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  <Article>
    <Journal>
      <PublisherName>MDPI AG</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>2076-3425</Issn>
      <Volume>15</Volume>
      <Issue>11</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2025</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Sensory Modality-Dependent Interplay Between Updating and Inhibition Under Increased Working Memory Load: An ERP Study</ArticleTitle>
    <FirstPage LZero="delete">1178</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Yuxi</FirstName>
        <LastName>Luo</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Ao</FirstName>
        <LastName>Guo</LastName>
        <Affiliation>Department of Psychology, Institute of Education, China West Normal University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Faculty of Biomedical Engineering, Shenzhen University of Advanced Technology</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>
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    <Abstract>Background/Objectives: Working memory (WM) performance relies on the coordination of updating and inhibition functions within the central executive system. However, their interaction under varying cognitive loads, particularly across sensory modalities, remains unclear. Methods: This study examined how sensory modality modulates flanker interference under increasing WM loads. Twenty-two participants performed a visual n-back task at three load levels (1-, 2-, and 3-back) while ignoring visual (within-modality) or auditory (cross-modality) flankers. Results: Behaviorally, increased WM load (2- and 3-back) led to reduced accuracy (AC) and prolonged reaction times (RTs) in both conditions. In addition, flanker interference was observed under the 2-back condition in both the visual within-modality (VM) and audiovisual cross-modality (AVM) tasks. However, performance impairment emerged at a lower load (2-back) in the VM condition, whereas in the AVM condition, it only emerged at the highest load (3-back). Significant performance impairment in the AVM condition occurred at higher WM loads, suggesting that greater WM load is required to trigger interference. Event-related potential (ERP) results showed that N200 amplitudes increased significantly for incongruent flankers under the highest WM load (3-back) in the visual within-modality condition, reflecting greater inhibitory demands. In the cross-modality condition, enhanced N200 was not observed across all loads and even reversed at low load (1-back). Moreover, the results also showed that P300 amplitude increased with load in the within-modality condition but decreased in the cross-modality condition. Conclusions: These results demonstrated that the interaction between updating and inhibition is shaped by both WM load and sensory modality, further supporting a sensory modality-specific resource allocation mechanism. The cross-modality configurations may enable more efficient distribution of cognitive resources under high load, reducing interference between concurrent executive demands.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
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        <Param Name="value">attentional resource allocation</Param>
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        <Param Name="value">modality-specific interference</Param>
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      <Object Type="keyword">
        <Param Name="value">inhibitory control</Param>
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      <Object Type="keyword">
        <Param Name="value">executive function</Param>
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      <Object Type="keyword">
        <Param Name="value">sensory modality</Param>
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  </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>
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    <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>
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  </Article>
  <Article>
    <Journal>
      <PublisherName>MDPI</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>2076-3425</Issn>
      <Volume>14</Volume>
      <Issue>12</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2024</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>The Impact of Selective Spatial Attention on Auditory-Tactile Integration: An Event-Related Potential Study</ArticleTitle>
    <FirstPage LZero="delete">1258</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Weichao</FirstName>
        <LastName>An</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Nan</FirstName>
        <LastName>Zhang</LastName>
        <Affiliation>Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Shengnan</FirstName>
        <LastName>Li</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">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>
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    <Abstract>Background: Auditory-tactile integration is an important research area in multisensory integration. Especially in special environments (e.g., traffic noise and complex work environments), auditory-tactile integration is crucial for human response and decision making. We investigated the influence of attention on the temporal course and spatial distribution of auditory-tactile integration. Methods: Participants received auditory stimuli alone, tactile stimuli alone, and simultaneous auditory and tactile stimuli, which were randomly presented on the left or right side. For each block, participants attended to all stimuli on the designated side and detected uncommon target stimuli while ignoring all stimuli on the other side. Event-related potentials (ERPs) were recorded via 64 scalp electrodes. Integration was quantified by comparing the response to the combined stimulus to the sum of the responses to the auditory and tactile stimuli presented separately. Results: The results demonstrated that compared to the unattended condition, integration occurred earlier and involved more brain regions in the attended condition when the stimulus was presented in the left hemispace. The unattended condition involved a more extensive range of brain regions and occurred earlier than the attended condition when the stimulus was presented in the right hemispace. Conclusions: Attention can modulate auditory-tactile integration and show systematic differences between the left and right hemispaces. These findings contribute to the understanding of the mechanisms of auditory-tactile information processing in the human brain.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
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        <Param Name="value">auditory-tactile integration</Param>
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      <Object Type="keyword">
        <Param Name="value">selective spatial attention</Param>
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      <Object Type="keyword">
        <Param Name="value">event-related potential</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">left-right hemispace differences</Param>
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      <Object Type="keyword">
        <Param Name="value">spatiotemporal distribution</Param>
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  </Article>
  <Article>
    <Journal>
      <PublisherName>MDPI</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>2076-3425</Issn>
      <Volume>14</Volume>
      <Issue>5</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2024</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Go/No-Go Ratios Modulate Inhibition-Related Brain Activity: An Event-Related Potential Study</ArticleTitle>
    <FirstPage LZero="delete">414</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Nan</FirstName>
        <LastName>Zhang</LastName>
        <Affiliation>Graduate of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Weichao</FirstName>
        <LastName>An</LastName>
