Computational models of emotion

Recent years have seen a significant expansion in research on computational models of human emotional processes, driven both by their potential for basic research on emotion and cognition as well as their promise for an ever-increasing range of applications. This has led to a truly interdisciplinary, mutually beneficial partnership between emotion research in psychology and in computational science, of which this volume is an exemplar. To understand this partnership and its potential for transforming existing practices in emotion research across disciplines and for disclosing important novel areas of research, we explore in this chapter the history of work in computational models of emotion including the various uses to which they have been put, the theoretical traditions that have shaped their development, and how these uses and traditions are reflected in their underlying architectures. For an outsider to the field, the last 15 years have seen the development of a seemingly bewildering array of competing and complementary computational models. Figure 1.2. 1 lists a ‘family tree’ of a few of the significant models and the theoretical traditions from which they stem. Although there has been a proliferation of work, the field is far from mature: the goals that a model is designed to achieve are not always clearly articulated; research is rarely incremental, more often returning to motivating theories rather than extending prior computational approaches; and rarely are models contrasted with each other in terms of their ability to achieve their set goals. Contributing to potential confusion is the reality that computational models are complex systems embodying a number of, sometimes unarticulated, design decisions and assumptions inherited from the psychological and computational traditions from which they emerged, a circumstance made worse by the lack of a commonly accepted lexicon even for designating these distinctions. In this chapter, we lay out the work on computational models of emotion in an attempt to reveal the common uses to which they may be put and the underlying techniques and assumptions from which the models are built. Our aim is to present conceptual distinctions and common terminology that can aid in discussion and comparison of competing models. Our hope is that this will not only facilitate an understanding of the field for outside researchers but work towards a lexicon that can help foster the maturity of the field towards more incremental research. In characterizing different computational models of emotion, we begin by describing interdisciplinary uses to which computational models may be put, including their uses in improving human–computer interaction, in enhancing general models of intelligence, and as methodological tools for furthering our understanding of human behaviour. We next discuss how models have been built, including the underlying theoretical traditions that have shaped their development. These differing theoretical perspectives often conceptualize emotion in quite different ways, emphasizing different scenarios and proposed functions, different component processes, and different linkages between these components. It should then come as no surprise that such