This paper describes an extension of a safety culture within hospital organizations providing more transparency and acknowledgement of all actors, and in particular the parents. It contributes a model architecture to support a hospital to develop such an extended safety culture. It is illustrated for prevention of postpartum depression. Postpartum depression is a commonly known consequence of childbirth for both mothers and fathers. In this research, we computationally analyze the risk factors and lack of support received by fathers. Therefore, we use shared mental models to model the effects of poor and additional communication by healthcare practitioners to mitigate the development of postpartum depression in both the mother and the father. Both individual mental models and shared mental models are considered in the design of the computational model. The paper illustrates the benefits of simple support in terms of communication during childbirth, which has lasting effects, even outside the hospital. For the impact of additional communication, a Virtual Safety Coach is designed that intervenes when necessary to provide support, i.e., when a health care practitioner doesn't. Moreover, organizational learning is also modelled to improve the mental models of both the Safety Coach and the Health Care Practitioner.@en

In this paper, equilibrium analysis for network models is addressed and applied in particular to a network model of multilevel organisational learning. The equilibrium analysis addresses properties of aggregation characteristics and connectivity characteristics of a network. For aggregation characteristics, it is shown how certain classes of nonlinear functions enable equilibrium analysis of the emerging dynamics within the network like linear functions do. For connectivity characteristics, by using a form of stratification for the network's strongly connected components, it is shown how equilibrium analysis results can be obtained relating equilibrium values in any component to equilibrium values in (independent) components without incoming connections. In addition, concerning aggregation characteristics, two specific types of nonlinear functions for aggregation in networks (weighted euclidean functions and weighted geometric functions) are analysed. It is illustrated in detail how by using certain function transformations also methods for equilibrium analysis based on a symbolic linear equation solver, can be applied to make predictions about equilibrium values for them. All these results are applied to a network model for organisational learning. Finally, it is analysed in some depth how the function transformations applied can be described by the more general notion of function conjugate relation, also often used for coordinate transformations.@en

This paper describes how the recently developed self-modeling network modeling approach for multilevel organisational learning has been tested on applicability for a real-world case of a project-based organisation. The modeling approach was able to successfully address this complex case by designing a third-order adaptive network model. Doing this, as a form of further innovation three new features have been added to the modeling approach: Recombination of selected high-quality mental model parts, refinement of mental model parts, and distinction between context-sensitive detailed control and global control.@en

This paper addresses formalization and computational modeling of multilevel organizational learning, which is one of the major challenges for the area of organizational learning. It is discussed how various conceptual mechanisms in multilevel organizational learning as identified in the literature, can be formalized by computational mechanisms which provide mathematical formalizations that enable computer simulation. The formalizations have been expressed using a self-modeling network modeling approach.@en

The interactions between a person and his or her primary caregiver shape the attachment pattern blueprint of how this person behaves in intimate relationships later in life. This attachment pattern serves as a mental model that includes both a mental model of the person her or himself and a mental model of the other person. While this attachment pattern has a lifelong effect on an individual, few studies have been conducted on how this attachment style evolves throughout a person’s life. This chapter discusses how a controlled adaptive network was designed and simulated to provide insights into how an attachment pattern is created and how this pattern then evolves as the person develops new intimate relationships at an older age. Two simulated scenarios are described: the first concerns interactions with securely attached persons, and the second scenario simulates an individual with the anxious-avoidant attachment pattern who encounters a securely attached person.@en

In this chapter, an overview of the wide variety of occurrences of mental models in the literature is discussed. They are classified according to two dimensions obtaining four categories of mental models: static-dynamic and world-mental, where static refers to mental models for static world states or for static mental states and dynamic refers to mental models for world processes or for mental processes. In addition, distinctions are made for what can be done by mental models: they can, for example, be (1) used for internal simulation, they can be (2) adapted, and these processes can be (3) controlled. This leads to a global three-level cognitive architecture covering these three ways of handling mental models. It is discussed that in this cognitive architecture reflection principles play an important role to define the interactions between the different levels.@en

