Mario Mikulincer and Phillip R. Shaver
According to attachment theory (Bowlby, 1973, 1982), the optimal functioning of the attachment behavioral system and the resulting sense of security in dealing with life’s challenges and difficulties facilitate the functioning of other behavioral systems, including the caregiving system that governs the activation of prosocial behavior and compassionate acts of helping needy others. In this chapter, we focus on what we have learned about the interplay of the attachment and caregiving systems and their effects on compassion and altruism. We begin by explaining the behavioral system construct in more detail and show how individual differences in a person’s attachment system affect the functioning of the caregiving system. We review examples from the literature on attachment, focusing on what attachment theorists call providing a “safe haven” for needy others. We then review studies that have shown how individual differences in attachment affect empathy, compassion, and support provision.
This chapter is from the forthcoming The Oxford Handbook of Affective Computing edited by Rafael Calvo, Sidney K. D'Mello, Jonathan Gratch, and Arvid Kappas. This chapter traces literary and cinematic representations of intelligent machines in order to provide background for the fantasies and implicit assumptions that accompany these figures in contemporary popular culture. Using examples from media depictions of robots, androids, cyborgs, and computers, this analysis offers a historical and theoretical overview of the cultural archive of fictional robots and intelligent machines—an archive that implicitly affects contemporary responses to technological projects.
Carlo Strapparava and Rada Mihalcea
This chapter is from the forthcoming The Oxford Handbook of Affective Computing edited by Rafael Calvo, Sidney K. D'Mello, Jonathan Gratch, and Arvid Kappas. The field of affective natural language processing (NLP), in particular the recognition of emotion in text, presents many challenges. Nonetheless with current NLP techniques it is possible to approach the problem with interesting results, opening up exciting applicative perspectives for the future. In this chapter we present some explorations in dealing with the automatic recognition of affect in text. We start by describing some available lexical resources, the problem of creating a “gold standard” using emotion annotations, and the affective text task at SemEval-2007, an evaluation contest of computational semantic analysis systems. That task focused on the classification of emotions in news headlines and was meant to explore the connection between emotions and lexical semantics. Then we approach the problem of recognizing emotions in texts, presenting some state-of-the-art knowledge- and corpus-based methods. We conclude by presenting two promising lines of research in the field of affective NLP. The first approaches the related task of humor recognition; the second proposes the exploitation of extralinguistic features (e.g., music) for emotion detection.
Jacqueline Kory and Sidney D'Mello
This chapter is from the forthcoming The Oxford Handbook of Affective Computing edited by Rafael Calvo, Sidney K. D'Mello, Jonathan Gratch, and Arvid Kappas. The ability to reliably and ethically elicit affective states in the laboratory is critical in studying and developing systems that can detect, interpret, and adapt to human affect. Many methods for eliciting emotions have been developed. In general, they involve presenting a stimulus to evoke a response from one or more emotion response systems. The nature of the stimulus varies widely. Passive methods include the presentation of emotional images, film clips, and music. Active methods can involve social or dyadic interactions with other people or behavioral manipulation in which an individual is instructed to adopt facial expressions, postures, or other emotionally relevant behaviors. This chapter discusses exemplar methods of each type, discusses advantages and disadvantages of each method, and briefly summarizes some additional methods.
Ronald C. Arkin and Lilia Moshkina
This chapter is from the forthcoming The Oxford Handbook of Affective Computing edited by Rafael Calvo, Sidney K. D'Mello, Jonathan Gratch, and Arvid Kappas. More and more, robots are expected to interact with humans in a social, easily understandable manner, which presupposes effective use of robot affect. This chapter provides a brief overview of research advances into this important aspect of human-robot interaction. In particular, we focus on the benefit provided for a human interacting with a robot using mechanisms for increasing the bandwidth in communication, including nonverbal methods to create a more effective and stronger relationship between artifact and person. This is illustrated in the context of a range of robot architectural exemplars.
