M usicians are typically identified in research papers by some single item measure (SIM) that focuses on just one component of musicality, such as expertise. Recently, musical sophistication has emerged as a more comprehensive approach by incorporating various components using multiple question items. However, the practice of SIM continues. The aim of this paper was to investigate which SIM in musical sophistication indexes best estimates musical sophistication. The Ollen Musical Sophistication Index (OMSI) and the Goldsmiths Musical Sophistication Index (Gold-MSI) were analyzed. The OMSI musician rank item (“ Which title best describes you? ”) was observed to be the best SIM for predicting OMSI and Gold-MSI scores. Analysis of the OMSI item indicated three parsimonious musical identity categories (MIC); namely, no musical identity (NMI), musical identity (MI), and strong musical identity (SMI). Further analyses of MIC against common SIMs used in literature showed characteristic profiles. For example, MIC membership according to years of private lessons are: NMI is < 6 years; MI is 6–10 years; and SMI is > 10 years. The finding of the study is that the SIM of musician rank should be used because of its face validity, correlation with musical sophistication, and plausible demarcation into the three MIC levels.

This paper presents a developing Pitch Error Coding Protocol for assessing the accuracy of a musician’s performance. The protocol organized performance errors into any of three main categories, each based on an established cognitive theory of music memory and processing: (1) the Serial Distance Hypothesis (SDH); (2) the Implication Realization (I-R) model; and (3) the mental organizing principle of the Schema Theory (SCH). An elite oboist formed the basis of a detailed case study where his sight reading, practice, and performance of a challenging excerpt of music were examined. These data were used to: 1) investigate the protocol; 2) ask whether any protocol components could explain errors better than others; 3) and show where improvements after practice occurred. The results revealed that the SDH accounted for the majority of pitch errors, these originated in a three-note proximity of the target and some SDH errors overlapped with the I-R category errors. Although final counts for SDH and I-R are similar, SDH uniquely identified errors more frequently than the I-R. Future research and development of the protocol might look at combinations of pieces and performer to determine whether SDH may be a dominant source of error.

Depression is often associated with a reduced motivation to engage in behavior that will improve one’s mood. This paper presents a study in which 175 university students listened to a self-selected piece of music on Youtube that made them sad. Post- and pre-listening scores of depressed mood on the Profile of Mood States (POMS) were taken, as were measures of rumination and scores on the Like Sad Music Scale (LSMS). Results indicate that listening to sad music via this medium can significantly increase feelings of depression in people with a tendency to depression (as suggested by high rumination scores). Furthermore, people with a tendency to depression demonstrate a liking for such music despite the potentially unhealthy consequences of listening to it.

Why do People Seek out Music that Makes Them cry? This paradox is a complex one that appears to have no single answer. Rather, numerous factors appear to be interacting in the diverse responses of individuals to music. The present study tested the hypothesis that individual differences in dissociation, absorption, fantasy proneness, empathy, and rumination would be related to the enjoyment of negative emotion in music. Fifty-nine participants completed a survey pertaining to this question. Results revealed statistically significant positive relationships between enjoyment of evoked negative emotion in response to music with both absorption and the recently reported construct of 'music empathy,' Factor analysis and a regression model confirmed these results, and the approach suggests that further study of individual differences will continue to provide new insights into some of the subtleties of the enjoyment of negative emotions in music.

WE STUDIED THE EMOTIONAL RESPONSES BY MUSICIANS to familiar classical music excerpts both when the music was sounded, and when it was imagined.We used continuous response methodology to record response profiles for the dimensions of valence and arousal simultaneously and then on the single dimension of emotionality . The response profiles were compared using cross-correlation analysis, and an analysis of responses to musical feature turning points, which isolate instances of change in musical features thought to influence valence and arousal responses. We found strong similarity between the use of an emotionality arousal scale across the stimuli, regardless of condition (imagined or sounded). A majority of participants were able to create emotional response profiles while imagining the music, which were similar in timing to the response profiles created while listening to the sounded music.We conclude that similar mechanisms may be involved in the processing of emotion in music when the music is sounded and when imagined.

TIME-KEEPING AMONG DANCERS WAS INVESTIGATED by measuring a dancer's movement in the presence and absence of music. If an internal clock was at work, then change from the ideal would manifest as scaling---consistently faster or slower unaccompanied performance; if time differences were due to lapsing, then sections from the with-music condition would be deleted, or material would be inserted into the no-music condition. Motion was recorded during ensemble performances of a four-minute choreographed piece with and without music. The median of 24 markers in the height dimension was analyzed for scaling and lapsing. Twenty percent of the variance was accounted for by sporadic scaling. Lapses---insertions and deletions---accounted for nearly all the speeding up---10.45 of 14 s. As in musical performance of memorized material, lapsing rather than scaling accounted for timing variations. Automation of lapsing and scaling detection has application in the analysis of music and dance time series data.

BARON-COHEN'S EMPATHIZER-SYSTEMIZER-THEORY (E-S theory, Baron-Cohen, Knickmeyer, & Belmonte, 2005) distinguishes two general cognitive styles. Empathizing is characterized as the capacity to respond to feeling states of other individuals, whereas systemizing is characterized as the capacity to respond to regularities of objects and events. To investigate these traits within the music domain, a questionnaire study ( N = 442) was conducted. Construct validity and reliability of the measurement instrument were assessed by factor analysis procedures. A simplified unit weighting (SUW) scale was used to determine individual differences in music empathizing (ME) and music systemizing (MS). Significant effects of sex and of music performance experience were observed. A highly similar pattern of results emerged from a replicating survey ( N = 155) using a short-version of the questionnaire. These results suggest that the ME and MS traits corroborate and extend the general E-S theory.

The relationship between musical features and perceived emotion was investigated by using continuous response methodology and time-series analysis. Sixty-seven participants responded to four pieces of Romantic music expressing different emotions. Responses were sampled once per second on a two-dimensional emotion space (happy-sad valence and aroused-sleepy). Musical feature variables of loudness, tempo, melodic contour, texture, and spectral centroid (related to perceived timbral sharpness) were coded. Musical feature variables were differenced and used as predictors in two univariate linear regression models of valence and arousal for each of the four pieces. Further adjustments were made to the models to correct for serial correlation. The models explained from 33% to 73% of variation in univariate perceived emotion. Changes in loudness and tempo were associated positively with changes in arousal, but loudness was dominant. Melodic contour varied positively with valence, though this finding was not conclusive. Texture and spectral centroid did not produce consistent predictions. This methodology facilitates a more ecologically valid investigation of emotion in music and, importantly in the present study, enabled the approximate identification of the lag between musical features and perceived emotion. Responses were made 1 to 3 s after a change in the causal musical event, with sudden changes in loudness producing response lags from zero (nearly instantaneous) to 1 s. Other findings, interactions, and ramifications of the methodology are also discussed.