Two approaches exist for explaining harmonic expectation. The sensory approach claims that harmonic expectation is a low-level process driven by sensory responses to acoustic properties of musical sounds. Conversely, the cognitive approach describes harmonic expectation as a high-level cognitive process driven by the recognition of syntactic structure learned through experience. Many previous studies have sought to distinguish these two hypotheses, largely yielding support for the cognitive hypothesis. However, subsequent re-analysis has shown that most of these results can parsimoniously be explained by a computational model from the sensory tradition, namely Leman’s (2000) model of auditory short- term memory (Bigand, Delbé, Poulin-Charronnat, Leman, & Tillmann, 2014). In this research we re-examine the explanatory power of auditory short-term memory models, and compare them to a new model in the Information Dynamics Of Music (IDyOM) tradition, which simulates a cognitive theory of harmony perception based on statistical learning and probabilistic prediction. We test the ability of these models to predict the surprisingness of chords within chord sequences (N = 300), as reported by a sample group of university undergraduates (N = 50). In contrast to previous studies, which typically use artificial stimuli composed in a classical idiom, we use naturalistic chord sequences sampled from a large dataset of popular music. Our results show that the auditory short-term memory models have remarkably low explanatory power in this context. In contrast, the new statistical learning model predicts surprisingness ratings relatively effectively. We conclude that auditory short-term memory is insufficient to explain harmonic expectation, and that cognitive processes of statistical learning and probabilistic prediction provide a viable alternative.
Forecasting Outbreak of COVID-19 in Turkey; Comparison of Box–Jenkins, Brown’s Exponential Smoothing and Long Short-Term Memory Models
