<?xml version="1.0" encoding="Windows-31J"?>
<ArticleSet xmlns="http://www.openarchives.org/OAI/2.0/">
  <Article>
    <Journal>
      <PublisherName>Elsevier BV</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>0010-9452</Issn>
      <Volume>194</Volume>
      <Issue/>
      <PubDate PubStatus="ppublish">
        <Year>2026</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Increasing visual uncertainty modulates multisensory decision-making</ArticleTitle>
    <FirstPage LZero="delete">50</FirstPage>
    <LastPage>62</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Xiangfu</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Weiping</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Department of Psychology, Faculty of Education, Hubei University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yinghua</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>The brain integrates and transforms information from multiple senses to make optimal decisions, a process that is critical for navigating complex environments with perceptual uncertainty. Despite a growing consensus that individuals adapt flexibly to uncertain sensory input, whether increasing visual uncertainty influences the decision process itself or other, non-decision sensory processes during multisensory decision-making are unclear. Here, an audiovisual categorization task was used to examine the responses of human participants (N = 30) to visual and audiovisual stimuli under low-, medium-, and high-uncertainty conditions. Modeling the behavioral data using a drift&#8210;diffusion model indicated that increased visual uncertainty in the audiovisual context decreased the evidence accumulation rate but had no effect on non-decision processes. Electrophysiological recordings confirmed and expanded upon these results: increased visual uncertainty in the audiovisual context reduced the amplitude during the late decision-making stage (300&#8211;380 msec) but had no effect on the amplitude during the early sensory encoding stage (140&#8211;220 msec). More importantly, electroencephalography analyses revealed that audiovisual integration in the early sensory encoding stage occurred robustly across all visual uncertainty conditions, whereas audiovisual integration in the late stage occurred only under medium and high visual uncertainty conditions. This study demonstrated that increased visual uncertainty modulates the decision process itself rather than early sensory encoding during multisensory decision-making. Moreover, multisensory integration strategies dynamically adapt to increasing visual uncertainty by engaging different mechanisms to maintain effective decision-making.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Multisensory decision-making</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Visual uncertainty</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Audiovisual integration</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Event-related potential</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Drift&#8210;diffusion model</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>MDPI</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>2076-328X</Issn>
      <Volume>15</Volume>
      <Issue>1</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2025</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>The Impact of Task Context on Pleasantness and Softness Estimations: A Study Based on Three Touch Strategies</ArticleTitle>
    <FirstPage LZero="delete">63</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Binyue</FirstName>
        <LastName>Gao</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yinghua</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>This study investigated the two distinct perceptions (pleasantness and softness) of deformable stimuli with different degrees of compliance under conditions with and without a contextual task. Three tactile strategies-grasping, pinching, and pressing-were used to perceive the stimuli. In Experiment 1 (without a contextual task), participants estimated the perceived intensity of softness or pleasantness for each stimulus. In Experiment 2 (with a contextual task), the participants sequentially perceived two stimuli with different compliance levels and indicated which stimulus they perceived as softer and pleasant. The results showed that the psychophysical relationship between compliance and perceived softness was consistent across all tactile strategies in both experiments, with softness estimates increasing as compliance increased. However, the relationship between compliance and pleasantness differed between the two experiments. In Experiment 1, pleasantness estimates increased monotonically with increased compliance. However, in Experiment 2, across all tactile strategies, pleasantness began to decrease within the compliance range of 0.25-2.0 cm2/N, exhibiting an inverted U-shaped trend. These findings indicate that the relationship between compliance and pleasantness is task-dependent, particularly demonstrating significantly different trends when a contextual task is introduced. In contrast, the relationship between compliance and softness remained consistently monotonic.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">pleasantness</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">softness</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">touch strategy</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">task context</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">psychophysics</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>American Physiological Society</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>0022-3077</Issn>
      <Volume>127</Volume>
      <Issue>5</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2022</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Tactile angle discriminability improvement: contributions of working memory training and continuous attended sensory input</ArticleTitle>
    <FirstPage LZero="delete">1398</FirstPage>
    <LastPage>1406</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Wu</FirstName>
        <LastName>Wang</LastName>
