Emotions exert profound influences on cognition in biological agents. This is particularly evident in decisionmaking. All of the processes mediating decision-making are affected by emotion: attention, perception, situation assessment, goal-management and action selection, as well as the associated memory processes. Emotion effects, and the associated affective decision biases and heuristics, can be adaptive or maladaptive, depending on their type, magnitude and context. For example, anxiety and fear are associated with preferential processing of high-threat stimuli. This is highly adaptive in situations where survival depends on quick detection of danger and appropriate reaction (e.g., avoid an approaching car that has swerved into your lane). The same bias can be maladaptive if neutral stimuli are judged to be threatening (e.g., a passing car is assumed to be on a collision course and causes you to swerve into a ditch.)
The ability to regulate one's affective state and to induce an affective state in another person is a key component of social and emotional interaction. In this paper we describe an architecture design aimed at modeling these two closely related processes. We first define the high-level functional components of each process, and then use these to refine the specific knowledge necessary for their implementation. Based on this analysis we propose a cognitive architecture capable of modeling affect regulation in the self, and affect induction in the other. We illustrate the functioning of this architecture by way of an example from a peacekeeping training simulation. The architecture is currently being implemented in the context of this simulation training task.
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.
Appraisal processes provide an affective assessment of an agent's current situation, in light of its needs and goals. This paper describes a computational model of the appraisal process, implemented within the broader context of a cognitive agent architecture. A particular focus here is on modeling the interacting influences of states and traits on perception and cognition, including their effects on the appraisal process itself. These effects are modeled by manipulating a series of architecture parameters, such as the speed and processing capacity of the individual modules. The paper presents results of an evaluation experiment modeling the behavior of three types of agents: 'normal', 'anxious', and'aggressive'. The appraisal model generated different affective appraisals of the same set of external circumstances for the different agent types, resulting in distinct emotions, and eventually leading to observable differences in behavior. The paper concludes with a brief discussion of some of the issues encountered during the appraisal model development.
We describe a generic approach for modeling the impact of emotion on cognition, perception, and behavior. The approach can model the effects of transient emotional states, longer moods, and stable personality and temperamental factors. The underlying assumption is that one of the primary ways in which emotions influence cognition and perception is by modulating a variety of processing parameters. We illustrate the approach in the context of both a generic integrated architecture of cognition, and a specific architecture, currently under development, designed to model decision making behavior. In this context, we illustrate how the approach would be instantiated within several representational formalisms (e.g., rules, belief nets). We focus on modeling the impact on tactical decision-making of three specific emotional states that have been studied extensively in experimental psychology: anxiety, negative affect (e.g., depression), and obsessiveness. The proposed approach can then be used both for investigating the interaction between cognition and emotion, and the resulting behavior, and for modeling specific types of personalities in interactive environments.