Music selection behaviors in everyday listening

Data on everyday music listening obtained via the Experience Sampling Method indicated that selection method was related to liking for and emotional response to the music, attention paid to the music, and perceived consequences of hearing the music. Individual listener’s characteristics (e.g., age and level of engagement with music) were associated with selection behaviors. Negative effects resulted when individuals perceived they did not have control over music selection. In contrast, possessing control led to positive consequences such as enjoyment and motivation. These results indicate that control is an important aspect of one’s everyday music experiences

Primary and early childhood education teachers education in Higher Education in Brazil : quality in balance between the ideal and the possible, favoured and disadvantaged teachers

Peer reviewedPublisher PD

Collaboration, cognitive effort, and self-reference in United Kingdom top 5 pop music lyrics 1960–2015

This research investigated associations between the lyrics of every song to have reached the weekly U.K. Top 5 singles chart from 1960 to 2015 and the number of people responsible for recording each song. Following computerized content analysis of the lyrics of the 4,534 unique songs, the results showed that the number of musicians involved was negatively related to use of cognitive terms, consistent with previous research on social loafing, and was also negatively related to instances of self-reference and use of language concerning social interaction, arguably in reflection of the inherent constraints on such that arise from collaborating with others

The Role of Location in Everyday Experiences of Music

Mehrabian and Russell’s (1974) Pleasure–Arousal–Dominance model states that a propensity to approach/avoid an environment can be conceptualized in terms of the pleasure and arousal it elicits and one’s degree of dominance therein. Using the Experience Sampling Method, 177 individuals provided responses concerning Mehrabian and Russell’s model throughout 1 wk regarding music experiences that occurred in their daily life (including how the music was heard and how their responses related to the listening location). Results indicate that the time of day and day of week are related to where music is experienced, and that the consequences of what was heard are related to both time and location. Although music was experienced more often in private locations than in public overall, interesting patterns of music experiences that occurred in public locations demonstrate in detail how music listening varies by location. Specifically, portable devices were associated with positive responses, which contrasted sharply with the responses to music broadcasted publicly in public settings. Participants’ ratings of choice, liking, and arousal demonstrated the importance of considering choice as an indication of dominance, such that music usage is consistent with Mehrabian and Russell’s model, and has functions that vary according to the specific characteristics of the situation

The relationship between pop music and lyrics: a computerized content analysis of the United Kingdom’s weekly top five singles, 1999–2013

The majority of research on music aesthetics treats music and lyrics as discrete entities, despite the artistic imperative that they should relate to one another in some way. The present research computer-analyzed both the music and lyrics of the songs to have reached the weekly United Kingdom top 5 singles chart from January 1999-December 2013 (N = 1,414). The findings indicate that the typicality of a given set of lyrics relative to the corpus as a whole was associated with their popularity; that there were numerous associations between each of six mood scores assigned to the music and various aspects of the lyrics (e.g., passionate music was associated with lyrics addressing hardship and less concern with precise numerical terms); and that the relative contribution of the lyrics and music to overall popularity varied according to the means by which these were operationalized so that, for instance, music and lyrics contributed equally to explaining peak chart position, whereas music outperformed lyrics in explaining the number of weeks spent on the top 5. Pop music and its lyrics are related to one another, and the relationship can be explained to some extent via existing concepts in the aesthetics literature

Parenting style as a predictor of music preference

Although previous research has established relationships between perceived parenting styles and children’s deviant behaviours and links between these behaviours and a liking for intense and rebellious music, no research has explored the associations between perceived parenting styles and children’s liking for different music styles. Whereas previous research has considered musical taste by looking at a small number of individual difference variables in isolation from one another, the present research used a cross-sectional correlational design to investigate whether parenting styles, the Big Five personality traits, sensation seeking, age, and gender were associated with a liking for different music styles. In total, 336 Australians completed an online, self-report questionnaire. Analyses demonstrated there were relationships between five of the six parenting style variables and five of the music styles considered. This indicates that various parenting styles were associated with musical taste, and the nature of these associations extends well beyond those concerning rebellious music and neglectful parenting that have been identified by previous research

HABIT: Horse Automated Behaviour Identification Tool: a position paper

HABIT (Horse Automated Behaviour Identification Tool) is an Animal Computer Interaction (ACI) project, on the interdisciplinary boundary between equitation science and computer science. HABIT will automate the analysis and recognition of horse-to-horse and horse-to-human behaviours, as observed in unconstrained / ad-hoc video. A horse-to-horse dyad video dataset will be compiled, illustrating exemplar behaviours. Behavioural signatures will be manually identified from video. Next, a system will be developed and trained to recognise these signatures. The tool will then be evaluated, when applied to both horse-to-horse and horse-to-human video clips. In the study of animal behaviour, an ‘ethogram’ is a set of comprehensive descriptions of the characteristic behaviour patterns of a species. HABIT is potentially the first step towards the ‘automated ethogram’. This project provides a welfare-orientated approach to evaluating horse behaviours. When horses are handled, trained or ridden, HABIT will help ensure that these experiences occur within the natural repertoire of equine behaviours. There is also scope to engage and educate the public about horse behaviours; both for general interest and to raise welfare-awareness. Additionally, automation could play an important methodological role in animal-centred design by reducing human biases during the requirements and evaluation processes

Popular music lyrics and musicians’ gender over time: a computational approach

The present study investigated how the gender distribution of the United Kingdom’s most popular artists has changed over time and the extent to which these changes might relate to popular music lyrics. Using data mining and machine learning techniques, we analyzed all songs that reached the UK weekly top 5 sales charts from 1960 to 2015 (4,222 songs). DICTION software facilitated a computerized analysis of the lyrics, measuring a total of 36 lyrical variables per song. Results showed a significant inequality in gender representation on the charts. However, the presence of female musicians increased significantly over the time span. The most critical inflection points leading to changes in the prevalence of female musicians were in 1968, 1976, and 1984. Linear mixed-effect models showed that the total number of words and the use of self-reference in popular music lyrics changed significantly as a function of musicians’ gender distribution over time, and particularly around the three critical inflection points identified. Irrespective of gender, there was a significant trend toward increasing repetition in the lyrics over time. Results are discussed in terms of the potential advantages of using machine learning techniques to study naturalistic singles sales charts data