of how their actions influenced the external environment. We hypothesize that people will achieve near-optimal performances on tasks requiring sequences of action-especially sensorimotor tasks with underlying latent causal structures-when they can detect the effects of their actions on the environment, and when they can represent and reason about these effects using an internal mental model.

An analysis of safe driving using concurrent tasks: effects of making decisions from the traffic sign information and performing verbal versus spatial-imagery tasks

Driving requires performing different subtasks concurrently that can interfere. Specifically, two driving sub-tasks are analysed in this work: Participants judge manoeuvres regulated by traffic signs (turn left or turn right...