People spontaneously synchronize their mental states and behavioral actions when they interact. This paper models general mechanisms that can lead to the emergence of interpersonal synchrony by multiple agents with internal cognitive and affective states. In our simulations, one agent was exposed to a repeated stimulus and the other agent started to synchronize consecutively its movements, affects, conscious emotions and verbal actions with the exposed agent. The behavior displayed by the agents was consistent with theory and empirical evidence from the psychological and neuroscience literature. These results shed new light on the emergence of interpersonal synchrony in a wide variety of settings, from close relationships to psychotherapy. Moreover, the present work could provide a basis for future development of socially responsive virtual agents.@en

People spontaneously synchronize their mental states and behavioral actions when they interact. This paper models general mechanisms that can lead to the emergence of interpersonal synchrony by multiple agents with internal cognitive and affective states. In our simulations, one agent was exposed to a repeated stimulus and the other agent started to synchronize consecutively its movements, affects, conscious emotions and verbal actions with the exposed agent. The behavior displayed by the agents was consistent with theory and empirical evidence from the psychological and neuroscience literature. These results shed new light on the emergence of interpersonal synchrony in a wide variety of settings, from close relationships to psychotherapy. Moreover, the present work could provide a basis for future development of socially responsive virtual agents.@en

Mental models usually are considered to represent a form of information or knowledge about situations or processes in the world or about mental states and processes in humans. In this chapter it is explored how this informational view on mental models relates to two more general principles that have been introduced in recent years: temporal factorisation and criterial causation. Propagated activation of neurons in the brain through their network co-occurs with adaptation of characteristics of this network such as connection strengths or excitability thresholds. According to the principle of criterial causation, these adaptive network characteristics can be interpreted as informational criteria for future activation of a considered neuron. This then is viewed as a form of emergent information formation and use of this information: the activation of neurons as determined by such information is termed criterial causation, where the information defines the criteria. The criterial causation principle strongly relates to the principle of temporal factorisation for the dynamics of the world in general, describing how the world represents information about its past in its present state, which then in turn determines the world’s future. In the chapter, these processes are analysed in more detail and modeled by (adaptive) network models showing how mental models that are learnt can be interpreted as emerging information according to the principles of criterial causation and temporal factorisation.@en

In this chapter, the role of subjective elements and control in social network adaptation is analysed computationally. In particular, it is analysed: (1) how the coevolution of social contagion and bonding by homophily may be controlled by the persons involved, and (2) how subjective representation states (e.g., what they know) can play a role in this coevolution and its control. To address this, a second-order adaptive social network model is presented in which persons do have a form of control over the coevolution process, and, in relation to this, their bonding depends on their subjective representation states about themselves and about each other, and social contagion depends on their subjective representation states about their connections.@en

Religion is an important aspect of many individuals’ lives. A central element of it is a mental God-model. Such a mental God-model can have different forms and depending on that it has a specific influence on human behaviour. This has been extensively studied from different perspectives. The current study uses a number of these perspectives as a basis for an integrative adaptive network model for formation and use of a mental God-model. It describes the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. Based on the obtained network model, simulations can be done for almost any kind of religion and person, showing different behaviours for persons with different religious backgrounds and characters. The focus here was mainly on the very relevant topic of the influence of religion on human empathy and disempathy. The developed adaptive network model can be valuable for many uses, involving support for a better understanding, and even prediction, of the behaviour of religious individuals and its underlying mechanisms. It is illustrated for a number of different scenarios for different types of persons and religions.@en

Religion is an important aspect of many individuals’ lives. A central element of it is a mental God-model. Such a mental God-model can have different forms and depending on that it has a specific influence on human behaviour. This has been extensively studied from different perspectives. The current study uses a number of these perspectives as a basis for an integrative adaptive network model for formation and use of a mental God-model. It describes the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. Based on the obtained network model, simulations can be done for almost any kind of religion and person, showing different behaviours for persons with different religious backgrounds and characters. The focus here was mainly on the very relevant topic of the influence of religion on human empathy and disempathy. The developed adaptive network model can be valuable for many uses, involving support for a better understanding, and even prediction, of the behaviour of religious individuals and its underlying mechanisms. It is illustrated for a number of different scenarios for different types of persons and religions.@en