        <Affiliation>School of Psychological and Cognitive Sciences, Peking University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yinghua</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Huazhi</FirstName>
        <LastName>Li</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yulong</FirstName>
        <LastName>Liu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yiyang</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiabin</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>College of Information Engineering, China Jiliang University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Xiaoyu</FirstName>
        <LastName>Tang</LastName>
        <Affiliation>School of Psychology, Liaoning Collaborative Innovation Center of Children and Adolescents Healthy Personality Assessment and Cultivation, Liaoning Normal University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jingjing</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>School of Computer Science and Technology, Changchun University of Science and Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Satoshi</FirstName>
        <LastName>Takahashi</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>Perceptual learning is commonly assumed to enhance perception through continuous attended sensory input. However, learning is generalizable to performance in untrained stimuli and tasks. Although previous studies have observed a possible generalization effect across tasks as a result of working memory (WM) training, comparisons of the contributions of WM training and continuous attended sensory input to perceptual learning generalization are still rare. Therefore, we compared which factors contributed most to perceptual generalization and investigated which skills acquired during WM training led to tactile generalization across tasks. Here, a Braille-like dot pattern matching n-back WM task was used as the WM training task, with four workload levels (0, 1, 2, and 3-back levels). A tactile angle discrimination (TAD) task was used as a pre- and posttest to assess improvements in tactile perception. Between tests, four subject groups were randomly assigned to four different workload n-back tasks to consecutively complete three sessions of training. The results showed that tactile n-back WM training could enhance TAD performance, with the 3-back training group having the highest TAD threshold improvement rate. Furthermore, the rate of WM capacity improvement on the 3-back level across training sessions was correlated with the rate of TAD threshold improvement. These findings suggest that continuous attended sensory input and enhanced WM capacity can lead to improvements in TAD ability, and that greater improvements in WM capacity can predict greater improvements in TAD performance.&lt;br&gt;
NEW &amp; NOTEWORTHY Perceptual learning is not always specific to the trained task and stimuli. We demonstrate that both continuous attended sensory input and improved WM capacity can be used to enhance tactile angle discrimination (TAD) ability. Moreover, WM capacity improvement is important in generalizing the training effect to the TAD ability. These findings contribute to understanding the mechanism of perceptual learning generalization across tasks.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">continuous attended sensory input</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">perceptual learning</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">tactile angle discriminability</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">tactile generalization</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">working memory training</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>Springer Science and Business Media LLC</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>0014-4819</Issn>
      <Volume>240</Volume>
      <Issue>3</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2022</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Global surface features contribute to human haptic roughness estimations</ArticleTitle>
    <FirstPage LZero="delete">773</FirstPage>
    <LastPage>789</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Huazhi</FirstName>
        <LastName>Li</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yinghua</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Wu</FirstName>
        <LastName>Wang</LastName>
        <Affiliation>School of Psychological and Cognitive Sciences, Peking University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yulong</FirstName>
        <LastName>Liu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Mengni</FirstName>
        <LastName>Zhou</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Qingqing</FirstName>
        <LastName>Li</LastName>
        <Affiliation>Department of Teacher Education, Wenzhou University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jingjing</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>School of Computer Science and Technology, Changchun University of Science and Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Shiping</FirstName>
        <LastName>Shao</LastName>
        <Affiliation>School of Social Welfare, Yonsei University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Satoshi</FirstName>
