[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124012-en":3,"doc-seo-124012-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124012,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Estimating Person-Specific Neural Correlates of Mental Rotation - A Machine Learning Approach","Using neurophysiological measures to model how the brain performs complex cognitive tasks such as mental rotation is a promising way toward precise predictions of behavioural responses. The mental rotation task requires objects to be mentally rotated in space and can be used to monitor progressive neurological disorders. Here, individually tailored machine learning models estimate person-specific neural activity, training ridge regressions on task-related EEG to predict reaction times.","PLOS ONE  \nOPEN ACCESS  \nCitation: Uslu S, Tangermann M, Vo¨gele C (2024) Estimating person-specific neural correlates of mental rotation: A machine learning approach. PLoS ONE 19(1): e0289094 . [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.1371/journal.pone](10.1371/journal.pone).0289094  \nEditor: Humaira Nisar, Universiti Tunku Abdul Rahman, MALAYSIA  \nReceived: June 15, 2023  \nAccepted: December 29, 2023  \nPublished: January 31, 2024  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here:  \n[https://doi.org/10.1371/journal.pone.0289094](https://doi.org/10.1371/journal.pone.0289094)  \n[Copyright:](Copyright:) © [2024](2024) Uslu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: The source code is available at [https://github.com/UsluSinan/EEG](https://github.com/UsluSinan/EEG)correlates-of-mental-rotation under the MIT license. It contains scripts to simulate random EEG data in the required format to run the analyses. The  \nRESEARCH ARTICLE  \nEstimating person-specific neural correlates of mental rotation: A machine learning approach  \nSinan Uslu1 *, Michael Tangermann2, Claus V¨ogele1  \n1 Department of Behavioural and Cognitive Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg, 2 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands  \n* sinan. uslu@uni. lu  \nAbstract  \nUsing neurophysiological measures to model how the brain performs complex cognitive tasks such as mental rotation is a promising way towards precise predictions of behavioural responses. The mental rotation task requires objects to be mentally rotated in space. It has been used to monitor progressive neurological disorders. Up until now, research on neural correlates of mental rotation have largely focused on group analyses yielding models with features common across individuals. Here, we propose an individually tailored machine learning approach to identify person-specific patterns of neural activity during mental rotation. We trained ridge regressions to predict the reaction time of correct responses in a mental rotation task using task-related, electroencephalographic (EEG) activity of the same person. When tested on independent data of the same person, the regression model predicted the reaction times significantly more accurately than when only the average reaction time was used for prediction (bootstrap mean difference of 0 .02, 95% CI: 0.01–0.03, p \u003C.001) . When tested on another person’s data, the predictions were significantly less accurate compared to within-person predictions. Further analyses revealed that considering person-specific reaction times and topographical activity patterns substantially improved a model’s generalizability. Our results indicate that a more individualized approach towards neural correlates can improve their predictive performance of behavioural responses, particularly when combined with machine learning.  \nIntroduction  \nNeural correlates quantify the relationship between neurophysiological properties and behavioural variables. Many studies have investigated the neural underpinnings of mental rotation. The mental rotation task has frequently been used to invoke complex cognitive processes including visuospatial representations and visual working memory. It involves the judgement of rotational invariance based on objects rotated in space. Neuroscience techniques such as positron emission tomography (PET scan), functional magnetic resonance imaging (fMRI),  \nPLOS ONE | [https://doi.org/10.1371/journal.pon","cbCaibHx6EnndinB","https://ap.wps.com/l/cbCaibHx6EnndinB","pdf",1509492,1,19,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What neural and behavioral relationship does the paper focus on?\",\"answer\":\"It focuses on estimating neural correlates that quantify how neurophysiological properties relate to behavioral variables during the mental rotation task, especially reaction time.\"},{\"question\":\"How does the proposed approach differ from previous group-based models?\",\"answer\":\"Instead of relying on features common across individuals, it trains a machine learning model tailored to each person using that person’s task-related EEG to capture person-specific neural activity patterns.\"},{\"question\":\"What is the main finding about prediction accuracy?\",\"answer\":\"Within-person testing shows reaction times are predicted significantly more accurately than using only an average reaction time, while cross-person predictions are less accurate.\"}]","Estimating Person-Specific Neural Correlates of Mental Rotation - 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