Download [PORTABLE] Music Flow G

Anyone here has an LG Music Flow device? I'm having issues casting to Youtube Music from both my phone and PC to my H7 speaker. Interestingly enough when I use Google Home to cast music it works, and then I can cast directly from the Youtube Music app...

Now think of a time when you were involved in singing or playing an instrument, or simply in listening to music. You will probably remember that time seemed to stop or to accelerate; you were totally concentrated on the music; everything flowed easily and you felt a sense of joy and fulfillment.

Download Music Flow G

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To explain further, flow experience (Csikszentmihalyi, 1975, 1990) is a state of full engagement, control, concentration and action awareness, occurring during an activity perceived as highly self-rewarding and characterized by clear goals, unambiguous feedback, distortion of time perception, loss of self-consciousness and a balance between challenges and skills required to best perform it. These characteristics of flow are also the nine dimensions this experience is composed of (Csikszentmihalyi, 1975).

The analysis of flow in musical contexts is a rapidly-developing field as suggested in Croom (2012, 2015) due to the complexity of both phenomena (i.e., flow and music), which has led scholars to focus on several different aspects of them, such as emotions (Lamont, 2012; Marin and Bhattacharya, 2013); motivation (Csikszentmihalyi and Rich, 1997; Schmidt, 2005; Karageorghis et al., 2008; Digelidis et al., 2014); performance anxiety management (Wilson and Roland, 2002; Kirchner, 2011; Fullagar et al., 2013); social relationships (Custodero, 2002; Bakker, 2005; Bloom and Skutnick-Henley, 2005; Freer and Raines, 2005; Freer, 2009; Hart and Di Blasi, 2015); creativity (Csikszentmihalyi, 1997; Sheridan and Byrne, 2002; MacDonald et al., 2006) and psychophysiological correlates of flow experience (de Manzano et al., 2010). Further, according to the aim of each study, flow was studied in relation to different populations of musicians and non-musicians involved in musical activities (Custodero, 2002, 2005, 2012; Bailey and Davidson, 2005; Bakker, 2005; Biasutti and Frezza, 2009; Freer, 2009; de Manzano et al., 2010; Nijs et al., 2012; Wrigley and Emmerson, 2013).

At a methodological level, the emerging questions regarding autotelic personalities are (i) how to measure the individual proneness to experience flow and (ii) which internal and relatively stable individual characteristics are related to the merging of flow (Keller and Blomann, 2008; Baumann, 2012; Mosing et al., 2012; Marin and Bhattacharya, 2013).

Indeed, according to Jackson and Eklund (2002), Jackson et al. (2008, 2010), Martin and Jackson (2008), and Jackson and Marsh (1996), it is possible to identify four main instruments to assess flow as a state (i.e., Flow State Scale; Flow State Scale-2; Short; and Core State Flow Scales) and four to assess flow as a trait (i.e., Dispositional Flow Scale; Dispositional Flow Scale-2; Short; and Core Dispositional Flow Scales; Swedish Flow Proneness Questionnaire).

Therefore, as each of the two above-mentioned approaches (trait and state) gives a unique contribution to a better understating of flow, the aim of this review is to present the implications of adopting one, the other or both perspectives to investigate flow in three above-mentioned most-investigated music domains, as suggested also by (Marin and Bhattacharya, 2013), namely: (i) composition, (ii) listening and (iii) music performance.

In order to distinguish studies that analyzed flow as a trait, as a state or as both of them, without ambiguity, we referred to literature concerning flow assessment (Jackson and Marsh, 1996; Jackson and Eklund, 2002; Jackson et al., 2008, 2010; Martin and Jackson, 2008), which identified four main instruments to assess flow as a state (i.e., Flow State Scale; Flow State Scale-2; Short; and Core State Flow Scales) and four to assess flow as a trait (i.e., Dispositional Flow Scale; Dispositional Flow Scale-2; Short; and Core Dispositional Flow Scales).

We decided to focus on flow because of the solid theoretical and methodological background supporting this experience (Jackson and Marsh, 1996; Jackson and Eklund, 2002; Jackson et al., 2008, 2010; Martin and Jackson, 2008). Further, we found evidence that flow, peak experiences and peak performances are different phenomena even though they share some aspects (Privette, 1983; Privette and Bundrick, 1991). Because the aim of this review is not clinical, we excluded music therapy studies.

There also exist two long and multidimensional scales to assess flow in dispositional terms, namely, the Dispositional Flow Scale (Jackson et al., 1998) and the Dispositional Flow Scale-2 (Jackson and Eklund, 2002). Both scales measure the individual proneness to experience of flow. In particular, they assess the frequency of flow experience dimensions in the life of an individual.

