Many authors have speculated about a close relationship between vocal expression of emotions and musical expression of emotions. but evidence bearing on this relationship has unfortunately been lacking. This review of 104 studies of vocal expression and 41 studies of music performance reveals similarities between the 2 channels concerning (a) the accuracy with which discrete emotions were communicated to listeners and (b) the emotion-specific patterns of acoustic cues used to communicate each emotion. The patterns are generally consistent with K. R. Scherer's (1986) theoretical predictions. The results can explain why music is perceived as expressive of emotion, and they are consistent with an evolutionary perspective on vocal expression of emotions. Discussion focuses on theoretical accounts and directions for future research.

/pdf/communication-of-emotions-in-vocal-expression-and-music-2to7fzkqnc.pdf

Communication of emotions in vocal expression and music performance: different channels, same code?

Research indicates that people value music primarily because of the emotions it evokes. Yet, the notion of musical emotions remains controversial, and researchers have so far been unable to offer a satisfactory account of such emotions. We argue that the study of musical emotions has suffered from a neglect of underlying mechanisms. Specifically, researchers have studied musical emotions without regard to how they were evoked, or have assumed that the emotions must be based on the "default" mechanism for emotion induction, a cognitive appraisal. Here, we present a novel theoretical framework featuring six additional mechanisms through which music listening may induce emotions: (1) brain stem reflexes, (2) evaluative conditioning, (3) emotional contagion, (4) visual imagery, (5) episodic memory, and (6) musical expectancy. We propose that these mechanisms differ regarding such characteristics as their information focus, ontogenetic development, key brain regions, cultural impact, induction speed, degree of volitional influence, modularity, and dependence on musical structure. By synthesizing theory and findings from different domains, we are able to provide the first set of hypotheses that can help researchers to distinguish among the mechanisms. We show that failure to control for the underlying mechanism may lead to inconsistent or non-interpretable findings. Thus, we argue that the new framework may guide future research and help to resolve previous disagreements in the field. We conclude that music evokes emotions through mechanisms that are not unique to music, and that the study of musical emotions could benefit the emotion field as a whole by providing novel paradigms for emotion induction.

/pdf/emotional-responses-to-music-the-need-to-consider-underlying-592kszluvl.pdf

Emotional responses to music: The need to consider underlying mechanisms

The RAVDESS is a validated multimodal database of emotional speech and song. The database is gender balanced consisting of 24 professional actors, vocalizing lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity, with an additional neutral expression. All conditions are available in face-and-voice, face-only, and voice-only formats. The set of 7356 recordings were each rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity and test-retest intrarater reliability were reported. Corrected accuracy and composite "goodness" measures are presented to assist researchers in the selection of stimuli. All recordings are made freely available under a Creative Commons license and can be downloaded at https://doi.org/10.5281/zenodo.1188976.

/pdf/the-ryerson-audio-visual-database-of-emotional-speech-and-jx71tsx3ts.pdf

The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English

One reason for the universal appeal of music lies in the emotional rewards that music offers to its listeners. But what makes these rewards so special? The authors addressed this question by progressively characterizing music-induced emotions in 4 interrelated studies. Studies 1 and 2 (n 354) were conducted to compile a list of music-relevant emotion terms and to study the frequency of both felt and perceived emotions across 5 groups of listeners with distinct music preferences. Emotional responses varied greatly according to musical genre and type of response (felt vs. perceived). Study 3 (n 801)—a field study carried out during a music festival—examined the structure of music-induced emotions via confirmatory factor analysis of emotion ratings, resulting in a 9-factorial model of music-induced emotions. Study 4 (n 238) replicated this model and found that it accounted for music-elicited emotions better than the basic emotion and dimensional emotion models. A domain-specific device to measure musically induced emotions is introduced—the Geneva Emotional Music Scale.

/pdf/emotions-evoked-by-the-sound-of-music-characterization-3ejdq33vvl.pdf

Emotions evoked by the sound of music: Characterization, classification, and measurement.

