Using decoding error patterns to trace the neural signature of auditory scene perception

Abstract

Multi-voxel pattern analysis has become a standard tool for analyzing neuroimaging data. In addition to the decoding accuracy, the particular pattern of decoding errors contains valuable information about the nature of neural code. We here use decoding errors in order to trace the processing of non-speech, non-music complex natural sounds in primary auditory cortex (A1) and its subdivisions, as well as across cortex. We use error patterns derived from an analysis of the physical stimulus properties and from a separate behavioral categorization experiment as references for this analysis. A linear mixed-effects model allows us to measure the similarity of the decoding error patterns to both of these references at the same time. Within A1 we find an interesting posterior-to-anterior trend, where the most posterior parts of A1 are linked more closely to properties of the stimuli, whereas more anterior parts of A1 are more closely aligned with human categorization behavior. In an exploratory searchlight analysis we find a similar trend across cerebral cortex. Decoding errors in occipital, posterior parietal and temporal cortex are more closely related to sound structure. Decoding errors in prefrontal cortex resemble behavioral errors. Our work illustrates the importance of decoding error analysis at the example of auditory processing in cortex and proposes linear mixed-effects modeling as a simple yet effective way for comparing decoding errors to reference error patterns obtained from auxiliary analyses or experiments.