Modeling Emotion: Arousal's Impact on Memory

Modeling Emotion: Arousal’s Impact on Memory Robert E. Cochran (rec38@drexel.edu) Drexel University, Department of Computer Science 3141 Chestnut Street, Philadelphia, PA 19104 USA Frank J. Lee (fjl@cs.drexel.edu) Drexel University, Department of Computer Science 3141 Chestnut Street, Philadelphia, PA 19104 USA Eric Chown (echown@bowdoin.edu) Bowdoin College, Brunswick, Maine 04011 USA of the interaction of arousal and memory, and this work is described in the sections that follow. Abstract It is generally accepted that an integrated cognitive architecture is required to fully explain complex functions of the brain such as learning and behavior. However, typical architectures do not include a theory of emotion. This article proposes a framework of emotion suitable for inclusion in an integrated architecture and examines the impact of arousal on memory and how a particular architecture that does not account for emotion fails to accurately model a classic psychological experiment. Simulations and memory models are also presented that account for arousal’s impact on memory. Modeling Emotion Emotions are often tied to appraisals. Fear, for example, can be seen as a reaction to a prediction of danger. The emotional model we are pursuing is in line with this point of view except that the “appraisals” are not cognitive, but are instead mechanistic. Pain, for example, is a simple “appraisal” mechanism that is crucial to emotions. This model, put forth by Chown and others (Chown, 2006; Kaplan, 1991), posits that emotions are a fast, automatic assessment system consisting of three core parts or mechanisms: m 1 : an arousal system that provides a measure of importance, m 2 : a pleasure/pain system that assesses valence, and m 3 : a clarity/confusion system that provides a measure of competence. All of these components are automatic (one does not decide to be confused for example), but can be affected by cognition. These mechanisms are chosen due to their ability to model a broad range of the emotional spectrum and can be used as a foundation for looking at how cognition and emotion interact. Typical emotional tags (angry, sad, happy) can be generated through a mapping of these mechanisms: (f: m 1 x m 2 x m 3 o tag). Introduction The breadth and complexity of our emotions are critical to our human experience. In many ways, the sensation or feeling of these emotions is what makes life worth living. These emotions also provide adaptive advantages important for our well being and survival. They facilitate a compacting of our experience that can result in a fast and desirable decision without requiring a substantial amount of time for deliberation. This effect can be critical when a quick decision is necessary but all the relevant information is either not available or the time required to process would be too great. It is our belief that emotion should be included as an integral component of any integrated cognitive architecture that strives to provide a rich and accurate explanation and framework for the emulation of human cognition. Such a model would have many applications in either an analysis or synthesis mode, including behavioral finance, interactive entertainment, automotive telematics, and computer assisted education. The figure below illustrates how these mechanisms can be viewed in a 3-state space and how the model may drive these states over time in response to various stimuli. Therefore, development is underway of a comprehensive computational model of emotion that is targeted at integration with cognition. This model is being formulated in terms of general mechanisms of the emotional system that interact with cognition and provide a mapping to typical emotional states, such as anger and happiness. An early step in this development is an analysis and simulation Figure 1: Representation of Emotion