Contents Historical, Contemporary, and Future Perspectives on Self-Regulated Learning and Performance Dale H. Schunk and Jeffrey A. Greene Section I. Basic Domains of Self-Regulation of Learning and Performance Social Cognitive Theoretical Perspective of Self-Regulation Ellen L. Usher and Dale H. Schunk Cognition and Metacognition Within Self-Regulated Learning Philip H. Winne Developmental Trajectories of Skills and Abilities Relevant for Self-Regulation of Learning and Performance Rick H. Hoyle and Amy L. Dent Motivation and Affect in Self-Regulated Learning: Does Metacognition Play a Role? Anastasia Efklides, Bennett L. Schwartz, and Victoria Brown Self-Regulation, Co-Regulation and Shared Regulation in Collaborative Learning Environments Allyson Hadwin, Sanna Jarvela, and Mariel Miller Section II. Self-Regulation of Learning and Performance in Context Metacognitive Pedagogies in Mathematics Classrooms: From Kindergarten to College and Beyond Zemira R. Mevarech, Lieven Verschaffel, and Erik De Corte Self-Regulated Learning in Reading Keith W. Thiede and Anique B. H. de Bruin Self-Regulation and Writing Steve Graham, Karen R. Harris, Charles MacArthur, and Tanya Santangelo The Self-Regulation of Learning and Conceptual Change in Science: Research, Theory, and Educational Applications Gale M. Sinatra and Gita Taasoobshirazi Using Technology-Rich Environments to Foster Self-Regulated Learning in the Social Studies Eric G. Poitras and Susanne P. Lajoie Self-Regulated Learning in Music Practice and Performance Gary E. McPherson, Peter Miksza, and Paul Evans Self-Regulation in Athletes: A Social Cognitive Perspective Anastasia Kitsantas, Maria Kavussanu, Deborah B. Corbatto, and Pepijn K. C. van de Pol Self-Regulation: An Integral Part of Standards-Based Education Marie C. White and Maria K. DiBenedetto Teachers as Agents in Promoting Students' SRL and Performance: Applications for Teachers' Dual-Role Training Program Bracha Kramarski Section III. Technology and Self-Regulation of Learning and Performance Emerging Classroom Technology: Using Self-Regulation Principles as a Guide for Effective Implementation Daniel C. Moos Understanding and Reasoning About Real-Time Cognitive, Affective, and Metacognitive Processes to Foster Self-Regulation With Advanced Learning Technologies Roger Azevedo, Michelle Taub, and Nicholas V. Mudrick The Role of Self-Regulated Learning in Digital Games John L. Nietfeld Self-Regulation of Learning and Performance in Computer-Supported Collaborative Learning Environments Peter Reimann and Maria Bannert Section IV. Methodology and Assessment of Self-Regulation of Learning and Performance Validity and the Use of Self-Report Questionnaires to Assess Self-Regulated Learning Christopher A. Wolters and Sungjun Won Capturing and Modeling Self-Regulated Learning Using Think-Aloud Protocols Jeffrey A. Greene, Victor M. Deekens, Dana Z. Copeland, and Seung Yu Assessing Self-Regulated Learning Using Microanalytic Methods Timothy J. Cleary and Gregory L. Callan Advancing Research and Practice About Self-Regulated Learning: The Promise of In-Depth Case Study Methodologies Deborah L. Butler and Sylvie C. Cartier Examining the Cyclical, Loosely Sequenced, and Contingent Features of Self-Regulated Learning: Trace Data and Their Analysis Matthew L. Bernacki Data Mining Methods for Assessing Self-Regulated Learning Gautam Biswas, Ryan S. Baker, and Luc Paquette Section V. Individual and Group Differences in Self-Regulation of Learning and Performance 26. Calibration of Performance and Academic Delay of Gratification: Individual and Group Differences in Self-Regulation of Learning Peggy P. Chen and Hefer Bembenutty 27. Academic Help Seeking as a Self-Regulated Learning Strategy: Current Issues, Future Directions Stuart A. Karabenick and Eleftheria N. Gonida 28. The Three Faces of Epistemic Thinking in Self-Regulated Learning Krista R. Muis and Cara Singh 29. Advances in Understanding Young Children's Self-Regulation of Learning Nancy E. Perry, Lynda R. Hutchinson, Nikki Yee, and Elina Maatta 30. Self-Regulation: Implications for Individuals With Special Needs Linda H. Mason and Robert Reid 31. Culture and Self-Regulation in Educational Contexts Dennis M. McInerney and Ronnel B. King

Handbook of Self-Regulation of Learning and Performance

Nine professional musicians were instructed to perform short melodies using various instruments - the violin, electric guitar, flute, and singing voice - so as to communicate specific emotional characters to listeners. The performances were first validated by having listeners rating the emotional expression and then analysed with regard to their physical characteristics, e.g. tempo, dynamics, timing, and spectrum. The main findings were that (a) the performer's expressive intention had a marked effect on all analysed variables; (b) the performers showed many similarities as well as individual differences in emotion encoding; (c) listeners were generally successful in decoding the intended expression; and (d) some emotional characters seemed easier to communicate than others. The reported results imply that we are unlikely to find performance rules independent of instrument, musical style, performer, or listener.

Emotional Expression in Music Performance: Between the Performer's Intention and the Listener's Experience:

The influence of musical structure on emotional expression.

A distinction is made between emotion perception, that is, to perceive emotional expression in music without necessarily being affected oneself, and emotion induction, that is, listeners’ emotional...

Emotion perceived and emotion felt: Same or different?

Publisher Summary Music performance is a large subject that can be approached in many different ways. This chapter focuses on empirical research of music performance and related matters. Most of this research is concerned with Western tonal music and mainly art music. Excellence in music performance involves two major components like a genuine understanding of what the music is about, its structure and meaning, and a complete mastery of the instrumental technique. Evaluation of performance included many studies which are reviewed earlier. Evaluation occurs in the everyday activity of music critics, music teachers, and musicians. An overall evaluation is considered as a weighted function of the evaluations in the specific aspects. In order to maintain the tempo and to achieve perceived synchrony, musicians should therefore play a small amount ahead of the beat they hear. With sharp attacks the delay is less, and instruments with sharp attacks may therefore serve as “beat-definers” for the rest of an ensemble. In addition, some attempts are made to predict evaluation of music performances from the physical characteristics of the performances.

Alf Gabrielsson

Papers

Emotional Expression in Music Performance: Between the Performer's Intention and the Listener's Experience:

The influence of musical structure on emotional expression.

Emotion perceived and emotion felt: Same or different?

The Performance of Music

Emotions in strong experiences with music.