        <LastName>Takahashi</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>Previous studies have paid special attention to the relationship between local features (e.g., raised dots) and human roughness perception. However, the relationship between global features (e.g., curved surface) and haptic roughness perception is still unclear. In the present study, a series of roughness estimation experiments was performed to investigate how global features affect human roughness perception. In each experiment, participants were asked to estimate the roughness of a series of haptic stimuli that combined local features (raised dots) and global features (sinusoidal-like curves). Experiments were designed to reveal whether global features changed their haptic roughness estimation. Furthermore, the present study tested whether the exploration method (direct, indirect, and static) changed haptic roughness estimations and examined the contribution of global features to roughness estimations. The results showed that sinusoidal-like curved surfaces with small periods were perceived to be rougher than those with large periods, while the direction of finger movement and indirect exploration did not change this phenomenon. Furthermore, the influence of global features on roughness was modulated by local features, regardless of whether raised-dot surfaces or smooth surfaces were used. Taken together, these findings suggested that an objectfs global features contribute to haptic roughness perceptions, while local features change the weight of the contribution that global features make to haptic roughness perceptions.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Haptic roughness perception</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Raised-dot surface</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Local feature</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Global feature</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>Academic Press Inc. Elsevier Science</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>1053-8119</Issn>
      <Volume>248</Volume>
      <Issue/>
      <PubDate PubStatus="ppublish">
        <Year>2022</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing</ArticleTitle>
    <FirstPage LZero="delete">118867</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Yinghua</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Laurentius</FirstName>
        <LastName>Huber</LastName>
        <Affiliation>MR-Methods Group, MBIC, Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, University of Maastricht, Cognitive Neuroscience</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Masaki</FirstName>
        <LastName>Fukunaga</LastName>
        <Affiliation>Division of Cerebral Research, National Institute for Physiological Sciences</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yuhui</FirstName>
        <LastName>Chai</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">David C.</FirstName>
        <LastName>Jangraw</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Gang</FirstName>
        <LastName>Chen</LastName>
        <Affiliation>Scientific and Statistical Computational Core, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Daniel A.</FirstName>
        <LastName>Handwerker</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Peter J.</FirstName>
        <LastName>Molfese</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Norihiro</FirstName>
        <LastName>Sadato</LastName>
        <Affiliation>Division of Cerebral Research, National Institute for Physiological Sciences</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Peter A.</FirstName>
        <LastName>Bandettini</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>The human brain continuously generates predictions of incoming sensory input and calculates corresponding prediction errors from the perceived inputs to update internal predictions. In human primary somatosensory cortex (area 3b), different cortical layers are involved in receiving the sensory input and generation of error signals. It remains unknown, however, how the layers in the human area 3b contribute to the temporal prediction error processing. To investigate prediction error representation in the area 3b across layers, we acquired layer specific functional magnetic resonance imaging (fMRI) data at 7T from human area 3b during a task of index finger poking with no-delay, short-delay and long-delay touching sequences. We demonstrate that all three tasks increased activity in both superficial and deep layers of area 3b compared to the random sensory input. The fMRI signal was differentially modulated solely in the deep layers rather than the superficial layers of area 3b by the delay time. Compared with the no-delay stimuli, activity was greater in the deep layers of area 3b during the short delay stimuli but lower during the long-delay stimuli. This difference activity features in the superficial and deep layers suggest distinct functional contributions of area 3b layers to tactile temporal prediction error processing. The functional segregation in area 3b across layers may reflect that the excitatory and inhibitory interplay in the sensory cortex contributions to flexible communication between cortical layers or between cortical areas.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Layer-specific fMRI</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Tactile prediction</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Primary somatosensory cortex</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Temporal prediction error</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">High-resolution CBV-fMRI</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>MDPI</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>2076-3417</Issn>
      <Volume>11</Volume>
      <Issue>15</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2021</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>A New Method for Haptic Shape Discriminability Detection</ArticleTitle>
    <FirstPage LZero="delete">7049</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Yulong</FirstName>
        <LastName>Liu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yinghua</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yiyang</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Wu</FirstName>
        <LastName>Wang</LastName>
        <Affiliation>The School of Psychological and Cognitive Sciences, Peking University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Huazhi</FirstName>
        <LastName>Li</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Satoshi</FirstName>
