Adaptive Temporal-causal Network Model Research Articles

An adaptive temporal-causal network model to analyse extinction of communication over time

The persistence of information communicated between humans is difficult to measure as it is affected by many features. This paper presents an approach to computationally model the cognitive processes of information sharing to describe persistence or extinction of communication in Twitter over time. The adaptive mental network model explains, for example, how an individual can experience information overflow on a topic, and how this affects the sharing of information. Parameter tuning by Simulated Annealing is used to identify characteristics of the network model that fit to empirical data from Twitter. The data collected is related to the independentism in Catalunya, Spain, which is considered a global issue with repercussion in Europe.

Decision Making Under Acute Stress Modeled by an Adaptive Temporal–Causal Network Model

The influence of acute severe stress or extreme emotion based on a Network-Oriented modeling methodology has been addressed here. Adaptive temporal causal network model is an approach to address the phenomena with complexity which cannot be or hard to be explained in a real-world experiment. In the first phase, the suppression of the existing network connections as a consequence of the acute stress modeled and in the second phase relaxing the suppression by giving some time and starting a new learning of the decision making in accordance to presence of stress starts again.

A second-order adaptive temporal-causal network model for age and gender differences in evolving choice of emotion regulation strategies

ABSTRACTEmotion being an essential part of one’s life, its regulation is of utmost importance. The choice of strategies that one employs for regulating their emotions, varies at various stages of their lives and with contexts. Similarly, gender also has an influence on the choice of emotion regulation strategies. This paper presents a second-order adaptive network model for this phenomenon where the choice of strategies varies with age and gender in an adaptive manner. Simulation results for both the genders (male and female) have been provided where for both the genders, choice of emotion regulation strategies changes as age increases. The second-order adaptive network model presented here extends a non-adaptive network model previously introduced at ICCCI’19.

An adaptive temporal-causal network model for social networks based on the homophily and more-becomes-more principle

This study describes the use of adaptive temporal-causal networks to model and simulate the development of mutually interacting opinion states and connections between individuals in social networks. The focus is on adaptive networks combining the homophily principle with the more becomes more principle. The model has been used to analyse a data set concerning opinions about the use of alcohol and tobacco, and friendship relations. The achieved results provide insights in the potential of the approach.

Modeling enabling learning of social interaction based on an adaptive temporal-causal network model

In this study, an adaptive temporal-causal network model is presented for learning of basic skills for social interaction. It focuses on greeting a known person and how that relates to learning how to recognize a person from seeing his or her face. The model involves a Hebbian learning process. The model also addresses avoidance behavior related to enhanced sensory processing sensitivity. In scenarios persons without and with enhanced sensory processing sensitivity are compared. Mathematical analysis was performed to verify correctness of the model.

Computational Analysis of Gender Differences in Coping with Extreme Stressful Emotions

In this paper a computational analysis is presented of differences between men and women in coping with extreme emotions. This analysis is based on an adaptive temporal-causal network model. It takes into account the suppression of connections between preparation states and sensory representations of action effects due to an extreme stressful emotion. It is shown how this model can be used to represent the difference between males and females facing an extreme emotion, thereby performing their own methods in coping with the extreme emotion, for males fight or flight and for females tend-and-befriend.

An adaptive cognitive-social model for mirroring and social bonding during synchronous joint action

This paper presents an adaptive temporal-causal network model of human synchronization and bonding during a joint action. Two adaptive modelling principles were adopted: the Hebbian learning principle for the mirroring process underlying synchronisation, and the Homophily principle representing the social bonding. As validation reported experimental conditions were simulated.