The online processing of both music and language involves making predictions about upcoming material, but the relationship between prediction in these two domains is not well understood. Electrophysiological methods for studying individual differences in prediction in language processing have opened the door to new questions. Specifically, we ask whether individuals with musical training predict upcoming linguistic material more strongly and/or more accurately than non-musicians. We propose two reasons why prediction in these two domains might be linked: Musicians may have greater (...) verbal short-term/working memory; music may specifically reward predictions based on hierarchical structure. We provide suggestions as to how to expand upon recent work on individual differences in language processing to test these hypotheses. (shrink)

A critical issue in perception is the manner in which top‐down expectancies guide lower level perceptual processes. In speech, a common paradigm is to construct continua ranging between two phonetic endpoints and to determine how higher level lexical context influences the perceived boundary. We applied this approach to music, presenting participants with major/minor triad continua after brief musical contexts. Two experiments yielded results that differed from classic results in speech perception. In speech, context generally expands the category of the expected (...) stimuli. We found the opposite in music: The major/minor boundary shifted toward the expected category, contracting it. Together, these experiments support the hypothesis that musical expectancy can feed back to affect lower‐level perceptual processes. However, it may do so in a way that differs fundamentally from what has been seen in other domains. (shrink)

Although music is universal, there is a great deal of cultural variability in music structures. Nevertheless, some aspects of music processing generalize across cultures, whereas others rely heavily on the listening environment. Here, we discuss the development of musical knowledge, focusing on four themes: (a) capabilities that are present early in development; (b) culture-general and culture-specific aspects of pitch and rhythm processing; (c) age-related changes in pitch perception; and (d) developmental changes in how listeners perceive emotion in music.

Are similar, or even identical, mechanisms used in the computational modeling of speech segmentation, serial image processing, and music processing? We address this question by exploring how TRACX2, a recognition‐based, recursive connectionist autoencoder model of chunking and sequence segmentation, which has successfully simulated speech and serial‐image processing, might be applied to elementary melody perception. The model, a three‐layer autoencoder that recognizes “chunks” of short sequences of intervals that have been frequently encountered on input, is trained on the tone intervals of (...) melodically simple French children's songs. It dynamically incorporates the internal representations of these chunks into new input. Its internal representations cluster in a manner that is consistent with “human‐recognizable” melodic categories. TRACX2 is sensitive to both contour and proximity information in the musical chunks that it encounters in its input. It shows the “end‐of‐word” superiority effect demonstrated by Saffran et al. (1999) for short musical phrases. The overall findings suggest that the recursive autoassociative chunking mechanism, as implemented in TRACX2, may be a general segmentation and chunking mechanism, underlying not only word‐ and image‐chunking, but also elementary melody processing. (shrink)

Musical knowledge is largely implicit. It is acquired without awareness of its complex rules, through interaction with a large number of samples during musical enculturation. Whereas several studies explored implicit learning of mostly abstract and less ecologically valid features of Western music, very little work has been done with respect to ecologically valid stimuli as well as non-Western music. The present study investigated implicit learning of modal melodic features in North Indian classical music in a realistic and ecologically valid way. (...) It employed a cross-grammar design, using melodic materials from two modes that use the same scale. Findings indicated that Western participants unfamiliar with Indian music incidentally learned to identify distinctive features of each mode. Confidence ratings suggest that participants' performance was consistently correlated with confidence, indicating that they became aware of whether they were right in their responses; that is, they possessed explicit judgment knowledge. Altogether our findings show incidental learning in a realistic ecologically valid context during only a very short exposure, they provide evidence that incidental learning constitutes a powerful mechanism that plays a fundamental role in musical acquisition. (shrink)

Why should music be of interest to cognitive scientists, and what role does it play in human cognition? We review three factors that make music an important topic for cognitive scientific research. First, music is a universal human trait fulfilling crucial roles in everyday life. Second, music has an important part to play in ontogenetic development and human evolution. Third, appreciating and producing music simultaneously engage many complex perceptual, cognitive, and emotional processes, rendering music an ideal object for studying the (...) mind. We propose an integrated status for music cognition in the Cognitive Sciences and conclude by reviewing challenges and big questions in the field and the way in which these reflect recent developments. (shrink)

This study presents a probabilistic model of melody perception, which infers the key of a melody and also judges the probability of the melody itself. The model uses Bayesian reasoning: For any “surface” pattern and underlying “structure,” we can infer the structure maximizing P(structure|surface) based on knowledge of P(surface, structure). The probability of the surface can then be calculated as ∑ P(surface, structure), summed over all structures. In this case, the surface is a pattern of notes; the structure is a (...) key. A generative model is proposed, based on three principles: (a) melodies tend to remain within a narrow pitch range; (b) note‐to‐note intervals within a melody tend to be small; and (c) notes tend to conform to a distribution (or key profile) that depends on the key. The model is tested in three ways. First, it is tested on its ability to identify the keys of a set of folksong melodies. Second, it is tested on a melodic expectation task in which it must judge the probability of different notes occurring given a prior context; these judgments are compared with perception data from a melodic expectation experiment. Finally, the model is tested on its ability to detect incorrect notes in melodies by assigning them lower probabilities than the original versions. (shrink)

Following in a psychological and musicological tradition beginning with Leonard Meyer, and continuing through David Huron, we present a functional, cognitive account of the phenomenon of expectation in music, grounded in computational, probabilistic modeling. We summarize a range of evidence for this approach, from psychology, neuroscience, musicology, linguistics, and creativity studies, and argue that simulating expectation is an important part of understanding a broad range of human faculties, in music and beyond.