        <LastName>Takahashi</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Qiong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Department of Psychology, Suzhou University of Science and Technology,</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>Touch shape discrimination is not only closely related to tactile mechanoreceptors but also higher cognitive function. However, previous shape discrimination methods are difficult to complete in a short time, and the devices are complicated to operate and not user-friendly for nonprofessionals. Here, we propose a new method, the evaluation quantity of which is the angle discrimination threshold. In addition, to make this method easy to use for nonprofessionals, we designed a haptic angle sorting system, including the device and software. To evaluate this method, the angle sorting and two-angle discrimination experiments were compared, and it was found that participants spent significantly less time in the former experiment than in the latter. At the same time, there is a strong correlation between the performance of angle sorting and two-angle discrimination, which shows that the angle threshold obtained by the new method can also be used to evaluate the ability of touch discrimination. Moreover, the angle sorting results of different age groups also further demonstrate the feasibility of the method. The efficiency of this new method and the effectiveness of the system also provide a convenient means for evaluating haptic shape discrimination, which may have potential clinical application value in the early diagnosis of peripheral neuropathy and even in the evaluation of cognitive function.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">haptic angle discrimination</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">angle sort</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">discrimination threshold</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">haptic device</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">human haptics</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>Academic Press Inc Elsevier Science</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>1053-8119</Issn>
      <Volume>231</Volume>
      <Issue/>
      <PubDate PubStatus="ppublish">
        <Year>2021</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Different activation signatures in the primary sensorimotor and higher-level regions for haptic three-dimensional curved surface exploration</ArticleTitle>
    <FirstPage LZero="delete">117754</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Peter J.</FirstName>
        <LastName>Molfese</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yinghua</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Daniel A.</FirstName>
        <LastName>Handwerker</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Gang</FirstName>
        <LastName>Chen</LastName>
        <Affiliation>Scientific and Statistical Computational Core, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Paul A.</FirstName>
        <LastName>Taylor</LastName>
        <Affiliation>Scientific and Statistical Computational Core, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Peter A.</FirstName>
        <LastName>Bandettini</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>Haptic object perception begins with continuous exploratory contact, and the human brain needs to accumulate sensory information continuously over time. However, it is still unclear how the primary sensorimotor cortex (PSC) interacts with these higher-level regions during haptic exploration over time. This functional magnetic resonance imaging (fMRI) study investigates time-dependent haptic object processing by examining brain activity during haptic 3D curve and roughness estimations. For this experiment, we designed sixteen haptic stimuli (4 kinds of curves x 4 varieties of roughness) for the haptic curve and roughness estimation tasks. Twenty participants were asked to move their right index and middle fingers along the surface twice and to estimate one of the two features -roughness or curvature -depending on the task instruction. We found that the brain activity in several higher-level regions (e.g., the bilateral posterior parietal cortex) linearly increased as the number of curves increased during the haptic exploration phase. Surprisingly, we found that the contralateral PSC was parametrically modulated by the number of curves only during the late exploration phase but not during the early exploration phase. In contrast, we found no similar parametric modulation activity patterns during the haptic roughness estimation task in either the contralateral PSC or in higher-level regions. Thus, our findings suggest that haptic 3D object perception is processed across the cortical hierarchy, whereas the contralateral PSC interacts with other higher-level regions across time in a manner that is dependent upon the features of the object.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Haptic object perception</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Primary somatosensory cortex</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Primary motor cortex</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">fMRI</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Parametric modulation</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Cortical hierarchy</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>SAGE Publications</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>2041-6695</Issn>
      <Volume>11</Volume>
      <Issue>6</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2020</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Semantic Congruency Modulates the Effect of Attentional Load on the Audiovisual Integration of Animate Images and Sounds</ArticleTitle>
    <FirstPage LZero="delete">2041669520981096</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Qingqing</FirstName>
        <LastName>Li</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Qiong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Department of Psychology, Suzhou University of Science and Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yiyang</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Fengxia</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Satoshi</FirstName>
