Roza Gizem Kamiloglu’s research while affiliated with University of Amsterdam and other places

Thankfully, most people feel good most of the time, and we often spontaneously show our positive feelings to others. Yet our scientific understanding of how we express positive emotions is largely based on posed displays. Here, we empirically test how 22 positive emotions are expressed on the face using the participant-negotiated episodic recall method. European respondents (n = 163) narrated emotional events from their lives (data collected from 2018 to 2020). Frequency analyses of the extracted facial expressions (67, 279 datapoints) mostly supported our pre-registered hypotheses for each of the 22 emotions. The results from our confirmatory analyses are illustrated with the aid of animated videos: https://drive.google.com/drive/folders/1pTz_cObpFEYs5hsGUGOPVjUd0nq0Acgt?usp=drive_link. We also applied network models to exploratorily probe whether discrete positive emotions would be associated with specific patterns of facial behaviors. Consistent patterns of facial expressions were found for 19 positive emotions; some were simple (e.g., hope, interest), while others were highly complex and involved multiple facial actions (e.g., amusement, triumph). Together, these results provide a tentative map of what specific positive emotions look like when spontaneously shown on the face. Importantly, none of the positive emotions were expressed merely by smiling alone, demonstrating that people show that they feel good in a wide range of qualitatively different ways.

Listening to and making music is often a distinctly emotional experience. We hear emotions when we listen to music, and music also gives rise to emotional reactions. In recent decades, the relationship between music and emotion has become the focus of psychological research. This chapter introduces psychological perspectives on this topic. We discuss how and why people might respond to music emotionally, and the implications for cross-cultural universality, as well as individual differences. We end by outlining three promising avenues for future research: (1) investigating individual differences in emotional responses to music, (2) leveraging citizen science to improve estimates of cross-cultural similarities, and (3) broadening our understanding of the diverse contexts in which emotional responses to music occur, through naturalistic data collection methods. The growing literature and new advances offer valuable insights into the individual, cultural, and contextual nuances that shape how music moves us.

Laughing is ubiquitous in human life, yet what causes it and how it sounds is highly variable. Considering this diversity, we sought to test whether there are fundamentally different kinds of laughter. Here, we sampled spontaneous laughs (n = 887) from a wide range of everyday situations (e.g. comedic performances and playful pranks). Machine learning analyses showed that laughs produced during tickling are acoustically distinct from laughs triggered by other kinds of events (verbal jokes, watching something funny or witnessing someone else’s misfortune). In a listening experiment (n = 201), participants could accurately identify tickling-induced laughter, validating that such laughter is not only acoustically but also perceptually distinct. A second listening study (n = 210) combined with acoustic analyses indicates that tickling-induced laughter involves less vocal control than laughter produced in other contexts. Together, our results reveal a unique acoustic and perceptual profile of laughter induced by tickling, an evolutionarily ancient play behaviour, distinguishing it clearly from laughter caused by other triggers. This study showcases the power of machine learning in uncovering patterns within complex behavioural phenomena, providing a window into the evolutionary significance of ticking-induced laughter.

Listening to and making music is often a distinctly emotional experience. We hear emotions when we listen to music, and music also gives rise to emotional reactions. In recent decades, the relationship between music and emotion has become the focus of psychological research. This chapter introduces psychological perspectives on this topic. We discuss how and why people might respond to music emotionally, and the implications for cross-cultural universality, as well as individual differences. We end by outlining three promising avenues for future research: (1) investigating individual differences in emotional responses to music, (2) leveraging citizen science to improve estimates of cross-cultural similarities, and (3) broadening our understanding of the diverse contexts in which emotional responses to music occur, through naturalistic data collection methods. The growing literature and new advances offer valuable insights into the individual, cultural, and contextual nuances that shape how music moves us.

What does it mean to feel good? Is our experience of gazing in awe at a majestic mountain fundamentally different than erupting with triumph when our favorite team wins the championship? Here, we use a semantic space approach to test which positive emotional experiences are distinct from each other based on in-depth personal narratives of experiences involving 22 positive emotions (n = 165; 3,592 emotional events). A bottom-up computational analysis was applied to the transcribed text, with unsupervised clustering employed to maximize internal granular consistency (i.e., the clusters being maximally different and maximally internally homogeneous). The analysis yielded four emotions that map onto distinct clusters of subjective experiences: amusement, interest, lust, and tenderness. The application of the semantic space approach to in-depth personal accounts yields a nuanced understanding of positive emotional experiences. Moreover, this analytical method allows for the bottom-up development of emotion taxonomies, showcasing its potential for broader applications in the study of subjective experiences.

