Simultaneous consonance is a salient perceptual phenomenon corresponding to the perceived pleasantness of simultaneously sounding musical tones. Various competing theories of consonance have been proposed over the centuries, but recently a consensus has developed that simultaneous consonance is primarily driven by harmonicity perception. Here we question this view, substantiating our argument by critically reviewing historic consonance research from a broad variety of disciplines, reanalyzing consonance perception data from 4 previous behavioral studies representing more than 500 participants, and modeling three Western musical corpora representing more than 100,000 compositions. We conclude that simultaneous consonance is a composite phenomenon that derives in large part from three phenomena: interference, periodicity/harmonicity, and cultural familiarity. We formalize this conclusion with a computational model that predicts a musical chord’s simultaneous consonance from these three features, and release this model in an open-source R package, incon, alongside 15 other computational models also evaluated in this paper. We hope that this package will facilitate further psychological and musicological research into simultaneous consonance.

Highlights d Musical pleasure depends on prospective and retrospective states of expectation d A machine-learning model quantified the uncertainty and surprise of pop song chords d Chords with low uncertainty and high surprise, and vice versa, evoked high pleasure d Joint effects of uncertainty and surprise found in the amygdala and auditory cortex

Memory, on multiple timescales, is critical to our ability to discover the structure of our surroundings, and efficiently interact with the environment. We combined behavioural manipulation and modelling to investigate the dynamics of memory formation for rarely reoccurring acoustic patterns. In a series of experiments, participants detected the emergence of regularly repeating patterns within rapid tone-pip sequences. Unbeknownst to them, a few patterns reoccurred every ~3 min. All sequences consisted of the same 20 frequencies and were distinguishable only by the order of tone-pips. Despite this, reoccurring patterns were associated with a rapidly growing detection-time advantage over novel patterns. This effect was implicit, robust to interference, and persisted for 7 weeks. The results implicate an interplay between short (a few seconds) and long-term (over many minutes) integration in memory formation and demonstrate the remarkable sensitivity of the human auditory system to sporadically reoccurring structure within the acoustic environment.

Beat perception is increasingly being recognised as a fundamental musical ability. A number of psychometric instruments have been developed to assess this ability, but these tests do not take advantage of modern psychometric techniques, and rarely receive systematic validation. The present research addresses this gap in the literature by developing and validating a new test, the Computerised Adaptive Beat Alignment Test (CA-BAT), a variant of the Beat Alignment Test (BAT) that leverages recent advances in psychometric theory, including item response theory, adaptive testing, and automatic item generation. The test is constructed and validated in four empirical studies. The results support the reliability and validity of the CA-BAT for laboratory testing, but suggest that the test is not well-suited to online testing, owing to its reliance on fine perceptual discrimination.

Modern psychometric theory provides many useful tools for ability testing, such as item response theory, computerised adaptive testing, and automatic item generation. However, these techniques have yet to be integrated into mainstream psychological practice. This is unfortunate, because modern psychometric techniques can bring many benefits, including sophisticated reliability measures, improved construct validity, avoidance of exposure effects, and improved efficiency. In the present research we therefore use these techniques to develop a new test of a well-studied psychological capacity: melodic discrimination, the ability to detect differences between melodies. We calibrate and validate this test in a series of studies. Studies 1 and 2 respectively calibrate and validate an initial test version, while Studies 3 and 4 calibrate and validate an updated test version incorporating additional easy items. The results support the new test’s viability, with evidence for strong reliability and construct validity. We discuss how these modern psychometric techniques may also be profitably applied to other areas of music psychology and psychological science in general.

Musical abilities and active engagement with music have been shown to be positively associated with many cognitive abilities as well as social skills and academic performance in secondary school students. While there is evidence from intervention studies that musical training can be a cause of these positive relationships, recent findings in the literature have suggested that other factors, such as genetics, family background or personality traits, might also be contributing factors. In addition, there is mounting evidence that self-concepts and beliefs can affect academic performance independently of intellectual ability. Students who believe that intelligence is malleable are more likely to attribute poor academic performances to effort rather than ability, and are more likely to take remedial action to improve their performance. However, it is currently not known whether student's beliefs about the nature of musical talent also influence the development of musical abilities in a similar fashion. Therefore, this study introduces a short self-report measure termed “Musical Self-Theories and Goals,” closely modeled on validated measures for self-theories in academic scenarios. Using this measure the study investigates whether musical self-theories are related to students' musical development as indexed by their concurrent musical activities and their performance on a battery of listening tests. We use data from a cross-sectional sample of 313 secondary school students to construct a network model describing the relationships between self-theories and academic as well as musical outcome measures, while also assessing potential effects of intelligence and the Big Five personality dimensions. Results from the network model indicate that self-theories of intelligence and musicality are closely related. In addition, both kinds of self-theories are connected to the students' academic achievement through the personality dimension conscientiousness and academic effort. Finally, applying the do-calculus method to the network model we estimate that the size of the assumed causal effects between musical self-theories and academic achievement lie between 0.07 and 0.15 standard deviations.

An important aspect of the perceived quality of vocal music is the degree to which the vocalist sings in tune. Although most listeners seem sensitive to vocal mistuning, little is known about the development of this perceptual ability or how it differs between listeners. Motivated by a lack of suitable preexisting measures, we introduce in this article an adaptive and ecologically valid test of mistuning perception ability. The stimulus material consisted of short excerpts (6 to 12 s in length) from pop music performances (obtained from MedleyDB; Bittner et al., 2014 ) for which the vocal track was pitch-shifted relative to the instrumental tracks. In a first experiment, 333 listeners were tested on a two-alternative forced choice task that tested discrimination between a pitch-shifted and an unaltered version of the same audio clip. Explanatory item response modeling was then used to calibrate an adaptive version of the test. A subsequent validation experiment applied this adaptive test to 66 participants with a broad range of musical expertise, producing evidence of the test’s reliability, convergent validity, and divergent validity. The test is ready to be deployed as an experimental tool and should make an important contribution to our understanding of the human ability to judge mistuning. Electronic supplementary material The online version of this article (10.3758/s13428-019-01225-1) contains supplementary material, which is available to authorized users.

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.