Pragmatists views, like those of John Dewey’s about music, emphasize the social nature of musical sensibility, instrumental expressions and adaptation to changing circumstances. Indeed, expectancy and violations of those expectations in music are tied to memory and human development. Cephalic (mind/brain/body) factors that underlie what we expect, and variation on the expected, inhere in musical experiences. Music is a piece of nature and is tied to movement and dance and rooted in social contact. Music of course serves many functions in (...) our lives. From the first sounds to some of the last, human well-being and music are ripe across the life cycle of human experience. (shrink)

The ongoing generation of expectations is fundamental to listeners’ experience of music, but research into types of statistical information that listeners extract from musical melodies has tended to emphasize transition probabilities and n‐grams, with limited consideration given to other types of statistical learning that may be relevant. Temporal associations between scale degrees represent a different type of information present in musical melodies that can be learned from musical corpora using expectation networks, a computationally simple method based on activation and decay. (...) Expectation networks infer the expectation of encountering one scale degree followed in the near (but not necessarily immediate) future by another given scale degree, with previous work suggesting that scale degree associations learned by expectation networks better predict listener ratings of pitch similarity than transition probabilities. The current work outlines how these learned scale degree associations can be combined to predict melodic continuations and tests the resulting predictions on a dataset of listener responses to a musical cloze task previously used to compare two other models of melodic expectation, a variable‐order Markov model (IDyOM) and Temperley's music‐theoretically motivated model. Under multinomial logistic regression, all three models explain significant unique variance in human melodic expectations, with coefficient estimates highest for expectation networks. These results suggest that generalized scale degree associations informed by both adjacent and nonadjacent relationships between melodic notes influence listeners’ melodic predictions above and beyond n‐gram context, highlighting the need to consider a broader range of statistical learning processes that may underlie listeners’ expectations for upcoming musical events. (shrink)

Bird songs often display musical acoustic features such as tonal pitch selection, rhythmicity, and melodic contouring. We investigated higher-order musical temporal structure in bird song using an experimental method called “music scrambling” with human subjects. Recorded songs from a phylogenetically diverse group of 20 avian taxa were split into constituent elements and recombined in original and random order. Human subjects were asked to evaluate which version sounded more “musical” on a per-species basis. Species identity and stimulus treatment were concealed from (...) subjects, and stimulus presentation order was randomized within and between taxa. Two recordings of human music were included as a control for attentiveness. Participants varied in their assessments of individual species musicality, but overall they were significantly more likely to rate bird songs with original temporal sequence as more musical than those with randomized temporal sequence. We discuss alternative hypotheses for the origins of avian musicality, including honest signaling, perceptual bias, and arbitrary aesthetic coevolution. (shrink)