Nonverbal vocalisations like laughter, sighs, and groans are a fundamental part of everyday communication. Yet surprisingly little is known about the social norms concerning which vocalisations are considered appropriate to express in which context (i.e., display rules). Here, in two pre-registered studies, we investigate how people evaluate the appropriateness of different nonverbal vocalisations across locations and relationships with listeners. Study 1, with a U.S. sample (n = 250), showed that certain vocalisations (e.g., laughter, sighs, cries) are consistently viewed as more socially acceptable than others (e.g., roars, groans, moans). Additionally, location (private vs. public) and interpersonal closeness (close vs. not close) significantly influenced these perceptions, with private locations and close relationships fostering greater expressive freedom. Study 2 extended this investigation across four societies with divergent cultural norms (n = 1120 in total): the U.S. (for direct replication), Türkiye, China, and the Netherlands. Findings largely replicated those from Study 1 and supported the existence of cross-culturally consistent patterns in display rules for nonverbal vocalisations, though with some variation across cultures. This research expands our understanding of how social norms affect auditory communication, extending beyond the visual modality of facial expressions to encompass the rich world of nonverbal vocalisations.

Nonverbal vocalisations are a fundamental part of human life. This review uses Tinbergen’s ethological framework to examine the functions, ontogenetic trajectories, evolutionary history, and underlying mechanisms of five types of vocalisations: cries, laughter, moans, screams, and sighs. By integrating insights from evolutionary biology and social psychology, we demonstrate how biological functions and social factors shape these vocalisations. The review discusses the relationship between acoustic properties and functions, highlighting the diverse roles vocalisations can play, including emotion regulation, social bonding, threat signalling, and fostering group cohesion. Tracing the development of vocalisations from infancy to adulthood emphasises the role of innate tendencies as well as delineating processes of social learning. We additionally examine how social context and cultural norms influence vocalisations and their interpretation. For each vocalisation type, we map their distinct nature and communicative potential: cries are crucial for survival and caregiving responses; laughter fosters social bonding and group cohesion; moans convey a wide range of internal states from pleasure to discomfort; screams serve as urgent alarms in critical situations; and sighs regulate emotions and signal shifts in emotional states. This review emphasises that both biological and social factors must be considered to fully understand nonverbal vocalisations.

This book provides a cutting-edge overview of emotion science from an evolutionary perspective. Part 1 outlines different ways of approaching the study of emotion; Part 2 covers specific emotions from an evolutionary perspective; Part 3 discusses the role of emotions in a variety of life domains; and Part 4 explores the relationship between emotions and psychological disorders. Experts from a number of different disciplines—psychology, biology, anthropology, psychiatry, and more—tackle a variety of “how” (proximate) and “why” (ultimate) questions about the function of emotions in humans and nonhuman animals, how emotions work, and their place in human life. This volume documents the explosion of knowledge in emotion science over the last few decades, outlines important areas of future research, and highlights key questions that have yet to be answered.

Nonverbal vocalisations like laughter, sighs, and groans are a fundamental part of everyday communication. Yet surprisingly little is known about the social norms concerning which vocalisations are considered appropriate to express in which context (i.e., display rules). Here, in two pre-registered studies, we investigate how people evaluate the appropriateness of different nonverbal vocalisations across locations and relationships with listeners. Study 1, with a U.S. sample (n = 250), showed that certain vocalisations (e.g., laughter, sighs, cries) are consistently viewed as more socially acceptable than others (e.g., roars, groans, moans). Additionally, location (private vs. public) and interpersonal closeness (close vs. not close) significantly influenced these perceptions, with private locations and close relationships fostering greater expressive freedom. Study 2 extended this investigation into four cultural contexts (n = 1120 in total): the U.S. (for direct replication), Türkiye, China, and the Netherlands. Findings largely replicated those from Study 1 and supported the existence of cross-culturally consistent patterns in display rules for nonverbal vocalisations, though with some variation across cultures. This research expands our understanding of how social norms affect auditory communication, extending beyond the visual modality of facial expressions to encompass the rich world of nonverbal vocalisations.

Laughing is ubiquitous in human life, yet what causes it and how it sounds is highly variable. Considering this diversity, we sought to test whether there are fundamentally different kinds of laughter. Here, we sampled spontaneous laughs (n = 887) from wide range of everyday situations (for example, comedic performances and playful pranks). Machine learning analyses showed that laughs produced during tickling are acoustically distinct from laughs triggered by other kinds of events (verbal jokes, watching something funny, or witnessing someone else’s misfortune). In a listening experiment (n = 201), participants could accurately identify tickling-induced laughter, validating that such laughter is not only acoustically, but also perceptually distinct. A second listening study (n = 210) combined with acoustic analyses indicate that tickling-induced laughter involves less vocal control than laughter produced in other contexts. Together, our results reveal a unique acoustic and perceptual profile of laughter induced by tickling, an evolutionarily ancient play behaviour, distinguishing it clearly from laughter caused by other triggers. This study showcases the power of machine learning in uncovering patterns within complex behavioural phenomena, providing a window into the evolutionary significance of ticking-induced laughter.