        <LastName>Takahashi</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>Attentional processes play a complex and multifaceted role in the integration of input from different sensory modalities. However, whether increased attentional load disrupts the audiovisual (AV) integration of common objects that involve semantic content remains unclear. Furthermore, knowledge regarding how semantic congruency interacts with attentional load to influence the AV integration of common objects is limited. We investigated these questions by examining AV integration under various attentional-load conditions. AV integration was assessed by adopting an animal identification task using unisensory (animal images and sounds) and AV stimuli (semantically congruent AV objects and semantically incongruent AV objects), while attentional load was manipulated by using a rapid serial visual presentation task. Our results indicate that attentional load did not attenuate the integration of semantically congruent AV objects. However, semantically incongruent animal sounds and images were not integrated (as there was no multisensory facilitation), and the interference effect produced by the semantically incongruent AV objects was reduced by increased attentional-load manipulations. These findings highlight the critical role of semantic congruency in modulating the effect of attentional load on the AV integration of common objects.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">audiovisual integration</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">common object</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">attentional load</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">semantic congruency</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">dual-task paradigm</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>Frontiers Media</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>1662-5196</Issn>
      <Volume>14</Volume>
      <Issue/>
      <PubDate PubStatus="ppublish">
        <Year>2021</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Stimulus Intervals Modulate the Balance of Brain Activity in the Human Primary Somatosensory Cortex: An ERP Study</ArticleTitle>
    <FirstPage LZero="delete">571369</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Yang</FirstName>
        <LastName>Liu</LastName>
        <Affiliation>Department of Psychology, Suzhou University of Science and Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Bo</FirstName>
        <LastName>Dong</LastName>
        <Affiliation>Department of Psychology, Suzhou University of Science and Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Qiong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Ming</FirstName>
        <LastName>Zhang</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>Neuronal excitation and inhibition occur in the brain at the same time, and brain activation reflects changes in the sum of excitation and inhibition. This principle has been well-established in lower-level sensory systems, including vision and touch, based on animal studies. However, it is unclear how the somatosensory system processes the balance between excitation and inhibition. In the present ERP study, we modified the traditional spatial attention paradigm by adding double stimuli presentations at short intervals (i.e., 10, 30, and 100 ms). Seventeen subjects participated in the experiment. Five types of stimulation were used in the experiment: a single stimulus (one raised pin for 40 ms), standard stimulus (eight pins for 40 ms), and double stimuli presented at intervals of 10, 30, and 100 ms. The subjects were asked to attend to a particular finger and detect whether the standard stimulus was presented to that finger. The results showed a clear attention-related ERP component in the single stimulus condition, but the suppression components associated with the three interval conditions seemed to be dominant in somatosensory areas. In particular, we found the strongest suppression effect in the ISI-30 condition (interval of 30 ms) and that the suppression and enhancement effects seemed to be counterbalanced in both the ISI-10 and ISI-100 conditions (intervals of 10 and 100 ms, respectively). This type of processing may allow humans to easily discriminate between multiple stimuli on the same body part.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">traditional spatial attention paradigm</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">ERP</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">interstimulus interval</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">enhancement and suppression</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">primary somatosensory cortex</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>Wiley</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>2162-3279</Issn>
      <Volume>11</Volume>
      <Issue>3</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2021</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Functional heterogeneity in the left lateral posterior parietal cortex during visual and haptic crossmodal dot-surface matching</ArticleTitle>
    <FirstPage LZero="delete">e02033</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yinghua</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Hiroaki</FirstName>
        <LastName>Shigemasu</LastName>
        <Affiliation>Kochi University of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Hiroshi</FirstName>
        <LastName>Kadota</LastName>
        <Affiliation>Kochi University of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Kiyoshi</FirstName>
        <LastName>Nakahara</LastName>
        <Affiliation>Kochi University of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Takanori</FirstName>
        <LastName>Kochiyama</LastName>
        <Affiliation>ATR Brain Activity Imaging Center</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>Background&lt;/br&gt;
Vision and touch are thought to contribute information to object perception in an independent but complementary manner. The left lateral posterior parietal cortex (LPPC) has long been associated with multisensory information processing, and it plays an important role in visual and haptic crossmodal information retrieval. However, it remains unclear how LPPC subregions are involved in visuo]haptic crossmodal retrieval processing.&lt;/br&gt;
