Abstract
Statistical learning is a cognitive process of great importance for the detection and representation of environmental regularities. Complex cognitive processes such as statistical learning usually emerge as a result of the activation of widespread cortical areas functioning in dynamic networks. The present study investigated the cortical large-scale network supporting statistical learning of tone sequences in humans. The reorganization of this network related to musical expertise was assessed via a cross-sectional comparison of a group of musicians to a group of non-musicians. The cortical responses to a statistical learning paradigm incorporating an oddball approach were measured via Magnetoencephalographic (MEG) recordings. Large-scale connectivity of the cortical activity was calculated via a statistical comparison of the estimated transfer entropy in the sources’ activity. Results revealed the functional architecture of the network supporting the processing of statistical learning, highlighting the prominent role of informational processing pathways that bilaterally connect superior temporal and intraparietal sources with the left IFG. Musical expertise is related to extensive reorganization of this network, as the group of musicians showed a network comprising of more widespread and distributed cortical areas as well as enhanced global efficiency and increased contribution of additional temporal and frontal sources in the information processing pathway.
Highlights
The human ability to extract regularities underlying the arrangement of the stimuli within a given stream, independently of the perceptual modality, is referred to as statistical learning
During the MEG measurement, two tone sequence sets were combined and randomly interleaved in an oddball paradigm: (a) the standard tone sequences which are sequences presented with a higher probability rate and (b) the deviant ones which are sequences presented with a lower probability
This network consisted of 230 nodes out of the 266 nodes that were available in the mask, and 920 edges, which represent the connections between nodes
Summary
The human ability to extract regularities underlying the arrangement of the stimuli within a given stream, independently of the perceptual modality, is referred to as statistical learning. Recent neuroimaging studies indicate that long term musical training may enhance implicit learning of auditory input and increase the ability to segment a series of tones or syllables on the basis of the underlying distributional properties of the corresponding stream[10]. The scope of the present study was to investigate functional connectivity changes of the cortical network underlying statistical learning using MEG measurements, and to assess how this network is reorganized due to long term musical training To this aim we re-analyzed the data of our previous study[11] that compared statistical learning effects in musicians and non-musicians following an approach that allowed us to identify changes in the corresponding cortical network via a graph theoretical approach. Based on the fact that musicians show enhanced cortical connectivity, in comparison to non-musicians, when confronted to music-related tasks[21], we hypothesized that under the condition of a statistical learning experiment musicians would show increased sharing of information between distributed cortical areas and increased connectivity compared with the non-musicians’ network
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
