Abstract
In the previous chapter, I discussed the problem of music recommendation as a sequential decision-making problem, as people’s preferences and expectations are informed by what has been played up to any given point in time. However, there are other ways in which people’s preferences and expectations are influenced by music. A relevant question in the context of studying interactive processes between people and automated systems is how background information, and music in particular, impact the way people make decisions. This chapter focuses on this question in two distinct contexts which engage different decision-making processes.
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Notes
- 1.
In this chapter and elsewhere in this book, p-values for correlation are results obtained by analysis of the distribution of correlation values given the null hypothesis.
- 2.
An example for information which may carry emotional content is words, a fact we have leveraged in the previous experiment.
- 3.
The expected score for a batch is simply the sum of expected wins (or losses) over trials. For instance, for an individual trial, a betting proportion of 8 : 5 has the expected score of \(0.5\cdot 8 - 0.5\cdot 5 = 1.5\). Aggregated over trials, this value provides a baseline for how a person would perform by betting randomly.
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Liebman, E. (2020). Modeling the Impact of Music on Human Decision-Making. In: Sequential Decision-Making in Musical Intelligence. Studies in Computational Intelligence, vol 857. Springer, Cham. https://doi.org/10.1007/978-3-030-30519-2_5
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