Perception of time as phase: Toward an adaptive-oscillator model of rhythmic pattern processing

Many human behaviors reflect the attunement of our perceptual systems to rhythmic patterns of stimulation. Examples include dancing to music, speech communication, and the performance of a symphony orchestra. However, developing a computational model of rhythm perception has proven to be difficult for two main reasons. First, rhythm is holistic, yet rhythmic patterns evolve over time. Second, periodicities in rhythmic patterns typically exhibit variability in their timing. Many previous approaches to rhythm perception have ignored these two problems by abstracting time to the level of musical notation, and thus failed to address the fundamental issue of the perception of time. The approach taken in this thesis is that the development of a model of rhythm perception must first address the perception of the time intervals which comprise rhythmic patterns.;I propose a class of adaptive-oscillator processing units which track periodicities in rhythmic patterns (beats). Modest random variations in the timing of rhythmic patterns do not reduce the adaptive oscillator's ability to attain synchrony, and can even improve it. An Entrainment Model of human time perception is then developed. The model is evaluated by comparing its performance on a series of tempo-discrimination simulations to data from analogous human listening experiments, investigating several rhythmic factors that influence listeners' ability to detect differences in the tempo of isochronous auditory sequences. Data obtained from the simulations agreed with the human data, providing support for the model. As an additional evaluation, two tempo-discrimination experiments were conducted to test model predictions regarding the perception of time as phase. The results of these two experiments also agreed with the model. Compared with other psychological models of time perception, the adaptive-oscillator-based Entrainment Model is the only model to provide a unified explanation for these tempo data. This thesis supports the adaptive-oscillator mechanism as a viable approach to modeling rhythm perception, addressing the holistic nature of rhythm, the problem of timing variability, and the perception of time. Furthermore, this thesis demonstrates how direct coupling of a computational system with the temporal structure of its environment is a potentially useful method for learning to interact with that environment.