Neural patterns reveal single-trial information on absolute pitch and relative pitch perception
Authors: Silvano Sele
Marielle Greber
Lutz Jäncke
Simon Leipold
Journal: NeuroImage (2019)
Discipline: NEUROSCIENCE & BEHAVIOR
Keywords: Auditory perception; Decoding; EEG; Multivariate pattern analysis; Representational similarity analysis.

Abstract:  Pitch is a fundamental attribute of sounds and yet is not perceived equally by all humans. Absolute pitch (AP) musicians perceive, recognize, and name pitches in absolute terms, whereas relative pitch (RP) musicians, representing the large majority of musicians, perceive pitches in relation to other pitches. In this study, we used electroencephalography (EEG) to investigate the neural representations underlying tone listening and tone labeling in a large sample of musicians (n = 105). Participants performed a pitch processing task with a listening and a labeling condition during EEG acquisition. Using a brain-decoding framework, we tested a prediction derived from both theoretical and empirical accounts of AP, namely that the representational similarity of listening and labeling is higher in AP musicians than in RP musicians. Consistent with the prediction, time-resolved single-trial EEG decoding revealed a higher representational similarity in AP musicians during late stages of pitch perception. Time-frequency-resolved EEG decoding further showed that the higher representational similarity was present in oscillations in the theta and beta frequency bands. Supplemental univariate analyses were less sensitive in detecting subtle group differences in the frequency domain. Taken together, the results suggest differences between AP and RP musicians in late pitch processing stages associated with cognition, rather than in early processing stages associated with perception. 

Retracted: No

PubPeer comments:
 In this paper they compared absolute pitch musicians and relative pitch musicians. **in the Chapter  2.6.1. Post-hoc source estimation ** The author did a source reconstruction in brainstorm. What they Did: Estimated Source -Averaged the Sources in Time -subtracted the current density values in the listening condition from the values in the labeling condition -averaged these differences per group. ![file] From my Point of view there are mistakes in the analysis pipeline. 1.tI m missing a Source Reconstructionn for the N100 Peak to verify that the Inverse Solution worked as it should. Since theyanalyzedd EEG Data from a pitch processing task with a listening and a labeling condition it should be obvious that you should briefly show that the Inverse Solution works properly! 2.tBased on the scale next to the picture, I conclude that he did not apply a baseline normalization on the sources as recommended in the tutorials on Brainstorm. 2.tWrong Colormap and Threshold. After you subtract in Brainstorm 2 Sources, the Scale should look like this: ![file] Red indicates that Condition A had more Activation and Blue That Condition B had more Activation. The author only shows where condition a is stronger, but it would be Interesting, if somewhere Condition B is stronger Based on my experience, when averaging unstandardized sources(Especially with the Dipole orientations  Loose ), they tend to show activation in the motor area, where actuality there aren t any activation at all. So from this Point of view, for me this Figure is meaningless. ,