Methods&lt;/br&gt;
In the present study, we used an fMRI experiment with a crossmodal delayed match]to]sample paradigm to reveal the functional role of LPPC subregions related to unimodal and crossmodal dot]surface retrieval.&lt;/br&gt;
Results&lt;/br&gt;
The visual]to]haptic condition enhanced the activity of the left inferior parietal lobule relative to the haptic unimodal condition, whereas the inverse condition enhanced the activity of the left superior parietal lobule. By contrast, activation of the left intraparietal sulcus did not differ significantly between the crossmodal and unimodal conditions. Seed]based resting connectivity analysis revealed that these three left LPPC subregions engaged distinct networks, confirming their different functions in crossmodal retrieval processing.&lt;/br&gt;
Conclusion&lt;/br&gt;
Taken together, the findings suggest that functional heterogeneity of the left LPPC during visuo]haptic crossmodal dot]surface retrieval processing reflects that the left LPPC does not simply contribute to retrieval of past information; rather, each subregion has a specific functional role in resolving different task requirements.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">crossmodal processing</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">fMRI</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">haptic dot-surface matching</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">lateral posterior parietal cortex</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">memory retrieval</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>American Physiological Society</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>0022-3077</Issn>
      <Volume>122</Volume>
      <Issue>5</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2019</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Tactile angle discriminability improvement: roles of training time intervals and different types of training tasks</ArticleTitle>
    <FirstPage LZero="delete">1918</FirstPage>
    <LastPage>1927</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Wu</FirstName>
        <LastName>Wang</LastName>
        <Affiliation>Graduate School of Natural Science and Technology, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Qiong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiabin</FirstName>
        <LastName>Yu</LastName>
        <Affiliation> Graduate School of Natural Science and Technology, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Satoshi</FirstName>
        <LastName>Takahashi</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>Perceptual learning, which is not limited to sensory modalities such as vision and touch, emerges within a training session and between training sessions and is accompanied by the remodeling of neural connections in the cortex. However, limited knowledge exists regarding perceptual learning between training sessions. Although tactile studies have paid attention to between-session learning effects, there have been few studies asking fundamental questions regarding whether the time interval between training sessions affects tactile perceptual learning and generalization across tactile tasks. We investigated the effects of different training time intervals on the consecutive performance of a tactile angle discrimination (AD) task and a tactile orientation discrimination (OD) task training on tactile angle discriminability. The results indicated that in the short-interval training group, AD task performance significantly improved in the early stage of learning and nearly plateaued in the later stage, whereas in the long-interval training group, significant improvement was delayed and then also nearly plateaued in the later stage; additionally, improved OD task performance resulted in improved AD task performance. These findings suggest that training time interval affects the early stage of learning but not the later stage and that generalization occurs between different types of tactile tasks.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">between-session learning</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">generalization</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">tactile angle discriminability</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">training time interval</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>American Association for the Advancement of Science</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>2375-2548</Issn>
      <Volume>5</Volume>
      <Issue>5</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2019</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Layer-specific activation of sensory input and predictive feedback in the human primary somatosensory cortex</ArticleTitle>
    <FirstPage LZero="delete">eaav9053</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Yinghua</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems,Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Laurentius</FirstName>
        <LastName>Huber</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems,Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">David C.</FirstName>
        <LastName>Jangraw</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Daniel A.</FirstName>
        <LastName>Handwerker</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Peter J.</FirstName>
        <LastName>Molfese</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Gang</FirstName>
        <LastName>Chen</LastName>
        <Affiliation>Scientific and Statistical Computing Core, National Institute of Mental Health</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems,Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems,Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Peter A.</FirstName>
        <LastName>Bandettini</LastName>
