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In this chapter, it is addressed by mathematical analysis how network-oriented modeling relates to the dynamical systems perspective on mental processes. It has been mathematically proven that any dynamical system can be modeled as a temporal-causal network model and that any adaptive dynamical system (of any order) can be modeled by a self-modeling network (of the same order).

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.

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 chapter, 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.

In this chapter it is discussed how a personalised temporal-causal network model can be obtained that fits well to specific characteristics of a person, and his or her connections and further context. A model is an approximation, but always a form of abstraction of a real-world phenomenon. Its accuracy and correctness mainly depend on the chosen abstracting assumptions and the personal and contextual (network) characteristics defining the model. Depending on the complexity of the model, the number of its characteristics can vary from just a couple to thousands. These network characteristics usually represent specific features or properties of the modelled phenomenon, for example, for modelling human processes personality traits or social interaction properties. No values for such characteristics are given at forehand. From a more general and abstract view, they can be considered parameters of the model. Estimation of such parameters for a given model is a nontrivial task. In this chapter, it is discussed how this can be addressed for temporal-causal network models based on the parameter tuning method of Simulated Annealing and a specific component within the dedicated modeling environment, thereby making use of MATLAB’s built-in optimser Optimtool.

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.

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.

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 chapter, 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.

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.

In this chapter, a multilevel cognitive architecture is introduced that can be used to model mental processes in clients of psychotherapeutic sessions and in particular the mental (self-)models they have about themselves. The architecture does not only cover base level mental processes but also mental processes involving self-referencing, self-awareness and self-interpretation. To this end, the cognitive architecture was designed according to four levels, where (part of) the structure of each level is represented by an explicit self-model of it at the next-higher level of the architecture. At that next-higher level, states represent part of the structure of the level below; these states have a referencing relation to it. In this way the overall architecture includes its own overall self-model. The cognitive architecture was evaluated for a case study of a realistic type of therapeutic session from clinical practice.

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.

Shows Mental Models in Individual Processes and Social ProcessesIncludes wide details about the Self-Modeling Network ModelingWritten by experts in the field

Networks provide an intuitive, declarative way of modeling with a wide scope of applicability. In many cases also adaptivity of a network plays a role, which easily leads to less declarative and transparent forms of modeling by using algorithmic or procedural descriptions for the adaptation processes. This chapter addresses this by exploiting the notion of self-modeling network that has been developed recently. Using that, adaptivity is obtained by adding a self-model to a given base network, with states that represent part of the base network’s structure. This adds a next level to the base network, resulting in a two-level network. This construction can easily be iterated to obtain more levels so that multiple orders of adaptation can be covered as well. This brings networks to a next level in more than one way. In particular, a three-level self-modeling network can be used to integrate dynamics, adaptivity and control in one network. It is shown how this can be used to design network models for mental model handling.

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.

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.

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.