The role of formal structure in liking for popular music

The effect of restructuring the form of three unfamiliar pop/rock songs was investigated in two experiments. In the first experiment, listeners' judgements of the likely location of sections of novel popular songs were explored by requiring participants to place the eight sections (Intro - Verse 1 - Chorus 1 - Verse 2 - Chorus 2 - Bridge (solo) - Chorus 3 - Extro) of the songs into the locations they thought them most likely to occur within the song. Results revealed that participants were able to place the sections in approximately the right location with some accuracy, though they were unable to differentiate between choruses. In Experiment 2, three versions of each of the songs were presented in three different structures: intact (original form), medium restructured (the sections in a moderately changed order), and highly restructured (more severe restructuring). The results show that listeners' judgments of predictability and liking were largely uninfluenced by the restructuring of the songs, in line with findings for classical music. Moment-by-moment liking judgements of the songs demonstrated a change in liking judgements with repeated exposure, though the trend was downwards with repeated exposure rather than upwards. Detailed analysis of moment-by-moment judgements at the ends and beginnings of sections showed that listeners were able to respond quickly to intact songs, but not to restructured songs. The results suggest that concatenism prevails in listening to popular song at the expense of paying attention to larger structural features. © 2012 by the regents of the university of california all rights reserved.

A Data Driven Tool supported CLD Creation Approach

The creation of Causal Loop Diagrams (CLDs) is a major phase in the System Dynamics (SD) life-cycle, since the created CLDs express dependencies and feedback in the system under study, as well as, guide modellers in building meaningful simulation models. The cre-ation of CLDs is still subject to the modeller's domain expertise (mental model) and her ability to abstract the system, because of the strong de-pendency on semantic knowledge. Since the beginning of SD, available system data sources (written and numerical models) have always been sparsely available, very limited and imperfect and thus of little benefit to the whole modelling process. However, in recent years, we have seen an explosion in generated data, especially in all business related domains that are analysed via Business Dynamics (BD). In this paper, we intro-duce a systematic tool supported CLD creation approach, which analyses and utilises available disparate data sources within the business domain. We demonstrate the application of our methodology on a given business use-case and evaluate the resulting CLD. Finally, we propose directions for future research to further push the automation in the CLD creation and increase confidence in the generated CLDs.