Tonal Space and the Human Mind By P. Janata Presented by Deepak Natarajan

Background Simple musical stimuli used in the past to measure expectancy violations Recently, neuroscientists prefer using more natural stimuli (REAL music), causing a need for modeling tonality in more sophisticated ways Neural, perceptual, and cognitive constraints of the human brain can provide more insight to music theorists and musicologists

Background Major perceptual dimensions of music (tonality, rhythm, timbre). Focus of this research is on tonality Analysis must be conscious of the human mind – Must establish short-term and long-term context of tonality – Need to consider other factors like human attention span We can use physiological knowledge to inform the models for music processing

Goal Develop models for tonal structure that are suitable for analyzing behavioral and neurophysiological data (Janata, 2005)

Navigating Tonal Space The toroidal model links music theory, cognitive psychology, and computational modeling (Krumhansl & Kessler, 1982)

Navigating Tonal Space Ability to use various distance metrics between keys Different disciplines seek specific relationships (eg. tonal/chordal relationships (circle of fifths) vs. psychological values (relatedness) All of these relationships can be modeled using self-organizing maps

Self-Organizing Maps An SOM is a type of artificial neural network that is trained using unsupervised learning – Unassuming of relationships among elements in source data – Produces a low-dimensional, discretized representation of the input space of the training samples – Can uncover structure in source data

SOMs in music theory The approach assumes that nervous systems learn to identify recurring patters of sensory input; the brain is a statistical engine We can use similarities in the pitch class distributions of input data and template data to train the neural network The learning algorithm adjusts the weights (ties) between input and output data to determine the most probabilistic key

SOMs in music theory Three types – Probe tone: Information on how well each of the twelve pitch-classes is perceived to fit into a particular key serves as an input to a SOM – Pitch-class: Similar to probe tone, but uses music theory (non-subjective) to create pitch-class distributions to represent the importance of each pitch in a key – Acoustic Waveforms: Uses models of known physiological mechanisms for defining transformations of the auditory input and subsequent representations

Acoustic Waveform SOMs (Janata, 2007)

Perception of Tonal Regions in a Modulating Melody To perform key finding, a SOM was trained on an 8 minute melody that modulated through all 24 major and minor keys – Resulted in an equal representation of PERCEIVED key regions Model was probed with various timescales using known stimuli and projected onto the SOM to determine activation dynamics at those different timescale

Input stimuli: B-major scale + variations (Janata, 2007)

Results: Activation Images 0.2 s2.0 s (Janata, 2007)

Results Activation consistently appears in the vicinity of the B-major label However, activation is biased toward different key regions, and the biasing depends on the harmonic structure of the input stimuli The 2.0 s time-scale activation patterns indicate the stable key. This analysis can be extended to even longer time-scales

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Brain Networks That Track Musical Structure Identifying regions of the brain that invoke tonal analysis (if at all!) – Compare model data to fMRI data and look for similarities. If matches are found, we can assume we are essentially modeling this function of the brain – Use outside knowledge to raise/answer questions concerning other brain functions occurring in those regions

Attentive listening Music provides a complex soundscape for attention to roam on We are interested in brain states that correspond to attentive and engaged listening To achieve this, neuroimaging experiments were performed where the subjects were asked to identify some phenomenon (eg. tonal expectancy violation)

Brain Activity fMRI data showed activity in premotor areas of the brain, which are known to be active in primarily perceptual tasks that have strong and directed anticipatory components to them This encourages the viewing of music in a perception/action cycle framework. The task demands shape the activity that we label as the brain’s processing of music.

Tracking Movement Through Tonal Space (Janata, 2005)

Tracking Movement Through Tonal Space Regions of high correlation between spherical harmonic model data and brain activity suggest other functions are linked to tracking tonality – Model data is extremely correlated with activity from the Rostral Medial Prefrontal Cortex (RMPFC) – This region is generally involved in the cognitive control and evaluation of emotion

Tracking Movement Through Tonal Space (Janata, 2005)

Revisit Goal Develop models for tonal structure that are suitable for analyzing behavioral and neurophysiological data (Janata, 2005)

Other Observations Rostral and ventral aspects of the MPFC are among the last in which significant cortical atrophy (weakening/degeneration) is observed in Alzheimer disease (AD) patient – (AD) patients have responded very positively to familiar music from their childhood, often singing along and readily detecting deviances implanted in the musical stimuli – Possibly suggests that the RMPFC is a locus at which music and autobiographical memories are bound together

References Janata, P. (2005). Brain networks that track musical structure. In The Neurosciences and Music II: From Perception to Performance (Vol. 1060): New York Academy of Sciences. Janata, P. (2007). Navigating tonal space. In E. Selfridge-Field (Ed.), Tonal Theory for the Digital Age (Computing in Musicology: Vol. 15, pp. 39– 50). Janata Lab (Center for Mind and Brain, UC Davis) –