Modeling the Mechanisms of Emotion Effects on Cognition

Proceedings of the second international joint conference on Autonomous agents and multiagent systems - AAMAS '03, 2003

This paper describes the implementation and evaluation of a framework for modeling emotions in complex, decision-making agents. Sponsored by U.S. Army Research Institute (ARI), the objective of this research is to make the decision-making process of complex agents less predictable and more realistic, by incorporating emotional factors that affect humans. In tune with modern theories of emotions, we regard emotions essentially as subconscious signals and evaluations that inform, modify, and receive feedback from a variety of sources including higher cognitive processes and the sensorimotor system. Thus, our work explicitly distinguishes the subconscious processes (in a connectionist implementation) and the decision making that is subject to emotional influences (in a symbolic cognitive architecture).

IEEE 10th International Conference on Cognitive Informatics and Cognitive Computing (ICCI-CC'11), 2011

An interactive motivation-attitude theory is developed based on the Layered Reference Model of the Brain (LRMB) and the Object-Attribute-Relation (OAR) model. This paper presents a rigorous model of human perceptual processes such as emotions, motivations, and attitudes. A set of mathematical models and formal cognitive processes of perception is developed. Interactions and relationships between motivation and attitude are formally described in Real-Time Process Algebra (RTPA). Applications of the mathematical models of motivations and attitudes in software engineering are demonstrated. This work is a part of the formalization of LRMB, which provides a comprehensive model for explaining the fundamental cognitive processes of the brain and their interactions. This work demonstrates that the complicated human emotional and perceptual phenomena can be rigorously modeled and formally treated based on cognitive informatics theories and denotational mathematics.

Cognitive Systems Research, 2009

Existing models that integrate emotion and cognition generally do not fully specify why cognition needs emotion and conversely why emotion needs cognition. In this paper, we present a unified computational model that combines an abstract cognitive theory of behavior control (PEACTIDM) and a detailed theory of emotion (based on an appraisal theory), integrated in a theory of cognitive architecture (Soar). The theory of cognitive control specifies a set of required computational functions and their abstract inputs and outputs, while the appraisal theory specifies in more detail the nature of these inputs and outputs and an ontology for their representation. We argue that there is a surprising functional symbiosis between these two independently motivated theories that leads to a deeper theoretical integration than has been previously obtained in other computational treatments of cognition and emotion. We use an implemented model in Soar to test the feasibility of the resulting integrated theory, and explore its implications and predictive power in several task domains.

2019

Emotions play an important role in human cognition and therefore need to be present in the Common Model of Cognition. In this paper, the emotion working group focuses on functional aspects of emotions and describes what we believe are the points of interactions with the Common Model of Cognition. The present paper should not be viewed as a consensus of the group but rather as a first attempt to extract common and divergent aspects of different models of emotions and how they relate to the Common Model of Cognition. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 9th Annual International Conference on Biologically Inspired Cognitive Architectures.

Chinese Science Bulletin, 2009

Cognition and emotion have long been thought of as independent systems. However, recent research in the cognitive and neurobiological sciences has shown that the relationship between cognition and emotion is more interdependent than separate. Based on evidence from behavioral and neuroscientific research, researchers have realized that it is necessary to propose a new conceptual framework to describe the relationship between cognition and emotion. In this article, recent research from behavioral, neuroscientific and developmental research on the interaction between cognition and emotion is summarized, and how the interaction of cognition and emotion might affect computer science and artificial intelligence is discussed. It especially focuses on the implications for affective computing. emotion, cognition, interaction, affective computing

Metacognition in Computation: Papers from the 2005 …, 2005

While research in metacognition has grown significantly in the past 10 years, there has been a relative lack of research devoted to the focused study of the interactions between metacognition and affective processes. Computational models represent a useful tool which can help remedy this situation by constructing causal models of demonstrated correlational relationships, and by generating empirical hypotheses which can be verified experimentally. In this paper we describe enhancements to an existing cognitive-affective architecture that will enable it to perform a subset of metacognitive functions. We focus on modeling the role of a specific metacognitive factor, the feeling of confidence, and the anxiety-linked metacognitive strategy of emotion-focused coping.

Biologically Inspired Cognitive Architectures, 2013

Human-like artificial emotional intelligence is vital for integration of future robots into the human society. This work introduces a general framework for representation and processing of emotional contents in a cognitive architecture, called ''emotional biologically inspired cognitive architecture'' (eBICA). Unlike in previous attempts, in this framework emotional elements are added virtually to all cognitive representations and processes by modifying the main building blocks of the prototype architectures. The key elements are appraisals associated as attributes with schemas and mental states, moral schemas that control patterns of appraisals and represent social emotions, and semantic spaces that give values to appraisals. Proposed principles are tested in an experiment involving human subjects and virtual agents, based on a simple paradigm in imaginary virtual world. It is shown that with moral schemas, but probably not without them, eBICA can account for human behavior in the selected paradigm. The model sheds light on clustering of social emotions and allows for their elegant mathematical description. The new framework will be suitable for implementation of believable emotional intelligence in artifacts, necessary for emotionally informed behavior, collaboration of virtual partners with humans, and self-regulated learning of virtual agents.

2004

We describe a computational framework for emotions and feelings that combines biological, cognitive, and social influences. The framework, Soar-Emote, combines ideas from Damasio and Gratch & Marsella. From Damasio we include physiological influences, ideas about the differences between emotions and feelings, and the direction of causality. From Gratch we include cognitive and social influences, appraisal theory and coping. We give results that show that these systems influence agent behavior for a simple interactive game.

In this paper we present a new neurobiologically-inspired affective cog-nitive architecture: NEUCOGAR (NEUromodulating COGnitive ARchitecture). The objective of NEUCOGAR is the identification of a mapping from the influence of serotonin, dopamine and noradrenaline to the computing processes based on Von Neuman's architecture, in order to implement af-fective phenomena which can operate on the Turing's machine model. As basis of the modeling we use and extend the Lövheims Cube of Emotion with parameters of the Von Neumann architecture. Validation is conducted via simulation on a computing system of dopamine neuromodulation and its effects on the Cortex. In the experimental phase of the project, the increase of computing power and storage redistribution due to emotion stimulus modulated by the dopamine system, confirmed the soundness of the model.