We investigated the perception of music in a cognitive musicology study, employing behavioral methods to examine general associative patterns--i.e. the propensity for subjects to recruit associations when listening to music, reminiscent of synaesthetic cross-wiring (Cytowic, 2009). Although non-Synaesthetic associations to music are less explored, experiments such as Köhler’s (1929) linguistic “Kiki, Boulba” study, demonstrated associations in non-synaesthetes, supporting the hypothesis that general listeners engage cross-sensorial connections.
Here we present an extension to the studyforrest dataset – a versatile resource for studying the behavior of the human brain in situations of real-life complexity (http://studyforrest.org). This release adds more high-resolution, ultra high-field (7 Tesla) functional magnetic resonance imaging (fMRI) data from the same individuals. The twenty participants were repeatedly stimulated with a total of 25 music clips, with and without speech content, from five different genres using a slow event-related paradigm.
Our research explores theories based upon past behavioural studies and FMRI
scans with Synaesthetes and general listeners. FMRI experiments have revealed that the
cross-modal associations to sounds in Synaesthetes are less pronounced, yet still present
in the general population. The results of our psycho-musicology study with 40
Synaesthetes and 40 non-Synaesthetes reveal a quasi-Synaesthetic [Nikolic, 2014]
spectrum extending to general listeners, similar to culturally founded Synaesthesia
[Kohler].
The cognitive representations that support our experience of pitch perception and imagery are not well understood and they generally focus on tonotopic organization of neural columns in the brain (place-based coding of absolute frequency). From prior behavioural studies, we understand musical pitch space to be relative to a reference key, and hierarchically organized. Our current study uses a new between-subject common representation of spatio-temporal multivariate population codes to identify the representational space of musical pitch.
A new branch of systematic musicology, “normative musicology,” is proposed and its practice demonstrated. Normative musicology is the study of optimal (“norma-tive”) expectations about future musical signals, given some corpus of past signals. It is a formalization of many “statistical learning” approaches (e.g. [1]) and may be considered a computational counterpart to empirical musicology.
