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Authors: Alex Frid 1 ; Hananel Hazan 2 ; Ester Koilis 1 ; Larry M. Manevitz 3 ; Maayan Merhav 4 and Gal Star 1

Affiliations: 1 University of Haifa, Israel ; 2 Technion, Israel ; 3 University of Haifa, Center of Information and Neural Networks and National Institute of Information and Communications Technology, Israel ; 4 German Center for Neurodegenerative Diseases (DZNE), Germany

ISBN: 978-989-758-157-1

Keyword(s): Machine Learning, Classification, functional Magnetic Resonance Imaging (fMRI), Feature Selection, Support Vector Machines, Radial Basis Function Kernel, Declarative Memory, Information Biomarkers.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computational Neuroscience ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Support Vector Machines and Applications ; Theory and Methods

Abstract: This work uses supervised machine learning methods over fMRI brain scans to establish the existence of two different encoding procedures for human declarative memory. Declarative knowledge refers to the memory for facts and events and initially depends on the hippocampus. Recent studies which used patients with hippocampal lesions and neuroimaging data, suggested the existence of an alternative process to form declarative memories. This process is triggered by learning mechanism called "Fast Mapping (FM)", as opposed to the 'standard' "Explicit Encoding (EE)" learning procedure. The present work gives a clear biomarker on the existence of two distinct encoding procedures as we can accurately predict which of the processes is being used directly from voxel activity in fMRI scans. The scans are taken during retrieval of information wherein the tasks are identical regardless of which procedure was used for acquisition and by that reflect conclusive prediction. This is an identification o f a more subtle cognitive task than direct perceptual cognitive tasks as it requires some encoding and processing in the brain. (More)

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Paper citation in several formats:
Frid, A.; Hazan, H.; Koilis, E.; M. Manevitz, L.; Merhav, M. and Star, G. (2015). Machine Learning Techniques and the Existence of Variant Processes in Humans Declarative Memory.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 114-121. DOI: 10.5220/0005594501140121

@conference{ncta15,
author={Alex Frid. and Hananel Hazan. and Ester Koilis. and Larry M. Manevitz. and Maayan Merhav. and Gal Star.},
title={Machine Learning Techniques and the Existence of Variant Processes in Humans Declarative Memory},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)},
year={2015},
pages={114-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005594501140121},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)
TI - Machine Learning Techniques and the Existence of Variant Processes in Humans Declarative Memory
SN - 978-989-758-157-1
AU - Frid, A.
AU - Hazan, H.
AU - Koilis, E.
AU - M. Manevitz, L.
AU - Merhav, M.
AU - Star, G.
PY - 2015
SP - 114
EP - 121
DO - 10.5220/0005594501140121

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