Influence of a continuous affect ratings task on listening time for unfamiliar art music

Taylor, John R.; Dean, Roger T.

doi:10.1080/09298215.2020.1867588

Cited by 6 publications

(8 citation statements)

References 53 publications

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“…These results also support the idea that continuous affect responses are not in themselves necessarily good indicators of discrete retrospective liking responses, as indicated in our recommender work, or of discrete retrospective emotion judgments. On the other hand, causing a participant to engage in continuous affect responses does seem to encourage greater listening time, and hence enhanced familiarity, which clearly predicts and enhances liking progressively (Taylor & Dean, 2021). In the present experiment, pieces were delivered in a sequence that differed from participant to participant, so it is not surprising that features such as trial number (position in the sequence of presentations), or retrospective familiarity and liking ratings (which generally increase across the experiment) are absent from the models discussed here which focus on the whole dataset of pieces or individual responses.…”

Section: Discussionmentioning

confidence: 99%

Continuous affect responses to a large diverse set of unfamiliar music: Bayesian time-series and cluster analyses.

Dean¹,

Taylor²

2023

Psychomusicology: Music, Mind, and Brain

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Sixty-nine participants made continuous response judgments of perceived arousal and valence while listening to 30-s extracts of 100 unfamiliar pieces within a novel recommender system. Our purpose was to take advantage of the relatively large number of participants and pieces studied (compared with prior work on time series analysis [TSA] of continuous affective responses to music) to test the generality of the deductions made from prior evidence in smaller studies. Accordingly, we expected highly autoregressive responses, and hypothesized that acoustic intensity fluctuation would be a major (population-wide, fixed-effect) predictor of arousal and to a lesser degree valence responses in multivariate models, operating in conjunction with broad features of the spectral profile of the music (such as spectral flatness and centroid). We expected considerable interparticipant variability and were interested in the degree to which there was also interpiece variability (both being features of group-specific, random effects). The basic expectations were supported, and we also demonstrated, partly by means of a novel use of cluster analysis, that the observed group variabilities in response features could not be readily attributed to indices of a range of participant-preference and piece-feature characteristics, determined prior to the experiments. A Bayesian analysis was developed, possibly for the first time in the TSA of continuous affect responses to music, since it provides a more comprehensive description of the parameters of the analysis (not only their best estimate point values) and their effects, and a comprehensive range of tests for meaningful effects.

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Section: Discussionmentioning

confidence: 99%

Continuous affect responses to a large diverse set of unfamiliar music: Bayesian time-series and cluster analyses.

Dean¹,

Taylor²

2023

Psychomusicology: Music, Mind, and Brain

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“…With respect to this latter understanding of the term, music engagement has been defined as being ‘actively immersed in the experience of listening to music, to the exclusion of extra-musical stimuli’ [18,19] while another operationalization that has found significant resonance [14,17,20,21] describes it as being ‘compelled, drawn in, connected to what is happening in the music, interested in what will happen next’ [22]. Finally, in yet other conceptualizations, music engagement, along with other forms of engagement (e.g.…”

Section: Operationalizing Time-varying Music Engagementmentioning

confidence: 99%

“…Worth noting is that, across several accounts, time-varying music engagement is described as multidimensional, multifaceted and strongly dependent on several factors including the individual, the musical style and culturally determined referential frameworks [18,20,22,26]. However, perhaps the most striking commonality across current operationalizations is the idea that time-varying music engagement (music engagement, henceforth) involves not just heightened attention but also heightened affect; the latter particularly in the form of curiosity and interest.…”

Section: Operationalizing Time-varying Music Engagementmentioning

confidence: 99%

A model of time-varying music engagement

Omigie,

Mencke

2023

Phil. Trans. R. Soc. B

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The current paper offers a model of time-varying music engagement, defined as changes in curiosity, attention and positive valence, as music unfolds over time. First, we present research (including new data) showing that listeners tend to allocate attention to music in a manner that is guided by both features of the music and listeners' individual differences. Next, we review relevant predictive processing literature before using this body of work to inform our model. In brief, we propose that music engagement, over the course of an extended listening episode, may constitute several cycles of curiosity, attention and positive valence that are interspersed with moments of mind-wandering. Further, we suggest that refocusing on music after an episode of mind-wandering can be due to triggers in the music or, conversely, mental action that occurs when the listener realizes they are mind-wandering. Finally, we argue that factors that modulate both overall levels of music engagement and how it changes over time include music complexity, listener background and the listening context. Our paper highlights how music can be used to provide insights into the temporal dynamics of attention and into how curiosity might emerge in everyday contexts. This article is part of the theme issue ‘Art, aesthetics and predictive processing: theoretical and empirical perspectives’.

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“…The first category refers to studies manipulating the presence of identifying framing elements, such as supplying listeners with the title of the work or the name of the artist, composer, or performer (e.g., Anglada-Tort et al, 2019; Damon, 1933; Taylor & Dean, 2021). While identifying information does not always strictly contain historical details (e.g., when only a title is supplied) it can help the listener understand the work, so we still regard this as an approach to framing.…”

Section: Historical and Valenced Framingmentioning

confidence: 99%

“…Similarly, Kiernan et al (2021) examined the impact of valenced historical biographies for the musical works of Zelenka, although the target variable was self-reported emotional responses rather than preference; they observed increased negative emotions accompanying the negatively valenced biography. In an alternative approach to examining valence and music preference, Taylor and Dean (2021) asked participants to rate their own perceptions of valence for each stimulus they listened to.…”

Section: Historical and Valenced Framingmentioning

confidence: 99%

Increasing music preference through guided self-framing: A comparison of historical and imaginative approaches.

Chmiel¹,

Milne²,

Dean³

et al. 2022

Psychology of Aesthetics, Creativity, and the Arts

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While accompanying or contextualizing information ("framing") is often included alongside music, prior research on the impact of framing on music preference has produced heterogeneous results. Most of these studies have examined historical framing, although a small subset has suggested that imaginative framing may have an understudied potential to increase preference. In such studies the participants are encouraged to freely use their imagination while listening. The present work directly compared these 2 approaches to framing to examine which has greater positive impact on preference. One hundred and fifty-two participants were exposed to 5 varied music excerpts (Pop/Rock and Classical), with participants placed into 1 of 3 conditions. Those in the historical condition were supplied with initial historical framing and were encouraged to freely search online for information related to the piece while listening to it (hence guided self-framing), whereas those in the imaginative condition were encouraged to freely use their imagination while listening. Those in a third, "unrelated" control condition were encouraged to choose an online game (selected from a provided list) while listening. Bayesian modeling was used to examine preference ratings by piece and condition. Historical framing led to higher preference than the control for 4 of 5 pieces, while imaginative framing led to higher preference than the control for 2 pieces. Additionally, those receiving historical framing rated preference higher for pieces that had a greater amount of information readily available online. Thus, historical framing most benefited preference, although we discuss limitations and future directions for research on guided self-framing.

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Influence of a continuous affect ratings task on listening time for unfamiliar art music

Cited by 6 publications

References 53 publications

Continuous affect responses to a large diverse set of unfamiliar music: Bayesian time-series and cluster analyses.

Continuous affect responses to a large diverse set of unfamiliar music: Bayesian time-series and cluster analyses.

A model of time-varying music engagement

Increasing music preference through guided self-framing: A comparison of historical and imaginative approaches.

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