"If You Have to Ask, You'll Never Know": Effects of Specialised Stylistic Expertise on Predictive Processing of Music

Abstract

Musical expertise entails meticulous stylistic specialisation and enculturation. Even so, research on musical training effects has focused on generalised comparisons between musicians and non-musicians, and cross-cultural work addressing specialised expertise has traded cultural specificity and sensitivity for other methodological limitations. This study aimed to experimentally dissociate the effects of specialised stylistic training and general musical expertise on the perception of melodies. Non-musicians and professional musicians specialising in classical music or jazz listened to sampled renditions of saxophone solos improvised by Charlie Parker in the bebop style. Ratings of explicit uncertainty and expectedness for different continuations of each melodic excerpt were collected. An information-theoretic model of expectation enabled selection of stimuli affording highly certain continuations in the bebop style, but highly uncertain continuations in the context of general tonal expectations, and vice versa. The results showed that expert musicians have acquired probabilistic characteristics of music influencing their experience of expectedness and predictive uncertainty. While classical musicians had internalised key aspects of the bebop style implicitly, only jazz musicians’ explicit uncertainty ratings reflected the computational estimates, and jazz-specific expertise modulated the relationship between explicit and inferred uncertainty data. In spite of this, there was no evidence that non-musicians and classical musicians used a stylistically irrelevant cognitive model of general tonal music providing support for the theory of cognitive firewalls between stylistic models in predictive processing of music.

Commentary: Predictions and the brain: how musical sounds become rewarding

“…From a computational perspective, the former relies on reinforcement learning, which sets up computational principles for maximizing reward value, irrespective of music-structural specifics (Schultz, 2013). The latter deals with predictions concerning musical structure and has been modeled using statistical learning and predictive coding (Vuust et al, 2009; Hansen and Pearce, 2014; Vuust and Witek, 2014; Hansen et al, 2016). In predictive coding, PE is neither “positive” nor “negative” per se , but rather strong/weak on a single continuum (Friston and Stephan, 2007).…”

“…If the new sounds were better than expected [i.e., RPE], positive PE would result.” Assessing whether music is “structurally-better-than-expected” requires a clear definition of “structurally good.” In music listening, however, expectations more likely pertain to the structure of music (PE) than to its reward value (RPE) (Huron, 2006; Miranda and Ullman, 2007; Hansen and Pearce, 2014; Vuust and Witek, 2014; Hansen et al, 2016). Accordingly, Salimpoor et al's notion of valenced PE with respect to structural continuation is problematic because it conflates expectations about experienced pleasure and perceived sounds.…”

“…(A) Initially, because unfamiliar music affords a weak predictive model, the gain on error units is set such that prediction error is low in salience (Friston, 2009). With increasing exposure, music is contextualized (e.g., determining key, tempo, meter, instrumentation, duration, form, genre) leading to schematic expectations with higher certainty (Hansen and Pearce, 2014; Hansen et al, 2016). Simultaneously, veridical expectations arise causing potential conflicts with schematic expectations (Huron, 2006).…”

“…Simultaneously, veridical expectations arise causing potential conflicts with schematic expectations (Huron, 2006). This sharpens the listener's predictive model generating stronger expectations with higher error-unit gain, leading to gradually more salient prediction error (Hansen and Pearce, 2014; Hansen et al, 2016). (B) .…”

Perception of Leitmotives in Richard Wagner's Der Ring des Nibelungen

“…While the aforementioned studies tend to reflect differences measuring individual's musical training, other studies have suggested that factors outside of musical training such as familiarity with the genre or style of the musical material (Tervaniemi, 2009; Hansen et al, 2016) as well as other non-performative abilities can play a crucial role in perceptual models (Bigand and Poulin-Charronnat, 2006). Though literature is sparse regarding perceptual models that takes into account genre familiarity, there are a number of studies that aim at mid-level features, such as schematic expectations (Eerola et al, 2009), and that do take into account listener backgrounds and musical acculturation that can be integrated in the modeling process via mechanisms such as statistical learning (Krumhansl et al, 2000).…”