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Advances in mobile technology have allowed young people to access social media regardless of time of day or geographic location. Communication and behaviors that once took place solely offline have shifted to online contexts. Research has found that viewing problematic media content related to risky sexual activity and drug use may change youths’ beliefs and behaviors about these issues. Because social media is popular with adolescents and emerging adults, we must evaluate how the content that they consume could be related to negative outcomes. By understanding the relation between young people’s social media content and their beliefs and behaviors, we can potentially use media as a tool to reinforce more positive behaviors.

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.

Discussions of architectural beauty have largely taken place outside of empirical science. Over the past decade, however, neuroaesthetics has generated new insights into the neurobiological underpinnings of beauty and art. Now, scientists are also beginning to probe the intersection of neuroscience and architecture. The emerging field of neuroarchitecture aims to investigate the neural and psychological implications of architectural design and to inspire the construction of built environments that support psychological experiences and wellbeing. This chapter explores how neuroarchitecture might develop into an experimental science, drawing lessons from recent developments in neuroaesthetics along the way. An initial neural framework is outlined, the aesthetic triad, in order to contextualize past neuroscientific research on the built environment and to motivate future hypothesis-testing studies. Finally, the potential benefits and limitations of this area of research are discussed.

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.

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.

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.

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.

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.

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.

From their very origins, psychology and sociology have each tended to follow their own path, without taking the other into consideration. This mutual indifference is particularly problematic when studying processes of socialization. On the developmental psychological side, there is perhaps a tendency to consider the child as a “lone explorer,” while on the sociology side, the irreducibility of the social agent to their individual epistemic endeavor is, of course, central. However, socialization is essentially about transmission, from one generation to the next, of ways of doing, thinking, and feeling. In this chapter, the authors argue that culture is mostly about what is socially relevant, and that what is socially relevant can be learned by observing relevant others’ affective expressions. This affective social learning may help close the gap between psychology and sociology by providing different mechanisms that enable individuals to embody, via their developing emotions, the system of cultural relevance in which they live.

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.

The first part of the chapter describes effects of motivation on attention at the behavioural and physiological levels. For example, reward increases detection sensitivity (dprime) in both endogenous attention and exogenous attention tasks, enhances stimulus coding, and influences the filtering of task-irrelevant stimuli. These recent findings are surprising insofar as traditional psychological models have described motivation as a fairly unspecific ‘force’. The results reviewed are far from global. Instead they reflect specific mechanisms that are manifested selectively both at behavioural and neural levels. The second part of the chapter describes the role of attention when emotion-laden visual stimuli are processed. When one considers the bulk of the evidence, emotional processing is revealed to be capacity-limited. Yet, emotional processing is prioritized relative to that of neutral items.

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. Facial expression communicates emotion, intention, and physical state; it also regulates interpersonal behavior. Automated face analysis (AFA) for the detection, synthesis, and understanding of facial expression is a vital focus of basic research. While open research questions remain, the field has become sufficiently mature to support initial applications in a variety of areas. We review (1) human observer‒based approaches to measurement that inform AFA; (2) advances in face detection and tracking, feature extraction, registration, and supervised learning; and (3) applications in action unit and intensity detection, physical pain, psychological distress and depression, detection of deception, interpersonal coordination, expression transfer, and other applications. We consider “user in the loop” as well as fully automated systems and discuss open questions in basic and applied research.

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. As technology for capturing human body movement is becoming more affordable and ubiquitous, the importance of bodily expressions is increasing as a channel for human-computer interaction. In this chapter we provide an overview of the area of automatic emotion recognition from bodily expressions. In particular, we discuss how affective bodily expressions can be captured and described to build recognition models. We briefly review the literature on affective body movement and body posture detection to identify the factors that can affect this process. We then discuss the recent advances in building systems that can automatically track and categorize affective bodily expressions. We conclude by discussing open issues and challenges as well as new directions that are being tackled in this field. We finally briefly direct the attention to aspects of body behavior that are often overlooked; some of which are dictated by the needs of real-world applications.

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 defines the core concepts surrounding biofeedback and denotes their relations. Subsequently, a closed-loop human-machine architecture is introduced in which a biofeedback protocol is executed. This architecture is brought from theory to practice via a personalized affective music player (AMP). Regression and kernel density estimation are applied to model the physiological changes elicited by music. The AMP was validated via a real-world evaluation over the course of several weeks. Results show that our autonomous closed-loop biofeedback system can cope with noisy situations and handle large interindividual differences in the music domain. The AMP augments music listening, where its techniques enable autonomous affect guidance. Our approach provides valuable insights for affective computing and autonomous closed-loop biofeedback systems in general.