In Flourish, the positive psychologist Martin Seligman (2011) identifies five commonly recognized factors that are characteristic of human flourishing or wellbeing: (1) “positive emotion,” (2) “relationships,” (3) “engagement,” (4) “achievement,” and (5) “meaning” (p. 24). Although there is no settled set of necessary and sufficient conditions neatly circumscribing the bounds of human flourishing (Seligman, 2011), we would mostly likely consider a person that possessed high levels of these five factors as paradigmatic or prototypical of human flourishing. Accordingly, if we wanted (...) to go about the practical task of actually increasing our level of wellbeing, we ought to do so by focusing on practically increasing the levels of the five factors that are characteristic of wellbeing. If, for instance, an activity such as musical engagement can be shown to positively influence each or all of these five factors, this would be compelling evidence that an activity such as musical engagement can positively contribute to one’s living a flourishing life. I’m of the belief that psychological research can and should be used, not only to identify and diagnose maladaptive psychological states, but identify and promote adaptive psychological states as well. In this article I advance the hypothesis and provide supporting evidence for the claim that musical engagement can positively contribute to one’s living a flourishing life. Since there has not yet been a substantive and up-to-date investigation of the possible role of music in contributing to one’s living a flourishing life, the purpose of this article is to conduct this investigation, thereby bridging the gap and stimulating discussion between the psychology of music and the psychology of wellbeing. (shrink)

This paper reviews evidence concerning the nature of grouping in music and language and their interactions with other linguistic and musical systems. I present brief typological surveys of the relationship between constituency and acoustic parameters in language and music, drawing from a wide variety of languages and musical genres. The two domains both involve correspondence between auditory discontinuities and group boundaries, reflecting the Gestalt principles of proximity and similarity, as well as a nested, hierarchical organization of constituents. Typically, computational-level theories (...) of musical grouping take the form of a function from acoustic properties through grouping representations to syntactic or interpretive constituents. Linguistic theories tend to be cast as functions in the opposite direction. This study argues that the difference in orientation is not grounded in principled differences in information flow between the two domains, and that reconceptualizing one or both theories allows for gains in analytical understanding. There are also obvious differences between musical and linguistic grouping. Grappling with those differences requires one to think in detail about modularity, information flow, levels of description, and the functional nature of cognitive domains. (shrink)

A basic function of cognition is to detect regularities in sensory input to facilitate the prediction and recognition of future events. It has been proposed that these implicit expectations arise from an internal predictive coding model, based on knowledge acquired through processes such as statistical learning, but it is unclear how different types of statistical information affect listeners’ memory for auditory stimuli. We used a combination of behavioral and computational methods to investigate memory for non-linguistic auditory sequences. Participants repeatedly heard (...) tone sequences varying systematically in their information-theoretic properties. Expectedness ratings of tones were collected during three listening sessions, and a recognition memory test was given after each session. Information-theoretic measures of sequential predictability significantly influenced listeners’ expectedness ratings, and variations in these properties had a significant impact on memory performance. Predictable sequences yielded increasingly better memory performance with increasing exposure. Computational simulations using a probabilistic model of auditory expectation suggest that listeners dynamically formed a new, and increasingly accurate, implicit cognitive model of the information-theoretic structure of the sequences throughout the experimental session. (shrink)

Previous studies of auditory expectation have focused on the expectedness perceived by listeners retrospectively in response to events. In contrast, this research examines predictive uncertainty —a property of listeners' prospective state of expectation prior to the onset of an event. We examine the information-theoretic concept of Shannon entropy as a model of predictive uncertainty in music cognition. This is motivated by the Statistical Learning Hypothesis, which proposes that schematic expectations reflect probabilistic relationships between sensory events learned implicitly through exposure. Using (...) probability estimates from an unsupervised, variable-order Markov model, 12 melodic contexts high in entropy and 12 melodic contexts low in entropy were selected from two musical repertoires differing in structural complexity (simple and complex). Musicians and non-musicians listened to the stimuli and provided explicit judgments of perceived uncertainty (explicit uncertainty). We also examined an indirect measure of uncertainty computed as the entropy of expectedness distributions obtained using a classical probe-tone paradigm where listeners rated the perceived expectedness of the final note in a melodic sequence (inferred uncertainty). Finally, we simulate listeners' perception of expectedness and uncertainty using computational models of auditory expectation. A detailed model comparison indicates which model parameters maximize fit to the data and how they compare to existing models in the literature. The results show that listeners experience greater uncertainty in high-entropy musical contexts than low-entropy contexts. This effect is particularly apparent for inferred uncertainty and is stronger in musicians than non-musicians. Consistent with the Statistical Learning Hypothesis, the results suggest that increased domain-relevant training is associated with an increasingly accurate cognitive model of probabilistic structure in music. (shrink)

The cognition of music, like that of language, is partly rooted in enculturative processes of implicit and incidental learning. Musicians and nonmusicians alike are commonly found to possess detailed implicit knowledge of musical structure which is acquired incidentally through interaction with large samples of music. This paper reports an experiment combining the methodology of artificial grammar learning with musical acquisition of melodic structure. Participants acquired knowledge of grammatical melodic structures under incidental learning conditions in both experimental and untrained control conditions. (...) Subsequent analysis indicates a large effect of unsupervised online learning in the experimental and control group throughout the course of the testing phase suggesting an effective ongoing learning process. Musicians did not outperform nonmusicians, indicating that musical expertise is not advantageous for the learning of a new, unfamiliar melodic system. Confidence ratings suggest that participants became aware of the knowledge guiding their classification performance despite the incidental learning conditions. (shrink)