Expectations for tonal cadences: Sensory and cognitive priming effects

Abstract

Studies examining the formation of melodic and harmonic expectations during music listening have repeatedly demonstrated that a tonal context primes listeners to expect certain (tonally related) continuations over others. However, few such studies have (1) selected stimuli using ready examples of expectancy violation derived from real-world instances of tonal music; (2) provided a consistent account for the influence of sensory and cognitive mechanisms on tonal expectancies by comparing different computational simulations; or (3) combined melodic and harmonic representations in modelling cognitive processes of expectation. To resolve these issues, this study measures expectations for the most recurrent cadence patterns associated with tonal music and then simulates the reported findings using three sensory-cognitive models of auditory expectation. In Experiment 1, participants provided explicit retrospective expectancy ratings both before and after hearing the target melodic tone and chord of the cadential formula.In Experiment 2, participants indicated as quickly as possible whether those target events were in or out of tune relative to the preceding context. Across both experiments, cadences terminating with stable melodic tones and chords elicited the highest expectancy ratings and the fastest and most accurate responses. Moreover, the model simulations supported a cognitive interpretation of tonal processing, in which listeners with exposure to tonal music generate expectations as a consequence of the frequent (co-)occurrence of events on the musical surface.

Expecting the end: Continuous expectancy ratings for tonal cadences

“…Type III Wald F tests of the fixed effects from the 5×2 LMM of the slider maxima revealed a significant effect of cadence category, F (4, 38.11) = 2.64, p = .049, and a significant interaction, F (4, 28.74) = 4.16, p =.009, but there was no main effect of training. As expected, the half cadence category received the lowest maximum rating on average, and polynomial contrasts revealed a quadratic trend in the ratings of musicians, B = 3.26, t = 4.28, p < .001, thereby replicating the U-shaped curves found in previous studies (Sears et al, 2019). Although the same U-shaped trend emerged in the ratings of the nonmusician group, the polynomial contrast was not significant.…”

“…To measure these sorts of expectancies for the events at cadential arrival, Sears et al (2019) adopted a priming paradigm and used a competing secondary task to orient the participants’ attention to other features of the stimulus. Participants indicated as quickly as possible whether the target melodic tone and chord were in or out of tune, where out-of-tune foil trials were tuned 40 cents sharp relative to the preceding context.…”

“…Using both explicit and implicit tasks, Sears et al (2019) found that cadences terminating with stable tones and chords—the PAC and IAC categories—elicited the highest goodness-of-fit ratings and fastest and most accurate responses. What is more, model simulations for the observed priming effects supported a cognitive interpretation of tonal processing, in which listeners with exposure to tonal music generate expectations as a consequence of the frequent (co-)occurrence of events on the musical surface (see also Sears, Pearce, Caplin, & McAdams, 2018).…”

The MUST Set and Toolbox: A Set of 200 Musical Stimuli Varying in Balance, Contour, Symmetry, and Complexity: Behavioral and Computational Assessments

“…IDyOM has been shown to accurately predict Western listeners' pitch expectations in behavioral, physiological, and EEG studies (e.g., Egermann et al, 2013;Hansen & Pearce, 2014;Omigie, Pearce, & Stewart, 2012;Omigie, Pearce, Williamson, & Stewart, 2013;Pearce, 2005;Pearce, Ruiz, Kapasi, Wiggins, & Bhattacharya, 2010), even better than static rule-based models (e.g., Narmour, 1991;Schellenberg, 1997). It has also been proved to account for expectations of the timing of melodic events (Sauvé, Sayed, Dean, & Pearce, 2018) and harmonic movement (Sears, Pearce, Spitzer, Caplin, & McAdams, 2018;Harrison & Pearce, 2018), and to simulate other psychological processes in music perception, including similarity perception (Pearce & Müllensiefen, 2017), recognition Running head: THE MUST SET AND TOOLBOX memory (Agres, Abdallah, & Pearce, 2018), phrase boundary perception (Pearce, Müllensiefen, & Wiggins, 2010), and aspects of emotional experience (Egermann et al, 2013;Gingras et al, 2016;Sauvé et al, 2018). We used the IDyOM in two configurations: first, the short-term model (STM) that learns incrementally on each stimulus independently; second, adding to the STM a long-term model (LTM) trained on a large corpus of Western tonal music (903 folk songs and chorales; datasets 1, 2, and 9 from Table 4.1 in Pearce, 2005, comprising 50,867 notes): the BOTH configuration.…”

The Expectancy Dynamics of Anti-Tonal Twelve-Tone Rows: A Commentary and Reanalysis of von Hippel & Huron (2020)

“…A growing body of research indeed demonstrates that expectations play a key role in all of these areas. This is, for example, the case for segmentation of music into phrases (Pearce, Müllensiefen, & Wiggins, 2010;Hansen, Kragness, Vuust, Trainor, & Pearce, under review), memory for melodies (Agres, Abdallah, & Pearce, 2018), stylistic enculturation (Hansen, Vuust, & Pearce, 2016;Morrison, Demorest, & Pearce, 2018), and the cognition of musical repetition (Margulis, 2014) and cadence formulas (Sears, Pearce, Caplin, & McAdams, 2018;Sears, Pearce, Spitzer, Caplin, & McAdams, 2019). Thus, claims about anti-tonality in dodecaphonic composition ultimately makes assumptions about psychological expectation mechanisms rather than about musical structure per se (i.e., independent from the listener's perspective).…”