The interest for state and trait components of flow in musical contexts seems to be growing as of late. In fact, it is possible to find several studies that aimed to validate dispositional and state flow instruments in musical contexts (Martin and Jackson, 2008; Sinnamon et al., 2012; Wrigley and Emmerson, 2013).

Martin and Jackson had explored this possibility, validating a brief measure of dispositional flow. While the Core Flow State Scale was never used to study the relationship between flow and music, Jackson et al. (2008) tested the Short Flow Scale (9-item scale on a 7-point answer scale) specifically in a live musical context and in relation to motivation and engagement. They considered a heterogeneous target [violin (20% of respondents), piano (19%), clarinet (9%), flute (8%), cello (6%), voice (6%), trumpet (5%), and 5% other instruments]. They found a confirmation for the nine-item model in musical contexts also from a dispositional perspective (Chi-square: 45.11; df = 27).

Following these premises, a total of 10 studies were included and fully reviewed. To be consistent with the main structure of the review, and in order to give a critical overview of the selected studies, we chose to discuss first studies regarding the domain of musical composition, the musical domain of listening, and finally, regarding musical performance.

Considering the first domain which we analyzed (i.e., musical composition), it seemed that one of the most fascinating issues, which emerged first in this review, was the embodied nature of both music and flow. This issue was explicitly addressed by Nijs et al. (2012) who investigated the relationship occurring among these two phenomena (i.e., flow and music), presence and new technologies. Musicians had the opportunity to use their own bodies to interact with a machine that helped them create their personal music. Nijs et al. (2012) underlined the role of the action-perception coupling principle, and therefore the role of the body, at the base of the relationship between flow and presence.

However, the issue of the embodied nature of music-flow system needs to be more deepened by future researches in this field which should consider also the peculiarities of the specific musical settings in which this relationship takes place.

For example, Baker and MacDonald clearly evidenced some features which characterized musical composition field. First, in their study it emerged that flow seemed not to be a matter of age. Indeed, no significant differences emerged in experiencing flow (as a state and as a trait) between students and retirees during tasks of musical composition. Therefore, it is necessary for future researches to investigate if this feature characterizes also the other two domains which we considered (i.e., listening and musical performance).

Besides this, flow proneness was able to predict the sensation of having done something meaningful and was more closely related to composition rather than performance (Jackson and Eklund, 2004; Baker and MacDonald, 2013).

Further, studies regarding the third domain of musical performance suggested that personal emotive intelligence emerged as influencing the strength of the flow experiences we live and as affecting our performance (Marin and Bhattacharya, 2013). Fritz and Avsec (2007) analyzed the relationship between emotions and flow proneness. They evidenced that a person (i) who is prone to have clear in mind what he/she was doing and (ii) who usually felt competent regarding the task could more likely experience positive emotions. On the other hand, paying more attention to a specific task seemed to hinder the emergence of positive emotions.

The predominance of emotive instead of cognitive components of flow seemed to be the key for our personal well-being but not for the reaching of an optimal performance (Bloom and Skutnick-Henley, 2005), in which it was the balance between these two dimensions that played a fundamental role (de Manzano et al., 2010).

Therefore, while listening to music was found to be closely related to flow (Pates et al., 2003), the link between performance and flow was less clear and needs to be further investigated, and dispositional components of this optimal experience seemed the key to better address this issue (Martin and Jackson, 2008; Sinnamon et al., 2012; Marin and Bhattacharya, 2013; Wrigley and Emmerson, 2013).

To address the second above-mentioned issue concretely (i.e., adequate managing of emotive components of flow), we referred to literature concerning anxiety and musical performance (Wilson and Roland, 2002; Ryan, 2004; Kirchner, 2005, 2011; Fullagar et al., 2013). As Lamont (2012) clearly suggested, focusing on negative emotive components occurring during music performance, such as anxiety, biofeedback training and the accurate selection of the excerpts of music to be played, could be appropriate techniques to successfully face performance anxiety.

Since affects seemed crucial in order to investigate both dispositional and state flow in musical contexts, a further integration in this direction could be considering also music-induced emotions and not only emotive components of flow, giving their relevance in both phenomena (i.e., music and flow). For example, Juslin (2013, p. 240) developed a model regarding music-evoked emotions in which the psychophysiological dimension was also considered. In a broad perspective, Goffin (2014) showed that music evokes bodily feelings that can be clustered into specific moods and can influence the esthetic appreciation of music. e24fc04721