Tune in to your emotions: a robust personalized affective music player

The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listeners’ personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application

Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals

Handbook of Biobehavioral Approaches to self-regulation

Self-regulation is a vital psychological process that allows people to guide their behavior in pursuit of their goals. This chapter introduces the present edited volume as the first state-of-the-art overview of biobehavioral approaches to self-regulation. We start by briefly discussing the origins and history of self-regulation research, which was rooted in social and cognitive psychology. Humans’ biological machinery and physiological processes have traditionally been neglected in self-regulation research, but fortunately this has changed over the past 10 years. The upsurge of interest in the biobehavioral foundations of self-regulation is reflected in the remaining 25 chapters of this book. These chapters highlight the involvement of the central nervous system, the autonomic and peripheral nervous system, and the interaction of both systems in self-regulation. The chapters are written by a selection of highly distinguished researchers in the field, and consider many different physiological systems and propose multiple theoretical perspectives. As such, this volume represents a starting point for readers to get a broad and diverse sampling of the field. We hope that the volume will stimulate scholars and practitioners to develop new ideas and applications on the biological processes that underlie the self-regulation of behavior

Protective inhibition of self-regulation and motivation: Extending a classic pavlovian principle to social and personality functioning

How can people master their own thoughts, feelings, and actions? This question is central to the scientific study of self-regulation. The behavioral side of self-regulation has been extensively investigated over the last decades, but the biological machinery that allows people to self-regulate has mostly remained vague and unspecified. Handbook of Biobehavioral Approaches to Self-Regulation corrects this imbalance. Moving beyond traditional mind-body dualities, the various contributions in the book examine how self-regulation becomes established in cardiovascular, hormonal, and central nervous systems. Particular attention is given to the dynamic interplay between affect and cognition in self-regulation. The book also addresses the psychobiology of effort, the impact of depression on self-regulation, the development of self-regulation, and the question what causes self-regulation to succeed or fail. These novel perspectives provide readers with a new, biologically informed understanding of self-awareness and self-agency. Among the topics being covered are: Self-regulation in an evolutionary perspective. The muscle metaphor in self-regulation in the light of current theorizing on muscle physiology. From distraction to mindfulness: psychological and neural mechanisms of attention strategies in self-regulation. Self-regulation in social decision-making: a neurobiological perspective. Mental effort: brain and autonomic correlates in health and disease. A basic and applied model of the body-mind system. Handbook of Biobehavioral Approaches to Self-Regulation provides a wealth of theoretical insights into self-regulation, with great potential for future applications for improving self-regulation in everyday life settings, including education, work, health, and interpersonal relationships. The book highlights a host of exciting new ideas and directions and is sure to provoke a great deal of thought and discussion among researchers, practitioners, and graduate-level students in psychology, education, neuroscience, medicine, and behavioral economics