Writing involves complex affective and cognitive processes highly influenced by the environments wherein we write, which are undergoing critical change. Today writing is typically performed using digital devices connected to the Internet, enabling writers to interact with content and with other people in new ways. This increased interconnectedness offers new opportunities as well as challenges. This chapter proposes a new type of tool, Reflective Writing Studios (RWS), which can be used to study writing phenomena in an encompassing way, taking into account the writer’s physical and social surroundings and his or her emotions, mental states, and cognitive processes. The chapter introduces the architecture for an affect-aware multimodal interaction system, its components, and their evaluation. Two types of data are used: structured information about the activity and multimodal sensor data from the writer and the environment. This framework opens new avenues for research in terms of multimodal data collection and interpretation.
Christian Mühl, Dirk Heylen, and Anton Nijholt
This chapter is from the forthcoming The Oxford Handbook of Affective Computing edited by Rafael Calvo, Sidney K. D'Mello, Jonathan Gratch, and Arvid Kappas. The brain is involved in the registration, evaluation, and representation of emotional events and in the subsequent planning and execution of appropriate actions. Novel interface technologies—so-called affective brain-computer interfaces (aBCI)—can use this rich neural information, occurring in response to affective stimulation, for the detection of the user’s affective state. This chapter gives an overview of the promises and challenges that arise from the possibility of neurophysiology-based affect detection, with a special focus on electrophysiological signals. After outlining the potential of aBCI relative to other sensing modalities, the reader is introduced to the neurophysiological and neurotechnological background of this interface technology. Potential application scenarios are situated in a general framework of brain-computer interfaces. Finally, the main scientific and technological challenges that have yet to be solved on the way toward reliable affective brain-computer interfaces are discussed.
Daniel S. Messinger, Leticia Lobo Duvivier, Zachary E. Warren, Mohammad Mahoor, Jason Baker, Anne S. Warlaumont, and Paul Ruvolo
This chapter is from the forthcoming The Oxford Handbook of Affective Computing edited by Rafael Calvo, Sidney K. D'Mello, Jonathan Gratch, and Arvid Kappas. Affective computing can illuminate early emotional dynamics and provide tools for intervention in disordered emotional functioning. This chapter reviews affective computing approaches to understanding emotional communication in typically developing children and children with an autism spectrum disorder (ASD). It covers the application of automated measurement of the dynamics of emotional expression and discusses advances in the modeling of infant and parent interactions based on insights from time-series analysis, machine learning, and recurrence theory. The authors discuss progress in the automated measurement of vocalization in infants and children and new methods for the efficient measurement of sympathetic activation and its application in children with ASD. They conclude by presenting translational applications of affective computing to children with ASD, including the use of embodied conversational agents (ECAs) to understand and influence the affective dynamics of learning, and the use of robots to improve the social and emotional functioning of children with ASD.
Jonathan Gratch and Stacy C. Marsella
This chapter is from the forthcoming The Oxford Handbook of Affective Computing edited by Rafael Calvo, Sidney K. D'Mello, Jonathan Gratch, and Arvid Kappas. This chapter discusses appraisal theory, the most influential theory of emotion in affective computing today, including how appraisal theory arose, some of its well-known variants, and why appraisal theory plays such a prominent role in computational models of emotion. The authors describe the component model framework, a useful framework for organizing and contrasting alternative computational models of emotion and outline some of the contemporary computational approaches based on appraisal theory and the practical systems they help support. Finally, the authors discuss open challenges and future directions.
This chapter begins with Bowlby’s theoretical work in the 1940s and 1950s and the evolution of attachment theory. Ainsworth’s seminal empirical research in the 1960s and 1970s, introducing the concepts of attachment security and caregiver sensitivity, is then discussed. This early theoretical and empirical research on infant attachment is evaluated in light of key findings published in the intervening years. Current views on factors predicting attachment security are presented, focusing on parental state of mind with regard to attachment, the quality of infant–parent interaction, aspects of the social environment, and child-centered factors. The chapter concludes with a summary of important questions on attachment that remain unanswered.