Abstract
We present a novel hypothetical account of entrainment in music and language, in context of the Information Dynamics of Thinking model, IDyOT. The extended model affords an alternative view of entrainment, and its companion term, pulse, from earlier accounts. The model is based on hierarchical, statistical prediction, modeling expectations of both what an event will be and when it will happen. As such, it constitutes a kind of predictive coding, with a particular novel hypothetical implementation. Here, we focus on the model's mechanism for predicting when a perceptual event will happen, given an existing sequence of past events, which may be musical or linguistic. We propose a range of tests to validate or falsify the model, at various different levels of abstraction, and argue that computational modeling in general, and this model in particular, can offer a means of providing limited but useful evidence for evolutionary hypotheses.
Highlights
We propose a hypothetical anticipatory model of the perception and cognition of events in time
We propose that the last of these options is the case: a bottomup hierarchical perceptual construction of temporal sequence accounts for rhythm and meter in music and language
The strength of predictions is determined by memorized hierarchical information, leading to the multiple different strengths of expectation required to explain the experienced complexity of rhythm in both music and language, from simple pulse up to the extreme rhythmic complexity found in Arabic, Indian and African musics, and the complexity of rhythm in language from everyday argot to the most carefully performed poetry or rap
Summary
We propose a hypothetical anticipatory model of the perception and cognition of events in time. The approach enables precise specification of metrical structures, hypothesized as patterns of entrainment that guide attention in musical listening (London, 2012). This perspective can be understood as a top-down specification of a theoretical notion of meter. Our second perspective is bottom-up: a mechanism that we hypothesize is capable of learning such a hierarchical representation of metrical time from exposure to the statistical regularity inherent in music and everyday perceptual experience. We propose that the combination of coinciding expectations at different levels of granularity are responsible for the percept of meter, explaining the effect modeled by London’s (2012) additive cycle approach to metrical strength
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