Home >

news ヘルプ

論文・著書情報


タイトル
和文: 
英文:Decoding Semantics across fMRI sessions with Different Stimulus Modalities: A practical MVPA Study 
著者
和文: 赤間啓之.  
英文: hiroyuki akama.  
言語 English 
掲載誌/書名
和文: 
英文:Frontiers in Neuroinformatics 
巻, 号, ページ         pp. 1-10
出版年月 2012年8月24日 
出版者
和文: 
英文:Frontiers in Neuroinformatics 
会議名称
和文: 
英文: 
開催地
和文: 
英文: 
ファイル
公式リンク http://www.frontiersin.org/Journal/Abstract.aspx?s=752&name=neuroinformatics&ART_DOI=10.3389/fninf.2012.00024
 
DOI https://doi.org/10.3389/fninf.2012.00024
アブストラクト Both embodied and symbolic accounts of conceptual organization would predict partial sharing and partial differentiation between the neural activations seen for concepts activated via different stimulus modalities. But cross-participant and cross-session variability in BOLD activity patterns makes analyses of such patterns with MVPA methods challenging. Here, we examine the effect of cross-modal and individual variation on the machine learning analysis of fMRI data recorded during a word property generation task. We present the same set of living and non-living concepts (land-mammals, or work tools) to a cohort of Japanese participants in two sessions: the first using auditory presentation of spoken words; the second using visual presentation of words written in Japanese characters. Classification accuracies confirmed that these semantic categories could be detected in single trials, with within-session predictive accuracies of 80–90%. However cross-session prediction (learning from auditory-task data to classify data from the written-word-task, or vice versa) suffered from a performance penalty, achieving 65–75% (still individually significant at p « 0.05). We carried out several follow-on analyses to investigate the reason for this shortfall, concluding that distributional differences in neither time nor space alone could account for it. Rather, combined spatio-temporal patterns of activity need to be identified for successful cross-session learning, and this suggests that feature selection strategies could be modified to take advantage of this.

©2007 Tokyo Institute of Technology All rights reserved.