Expectation, or prediction, has become a major theme in cognitive science. Music offers a powerful system for studying how expectations are formed and deployed in the processing of richly structured sequences that unfold rapidly in time. We ask to what extent expectations about an upcoming note in a melody are driven by two distinct factors: Gestalt-like principles grounded in the auditory system (e.g. a preference for subsequent notes to move in small intervals), and statistical learning of melodic structure. We use multinomial regression

*Previous work has shown that the N400 ERP component is elicited by all words, whether presented in isolation or in structured contexts, and that its amplitude is modulated by semantic association and contextual predictability. What is less clear is the extent to which the N400 response is modulated by semantic incongruity when predictability is held constant. In the current study we examine N400 modulation associated with independent manipulations of predictability and congruity in an adjective-noun paradigm that allows us to precisely control predictability through corpus counts. Our results demonstrate small N400 effects of semantic congruity (yellow bag vs. innocent bag), and much more robust N400 effects of predictability (runny nose vs. dainty nose) under the same conditions. These data argue against unitary N400 theories according to which N400 effects of both predictability and incongruity reflect a common process such as degree of integration difficulty, as large N400 effects of predictability were observed in the absence of large N400 effects of incongruity. However, the data are consistent with some versions of unitary 'facilitated access' N400 theories, as well as multiple-generator accounts according to which the N400 can be independently modulated by facilitated conceptual/lexical access (as with predictability) and integration difficulty (as with incongruity, perhaps to a greater extent in full sentential contexts).

Prediction or expectancy is thought to play an important role in both music and language processing. However, prediction is currently studied independently in the two domains, limiting research on relations between predictive mechanisms in music and language. One limitation is a difference in how expectancy is quantified. In language, expectancy is typically measured using the cloze probability task, in which listeners are asked to complete a sentence fragment with the first word that comes to mind. In contrast, previous production-based studies of melodic expectancy have asked participants to sing continuations following only one to two notes. We have developed a melodic cloze probability task in which listeners are presented with the beginning of a novel tonal melody (5–9 notes) and are asked to sing the note they expect to come next. Half of the melodies had an underlying harmonic structure designed to constrain expectations for the next note, based on an implied authentic cadence (AC) within the melody. Each such ‘authentic cadence’ melody was matched to a ‘non-cadential’ (NC) melody matched in terms of length, rhythm and melodic contour, but differing in implied harmonic structure. Participants showed much greater consistency in the notes sung following AC vs. NC melodies on average. However, significant variation in degree of consistency was observed within both AC and NC melodies. Analysis of individual melodies suggests that pitch prediction in tonal melodies depends on the interplay of local factors just prior to the target note (e.g., local pitch interval patterns) and larger-scale structural relationships (e.g., melodic patterns and implied harmonic structure). We illustrate how the melodic cloze method can be used to test a computational model of melodic expectation. Future uses for the method include exploring the interplay of different factors shaping melodic expectation, and designing experiments that compare the cognitive mechanisms of prediction in music and language.