        <Affiliation>Section on Functional Imaging Methods, National Institute of Mental Health</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>When humans perceive a sensation, their brains integrate inputs from sensory receptors and process them based on their expectations. The mechanisms of this predictive coding in the human somatosensory system are not fully understood. We fill a basic gap in our understanding of the predictive processing of somatosensation by examining the layer-specific activity in sensory input and predictive feedback in the human primary somatosensory cortex (S1). We acquired submillimeter functional magnetic resonance imaging data at 7T (n = 10) during a task of perceived, predictable, and unpredictable touching sequences. We demonstrate that the sensory input from thalamic projects preferentially activates the middle layer, while the superficial and deep layers in S1 are more engaged for cortico-cortical predictive feedback input. These findings are pivotal to understanding the mechanisms of tactile prediction processing in the human somatosensory cortex.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList/>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>Elsevier</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>2405-8440</Issn>
      <Volume>5</Volume>
      <Issue>8</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2019</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Stroking hardness changes the perception of affective touch pleasantness across different skin sites</ArticleTitle>
    <FirstPage LZero="delete">e02141</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Jiabin</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yinghua</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Qiong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Satoshi</FirstName>
        <LastName>Takahashi</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>Human unmyelinated tactile afferents (CT afferents) in hairy skin are thought to be involved in the transmission of affective aspects of touch. How the perception of affective touch differs across human skin has made substantial progress; however, the majority of previous studies have mainly focused on the relationship between stroking velocities and pleasantness ratings. Here, we investigate how stroking hardness affects the perception of affective touch. Affective tactile stimulation was given with four different hardness of brushes a three different forces, which were presented to either palm or forearm. To quantify the physical factors of the stimuli (brush hardness), ten naive, healthy participants assessed brush hardness using a seven-point scale. Based on these ten participants, five more participants were added to rate the hedonic value of brush stroking using a visual analogue scale (VAS). We found that pleasantness ratings over the skin resulted in a preference for light, soft stroking, which was rated as more pleasant when compared to heavy, hard stroking. Our results show that the hairy skin of the forearm is more susceptible to stroking hardness than the glabrous of the palm in terms of the perception of pleasantness. These findings of the current study extend the growing literature related to the effect of stroking characteristics on pleasantness ratings.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Neuroscience</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Pleasantness ratings</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Affective tactile</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Physical factors</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">CT afferents</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Stroking hardness</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>Elsevier</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>24058440</Issn>
      <Volume>5</Volume>
      <Issue>8</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2019</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Stroking hardness changes the perception of affective touch pleasantness across different skin sites</ArticleTitle>
    <FirstPage LZero="delete">e02141</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Jiabin</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yinghua</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Qiong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Satoshi</FirstName>
        <LastName>Takahashi</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yoshimichi</FirstName>
        <LastName>Ejima</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>Human unmyelinated tactile afferents (CT afferents) in hairy skin are thought to be involved in the transmission of affective aspects of touch. How the perception of affective touch differs across human skin has made substantial progress; however, the majority of previous studies have mainly focused on the relationship between stroking velocities and pleasantness ratings. Here, we investigate how stroking hardness affects the perception of affective touch. Affective tactile stimulation was given with four different hardness of brushes at three different forces, which were presented to either palm or forearm. To quantify the physical factors of the stimuli (brush hardness), ten na&#239;ve, healthy participants assessed brush hardness using a seven-point scale. Based on these ten participants, five more participants were added to rate the hedonic value of brush stroking using a visual analogue scale (VAS). We found that pleasantness ratings over the skin resulted in a preference for light, soft stroking, which was rated as more pleasant when compared to heavy, hard stroking. Our results show that the hairy skin of the forearm is more susceptible to stroking hardness than the glabrous of the palm in terms of the perception of pleasantness. These findings of the current study extend the growing literature related to the effect of stroking characteristics on pleasantness ratings.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Affective tactile</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">CT afferents; Neuroscience</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Physical factors</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Pleasantness ratings</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Stroking hardness.</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
</ArticleSet>
