Abstract
The perceptual organization of pitch is frequently described as helical, with a monotonic dimension of pitch height and a circular dimension of pitch chroma, accounting for the repeating structure of the octave. Although the neural representation of pitch height is widely studied, the way in which pitch chroma representation is manifested in neural activity is currently debated. We tested the automaticity of pitch chroma processing using the MMN—an ERP component indexing automatic detection of deviations from auditory regularity. Musicians trained to classify pure or complex tones across four octaves, based on chroma—C versus G (21 participants, Experiment 1) or C versus F# (27, Experiment 2). Next, they were passively exposed to MMN protocols designed to test automatic detection of height and chroma deviations. Finally, in an “attend chroma” block, participants had to detect the chroma deviants in a sequence similar to the passive MMN sequence. The chroma deviant tones were accurately detected in the training and the attend chroma parts both for pure and complex tones, with a slightly better performance for complex tones. However, in the passive blocks, a significant MMN was found only to height deviations and complex tone chroma deviations, but not to pure tone chroma deviations, even for perfect performers in the active tasks. These results indicate that, although height is represented preattentively, chroma is not. Processing the musical dimension of chroma may require higher cognitive processes, such as attention and working memory.
| Original language | American English |
|---|---|
| Pages (from-to) | 669-685 |
| Number of pages | 17 |
| Journal | Journal of Cognitive Neuroscience |
| Volume | 31 |
| Issue number | 5 |
| DOIs | |
| State | Published - 2019 |
Bibliographical note
Funding Information:We are thankful to Prof. Roni Granot for fruitful discussions. We thank Assaf Brown for helping with recruitment of participants from the music academy and for consulting musical issues regarding study design. We also thank Noam Segel for aiding with data collection and analysis of Experiment 2. We thank all research assistants who helped with data collection and analysis—Geffen Markusfeld, Michal Rabinovits, Eden Krispin, Anael Benistri, and Lior Matityahu, who helped with formatting bibliography. T. I. R. was supported by the Hoffman Leadership and Responsibility Program at the Hebrew University. I. N. was supported by a grant from the Israel Academy of Sciences (390/ 13). L. Y. D. is supported by Jack H. Skirball research fund.
Publisher Copyright:
© 2019 Massachusetts Institute of Technology.