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- # Metadata for DOI registration according to DataCite Metadata Schema 4.1.
- # For detailed schema description see https://doi.org/10.5438/0014
- ## Required fields
- # The main researchers involved. Include digital identifier (e.g., ORCID)
- # if possible, including the prefix to indicate its type.
- authors:
- -
- firstname: "Evangelos"
- lastname: "Paraskevopoulos"
- affiliation: "Department of Psychology, University of Cyprus, Nicosia, Cyprus"
- id: "0000-0003-4063-4182"
- -
- firstname: "Alexandra"
- lastname: "Anagnostopoulou"
- affiliation: "School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece"
- id: "0000-0001-9578-7552"
- -
- firstname: "Nikolas"
- lastname: "Chalas"
- affiliation: "Institute for Biomagnetism and Biosignal Analysis, University of Münster, Germany"
- id: "0000-0001-5769-0055"
- -
- firstname: "Maria"
- lastname: "Karagianni"
- affiliation: "School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece"
- -
- firstname: "Panagiotis"
- lastname: "Bamidis"
- affiliation: "School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece"
- id: "0000-0002-9936-5805"
- # A title to describe the published resource.
- title: "Unravelling the multisensory learning advantage: Different patterns of within and across frequency-specific interactions drive uni- and multisensory neuroplasticity"
- # Additional information about the resource, e.g., a brief abstract.
- description: "In the field of learning theory and practice, the superior efficacy of multisensory learning over uni-sensory is well-accepted. However, the underlying neural mechanisms at the macro-level of the human brain remain largely unexplored. This study addresses this gap by providing novel empirical evidence and a theoretical framework for understanding the superiority of multisensory learning. Through a cognitive, behavioral, and electroencephalographic assessment of carefully controlled uni-sensory and multisensory training interventions, our study uncovers a fundamental distinction in their neuroplastic patterns. The outcomes confirm the superior efficacy of multisensory learning in enhancing cognitive processes and improving multisensory processing. A multilayered network analysis of pre- and post- training EEG data allowed us to model connectivity within and across different frequency bands at the cortical level. Pre-training EEG analysis unveils a complex network of distributed sources communicating through cross-frequency coupling, while comparison of pre- and post-training EEG data demonstrates significant differences in the reorganizational patterns of uni-sensory and multisensory learning. Uni-sensory training primarily modifies cross-frequency coupling between lower and higher frequencies, whereas multisensory training induces changes within the beta band in a more focused network, implying the development of a unified representation of audiovisual stimuli. In combination with behavioural and cognitive findings this suggests that, multisensory learning benefits from an automatic top-down transfer of training, while uni-sensory training relies mainly on limited bottom-up generalization. Our findings offer a compelling theoretical framework for understanding the advantage of multisensory learning."
- # Lit of keywords the resource should be associated with.
- # Give as many keywords as possible, to make the resource findable.
- keywords:
- - Neuroscience
- - Multisensory learning
- # License information for this resource. Please provide the license name and/or a link to the license.
- # Please add also a corresponding LICENSE file to the repository.
- license:
- name: "Creative Commons CC0 1.0 Public Domain Dedication"
- url: "https://creativecommons.org/publicdomain/zero/1.0/"
- ## Optional Fields
- # Funding information for this resource.
- # Separate funder name and grant number by comma.
- funding:
- - "HFRI: 2089"
- # Related publications. reftype might be: IsSupplementTo, IsDescribedBy, IsReferencedBy.
- # Please provide digital identifier (e.g., DOI) if possible.
- # Add a prefix to the ID, separated by a colon, to indicate the source.
- # Supported sources are: DOI, arXiv, PMID
- # In the citation field, please provide the full reference, including title, authors, journal etc.
- references:
- -
- id: "doi:tba"
- reftype: "IsSupplementTo"
- citation: "tba"
- # Resource type. Default is Dataset, other possible values are Software, DataPaper, Image, Text.
- resourcetype: Dataset
- # Do not edit or remove the following line
- templateversion: 1.2
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