This chapter describes a second-order adaptive network model for mental processes making use of shared mental models (SMM) for team performance. The chapter illustrates on the one hand the value of adequate SMM’s for safe and efficient team performance and on the other hand in cases of imperfections of such shared team models how this complicates the team performance. To this end, the adaptive network model covers use, adaptation and control of the shared mental model. It is illustrated for an application context of a medical team performing a tracheal intubation, executed by a nurse and a medical specialist. Simulations illustrate how the adaptive network model is able to address the type of complications that can occur in realistic scenarios.@en

In this paper counterfactual thinking is addressed based on literature mainly from Neuroscience and Psychology. A detailed literature review was conducted in identifying processes, neural correlates and theories related to counterfactual thinking from different disciplines. A familiar scenario with respect to counterfactual thinking was identified. Based on the literature, an adaptive self-modeling network model was designed. This model captures the complex process of counterfactual thinking and the learning and control involved.@en

In this chapter counterfactual thinking is addressed based on literature mainly from Neuroscience and Psychology. A detailed literature review was conducted in identifying processes, neural correlates and theories related to counterfactual thinking from different disciplines. A familiar scenario with respect to counterfactual thinking was identified. Based on the literature, an adaptive self-modeling network model was designed. This model captures the complex process of counterfactual thinking, the mental models that are involved, and the learning and control.@en

In this chapter, the concept of context-dependent realisation of mental models is introduced and discussed. Literature from neuroscience is discussed showing that different types of mental models can use different types of brain areas. Moreover, it is discussed that the same occurs for the formation and adaptation of mental models and the control of these processes. This makes that it is hard to claim that all mental models use the same brain mechanisms and areas. Instead, the notion of context-dependent realisation is proposed here as a better manner to relate neural correlates to mental models. It is shown in some formal detail how this context-dependent realisation approach can be related to well-known perspectives based on bridge principle realisation and interpretation mapping realisation.@en

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-car-driving systems.@en

In this chapter, 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.@en

In this chapter, analysis methods for the dynamics of self-modeling network models in relation to their network structure are presented. In particular, stationary points and equilibria are addressed and related to the network structure. It is shown how such analyses can be used for verification purposes: to verify whether an implemented network model used for simulation is correct with respect to the design description of the network’s structure. An always applicable method is presented first. It is based on substitution of state values from simulations in stationary point or equilibrium equations, which can always be done. In addition, methods are presented that are applicable for certain groups of network models, where the aggregation is specified by combination functions for which equilibrium equations can be solved symbolically. As shown, these methods cover cases of self-model states for adaptation principles such as Hebbian learning for mental networks and Bonding based on homophily for social networks. In addition, such methods are shown to cover cases where the combination functions for aggregation satisfy certain properties such as being monotonically increasing, scalar-free, and normalised. The analysis for this class of functions used for aggregation also takes into account the network’s connectivity in terms of its strongly connected components. This provides a class of functions which includes nonlinear functions but in contrast to often held beliefs, still enables analysis of the emerging network dynamics as well as linear functions do. Within this class, two specific subclasses of nonlinear functions (weighted Euclidean functions and weighted geometric functions) are addressed. Focusing on them in particular, it is illustrated in detail how methods for equilibrium analysis as normally only used for linear functions (based on a symbolic linear equation solver), can be applied to predict the state values in equilibria for such nonlinear cases as well. Finally, it shown how a stratified form of the condensation graph based on a network's strongly connected components can be used in equilibrium analysis.@en

The concept of self-modeling network architecture is the basis of the implementation of a dedicated software environment developed by the author in MATLAB. The environment includes a combination function library and a generic computational self-modeling network engine. It uses as input role matrices specifying in a standardised and compact form the characteristics for the designed network model. Based on this input, the computational self-modeling network engine can be used to generate simulations for the network model, thereby using combination functions from the library. In this chapter, this software environment and how to use it is described in more detail.@en