A computational model of meter cognition during the audition of functional tonal music: Modeling a-priori bias in meter cognition

We describe a series of experiments using sequential neural networks to model the effect of contextual bias in music cognition. The model quantifies the strength and specificity of a virtual listener’s expectations while listening to functional tonal harmonic chord sequences. The network integrates pools of duple and triple metric units with pitch class representations of chords. The ’listener ’ is then exposed to new chord sequences. We interpret the output of each sequential vector as the expectation for the next event. By representing segregated duple and triple metric beat units we visualize the process of metric cognition, and the mutual reliance of metric and functional harmonic expectations in establishing a percept of meter and a context for expecting consequential harmonic activity. 1. Background Recent studies in cognition address the interdependence and mutual influence of multiple schemas in creating contexts. Schema based studies and models of music cognition [Leman, 1998] have applied Gestalt approaches to many aspects of the musical experience. However, few of these studies consider premonitory conditioning in schema selection. In this paper we show that a-priori contextual bias can strongly influence a sequential neural network model of music cognition and propose that this influenc