One of the main challenges in affective computing is the development of models to represent the information that is inherent to emotions. It is necessary to consider that the terms used by humans to name emotions depend on the culture and language used. This article presents an experiment-based method to represent and adapt emotion terms to different cultural environments. We propose using circular boxplots to analyze the distribution of emotions in the Pleasure-Arousal space. From the results of this analysis, we define a new cross-cultural representation model of emotions in which each emotion term is assigned to an area in the Pleasure-Arousal space. An emotion is represented by a vector in which the direction indicates the type, and the module indicates the intensity of the emotion. We propose two methods based on fuzzy logic to represent and express emotions: the emotion representation process in which the term associated with the recognized emotion is defuzzified and projected as a vector in the Pleasure-Arousal space; and the emotion expression process in which a fuzzification of the vector is produced, generating a fuzzy emotion term that is adapted to the culture and language in which the emotion will be used.

Affective empathy is an indispensable ability for humans and other species' harmonious social lives, motivating altruistic behavior, such as consolation and aid-giving. How to build an affective empathy computational model has attracted extensive attention in recent years. Most affective empathy models focus on the recognition and simulation of facial expressions or emotional speech of humans, namely Affective Computing. However, these studies lack the guidance of neural mechanisms of affective empathy. From a neuroscience perspective, affective empathy is formed gradually during the individual development process: experiencing own emotion—forming the corresponding Mirror Neuron System (MNS)—understanding the emotions of others through the mirror mechanism. Inspired by this neural mechanism, we constructed a brain-inspired affective empathy computational model, this model contains two submodels: (1) We designed an Artificial Pain Model inspired by the Free Energy Principle (FEP) to the simulate pain generation process in living organisms. (2) We build an affective empathy spiking neural network (AE-SNN) that simulates the mirror mechanism of MNS and has self-other differentiation ability. We apply the brain-inspired affective empathy computational model to the pain empathy and altruistic rescue task to achieve the rescue of companions by intelligent agents. To the best of our knowledge, our study is the first one to reproduce the emergence process of mirror neurons and anti-mirror neurons in the SNN field. Compared with traditional affective empathy computational models, our model is more biologically plausible, and it provides a new perspective for achieving artificial affective empathy, which has special potential for the social robots field in the future.

Emotional artificial intelligence (AI)—systems that infer, simulate, or influence human feelings—create ethical risks that existing frameworks of privacy, transparency, and oversight cannot fully address. This paper advances the concept of Affective Sovereignty : the right of individuals to remain the ultimate interpreters of their own emotions. We make four contributions. First, we develop formal foundations by decomposing risk functions to capture interpretive override as a measurable cost. Second, we propose a Sovereign-by-Design architecture that embeds safeguards and contestability into the machine learning lifecycle. Third, we operationalize sovereignty through new metrics—the Interpretive Override Score (IOS), After-correction Misalignment Rate (AMR), and Affective Divergence (AD)—and demonstrate their use in a proof-of-concept simulation. Fourth, we link technical design to governance by introducing the Affective Sovereignty Contract (ASC) , a machine-readable policy layer, and by issuing a Declaration of Affective Sovereignty as a normative anchor for regulation. Together, these elements offer a computational framework for aligning emotional AI with human dignity and autonomy, moving beyond abstract principles toward enforceable, testable standards. In proof-of-mechanism simulations with $$k=10$$ random seeds, enforcing DRIFT (Dynamic Risk and Interpretability Feedback Throttling) with policy constraints reduces the Interpretive Override Score (IOS) from $$32.4\%\pm 3.8$$ (baseline) to $$14.1\%\pm 2.9$$ , demonstrating measurable preservation of affective sovereignty with quantified variability. Results reported here are based on proof-of-mechanism simulations; a preregistered human-subject evaluation ( $$n=48$$ ) is planned and has not yet been conducted.

Affective - that is, emotionally aware - interactions with virtual humans contribute to improved user experience in both immersive and non-immersive virtual worlds. Some important open problems in this area are how to generate plausible moment-to-moment emotions in virtual humans, how to enable and model the transfer of emotions between multiple virtual humans, and how these emotions change and are changed by the environment. Motivated by these open questions, this paper presents a simulation study for the computational models of emotion and its shift and transference between characters.

With the advent of modern day computational power, there is a great deal of interest in the simulation and modeling of complex biological systems. A significant effort is being made to develop generalized software packages for the simulation of cellular processes, metabolic pathways and complex biochemical reaction systems. The advantages to being able to implement and simulate complex biological systems in a virtual environment are several. Simulations of this type, if sufficiently detailed, provide experimental physiologists with the ability to visualize the dynamics of a given biological system of interest. The validity of hypotheses related to the system under study can be tested in a virtual environment prior to carrying out experimental studies. We discuss a systematic approach by which certain reaction balance equations can be transformed into equivalent circuit models that may then be implemented and simulated using SPICE (Simulation Program with Integrated Circuit Emphasis). To introduce the methodology, we develop a simulation for a single ligand-receptor interaction and then we utilize this framework to implement a simulation of nicotinic acetylcholine receptor kinetics at the postsynaptic membrane of the neuromuscular junction. Although the example studies that we present are specific to biochemical reaction systems associated with cellular processes, the procedure is equally applicable to any biochemical or chemical process for which analogous systems of mass bal

must be informed by advances in neuroscience, such as the study of neural networks, brain plasticity, and the biochemical basis of cognition and behavior Philosophy of mind is a branch of philosophy that deals with the nature of the mind and its relation to the body and the external world. The mind–body problem is a paradigmatic issue in philosophy of mind, although a number of other issues are addressed, such as the hard problem of consciousness and the nature of particular mental states. Aspects of the mind that are studied include mental events, Neurophilosophy is an interdisciplinary field that examines the intersection of neuroscience and philosophy, particularly focusing on how neuroscientific findings inform and challenge traditional arguments in the philosophy of mind, offering insights into the nature of consciousness, cognition, and the mind-brain relationship. Patricia Churchland argues for a deep integration of neuroscience and philosophy, emphasizing that understanding the mind requires grounding philosophical questions in empirical findings about the brain. Churchland challenges traditional dualistic and purely conceptual approaches to the mind, advocating for a materialistic framework where mental phenomena are understood as brain processes. She posits that philosophical theories of mind must be informed by advances in neuroscience, such as the study of neural networks, brain plasticity, and the biochemical basis of cognition and behavior. Churchland critiques the idea that introspection or purely conceptual analysis can sufficiently explain consciousness, arguing instead that empirical methods can illuminate how subjective experiences arise from neural mechanisms. An unsolved question in neuroscience and the philosophy of mind is the binding problem, which is the problem of how objects, background, and abstract or emotional features are combined into a single experience. It is considered a "problem" because no complete model exists. The binding problem can be subdivided into the four areas of perception, neuroscience, cognitive science, and the philosophy of mind. It includes general considerations on coordination, the subjective unity of perception, and variable binding. Another related problem is known as the boundary problem. The boundary problem is essentially the inverse of the binding problem, and asks how binding stops occurring and what prevents other neurological phenomena from being included in first-person perspectives, giving first-person perspectives hard boundaries. Recent interdisciplinary scholarship has also highlighted the importance of first-person subjective experience in understanding consciousness and mental phenomena. One recent review argues that distinguishing…

Methods in genomic neuroscience. Methods and new frontiers in neuroscience, CRC Press, Boca … Biomedical Engineering: Signal Processing and Physiological Systems Modeling Suresh R. Devasahayam Models … creation of biologically plausible computational models aiming at modeling and understanding