A combined model of sensory and cognitive representations underlying tonal expectations in music: From audio signals to behavior.

Abstract

Listeners' expectations for melodies and harmonies in tonal music are perhaps the most studied aspect of music cognition. Long debated has been whether faster response times (RTs) to more strongly primed events (in a music theoretic sense) are driven by sensory or cognitive mechanisms, such as repetition of sensory information or activation of cognitive schemata that reflect learned tonal knowledge, respectively. We analyzed over 300 stimuli from 7 priming experiments comprising a broad range of musical material, using a model that transforms raw audio signals through a series of plausible physiological and psychological representations spanning a sensory-cognitive continuum. We show that RTs are modeled, in part, by information in periodicity pitch distributions, chroma vectors, and activations of tonal space--a representation on a toroidal surface of the major/minor key relationships in Western tonal music. We show that in tonal space, melodies are grouped by their tonal rather than timbral properties, whereas the reverse is true for the periodicity pitch representation. While tonal space variables explained more of the variation in RTs than did periodicity pitch variables, suggesting a greater contribution of cognitive influences to tonal expectation, a stepwise selection model contained variables from both representations and successfully explained the pattern of RTs across stimulus categories in 4 of the 7 experiments. The addition of closure--a cognitive representation of a specific syntactic relationship--succeeded in explaining results from all 7 experiments. We conclude that multiple representational stages along a sensory-cognitive continuum combine to shape tonal expectations in music.

Processing structure in language and music: a case for shared reliance on cognitive control

“…This account is supported by demonstrations that the effects of many types of harmonic structural manipulations can be explained in terms of plausible sensory mechanisms (Collins, Tillmann, Barrett, Delbé, & Janata, 2014) and that harmonic manipulations can influence the attention devoted to concurrent non-musical (and non-linguistic) tasks (e.g., Escoffier & Tillmann, 2008). However, it seems unlikely that the interactions between harmonic and linguistic structure described above are due entirely to shared reliance on attentional resources for two reasons.…”

Tonal priming is resistant to changes in pitch height

“…However, more recent evidence has argued strongly against a purely sensory account of tonal priming (Bigand et al, 2003;Marmel et al, 2010;Vuvan & Schmuckler, 2011). A recent comprehensive review, meta-analysis, and updated modeling of numerous priming studies demonstrated that both sensory and cognitive factors play roles in tonal priming, and moreover, that cognitive factors prevail as the more influential ones (Collins et al, 2014). These authors predicted the strength of priming effects using three factors-periodicity pitch (sensory), the distribution of pitch classes (chroma vector), and the relative activation of musical keys (tonal space).…”

“…The roles of sensory and cognitive priming are especially important in music, in particular with regard to tonal priming (for a review, see Collins, Tillmann, Barrett, Delbé, & Janata, 2014). This phenomenon typically refers to the fact that for listeners enculturated in Western music, a prime chord (multiple simultaneous notes) creates a strong expectation for a musically related chord (Bharucha & Stoeckig, 1986;Bigand, Poulin, Tillmann, Madurell, & D'Adamo, 2003).…”

“…Without a component of pitch height in MUSACT, a primed chord should receive the same benefit regardless of the octave in which it occurred. Although this model is successful at modeling perceptual data (see Collins et al, 2014), including a self-organizing neural network instantiation (Tillmann, Bharucha, & Bigand, 2000), this prediction has not been tested empirically. Because the model proposed by Collins et al (2014) uses an auditory input as a signal, it should be capable of addressing the role of pitch height.…”

“…Although this model is successful at modeling perceptual data (see Collins et al, 2014), including a self-organizing neural network instantiation (Tillmann, Bharucha, & Bigand, 2000), this prediction has not been tested empirically. Because the model proposed by Collins et al (2014) uses an auditory input as a signal, it should be capable of addressing the role of pitch height. However, despite data based on the model fitting a majority of the prior studies well, no specific testing of the effect of pitch height on tonal priming has yet been conducted.…”