Figure 1.

IDyOM model. We used the IDyOM model (Pearce, 2005, 2018) to systematically measure music unpredictability as IC and entropy. As configured here, IDyOM first builds a long-term model (LTM) of the statistical structure of a large training set of 903 melodies, represented as sequences of pitches and inter-onset interval ratios (IOIr). In a new stimulus melody with n notes, IDyOM then estimates the probability of each possible continuation x from an alphabet X, at each note index i based on the LTM and a short-term model (STM) learned dynamically within the current stimulus (i.e., from note 1 to note i). To combine the probabilities derived from the LTM and STM, IDyOM first computes a geometric mean (signified by '*') of the LTM and STM probabilities for pitch and IOIr separately, weighting each according to its entropy such that predictions based on higher-entropy models are less influential, and then multiplies these resulting pitch and IOIr probabilities. It then computes the note's IC as its negative log probability to the base 2, and its entropy as the expected value of the IC across all possible continuations (X). The result is a reliable computational measure of pitch unpredictability and uncertainty based on long- and short-term musical statistics. In the present studies, we averaged these note-by-note measures across each stimulus to represent each 30 s stimulus as one unit.