        <Affiliation>Graduate of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yinghua</FirstName>
        <LastName>Yu</LastName>
        <Affiliation>Graduate of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Graduate of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jiajia</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Graduate of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
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      <ArticleId IdType="doi"/>
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    <Abstract>(1) Background: Response inhibition refers to the conscious ability to suppress behavioral responses, which is crucial for effective cognitive control. Currently, research on response inhibition remains controversial, and the neurobiological mechanisms associated with response inhibition are still being explored. The Go/No-Go task is a widely used paradigm that can be used to effectively assess response inhibition capability. While many studies have utilized equal numbers of Go and No-Go trials, how different ratios affect response inhibition remains unknown; (2) Methods: This study investigated the impact of different ratios of Go and No-Go conditions on response inhibition using the Go/No-Go task combined with event-related potential (ERP) techniques; (3) Results: The results showed that as the proportion of Go trials decreased, behavioral performance in Go trials significantly improved in terms of response time, while error rates in No-Go trials gradually decreased. Additionally, the NoGo-P3 component at the central average electrodes (Cz, C1, C2, FCz, FC1, FC2, PCz, PC1, and PC2) exhibited reduced amplitude and latency; (4) Conclusions: These findings indicate that different ratios in Go/No-Go tasks influence response inhibition, with the brain adjusting processing capabilities and rates for response inhibition. This effect may be related to the brain's predictive mechanism model.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
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        <Param Name="value">go/no-go task</Param>
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      <Object Type="keyword">
        <Param Name="value">ERP</Param>
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      <Object Type="keyword">
        <Param Name="value">NoGo-P3 component</Param>
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  </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>
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    <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>
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        <Param Name="value">continuous attended sensory input</Param>
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      <Object Type="keyword">
        <Param Name="value">perceptual learning</Param>
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      <Object Type="keyword">
        <Param Name="value">tactile angle discriminability</Param>
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      <Object Type="keyword">
        <Param Name="value">tactile generalization</Param>
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      <Object Type="keyword">
        <Param Name="value">working memory training</Param>
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    <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/>
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    <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>
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        <Param Name="value">Haptic roughness perception</Param>
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      <Object Type="keyword">
        <Param Name="value">Raised-dot surface</Param>
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      <Object Type="keyword">
        <Param Name="value">Local feature</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Global feature</Param>
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  </Article>
  <Article>
    <Journal>
      <PublisherName>MDPI</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>2076-3425</Issn>
      <Volume>13</Volume>
      <Issue>7</Issue>
      <PubDate PubStatus="ppublish">
        <Year>2023</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Audiovisual n-Back Training Alters the Neural Processes of Working Memory and Audiovisual Integration: Evidence of Changes in ERPs</ArticleTitle>
    <FirstPage LZero="delete">992</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Ao</FirstName>
        <LastName>Guo</LastName>
        <Affiliation>Cognitive Neuroscience Laboratory, 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">Xiangfu</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>Department of Psychology, Faculty of Education, Hubei University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinfei</FirstName>
        <LastName>Lin</LastName>
        <Affiliation>Department of Psychology, Faculty of Education, Hubei University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Zimo</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">Yanna</FirstName>
        <LastName>Ren</LastName>
        <Affiliation>Department of Psychology, College of Humanities and Management, Guizhou University of Traditional Chinese Medicine</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>(1) Background: This study investigates whether audiovisual n-back training leads to training effects on working memory and transfer effects on perceptual processing. (2) Methods: Before and after training, the participants were tested using the audiovisual n-back task (1-, 2-, or 3-back), to detect training effects, and the audiovisual discrimination task, to detect transfer effects. (3) Results: For the training effect, the behavioral results show that training leads to greater accuracy and faster response times. Stronger training gains in accuracy and response time using 3- and 2-back tasks, compared to 1-back, were observed in the training group. Event-related potentials (ERPs) data revealed an enhancement of P300 in the frontal and central regions across all working memory levels after training. Training also led to the enhancement of N200 in the central region in the 3-back condition. For the transfer effect, greater audiovisual integration in the frontal and central regions during the post-test rather than pre-test was observed at an early stage (80-120 ms) in the training group. (4) Conclusion: Our findings provide evidence that audiovisual n-back training enhances neural processes underlying a working memory and demonstrate a positive influence of higher cognitive functions on lower cognitive functions.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">audiovisual n-back</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">training</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">audiovisual integration</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">ERPs</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">training effect</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">transfer effect</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>Frontiers Media</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>1662-5196</Issn>
      <Volume>14</Volume>
      <Issue/>
      <PubDate PubStatus="ppublish">
        <Year>2020</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Development of a Non-invasive Deep Brain Stimulator With Precise Positioning and Real-Time Monitoring of Bioimpedance</ArticleTitle>
    <FirstPage LZero="delete">574189</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Heng</FirstName>
        <LastName>Wang</LastName>
        <Affiliation>School of Mechatronic Engineering, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Zhongyan</FirstName>
        <LastName>Shi</LastName>
        <Affiliation>School of Life Science, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Weiqian</FirstName>
        <LastName>Sun</LastName>
        <Affiliation>School of Life Science, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jianxu</FirstName>
        <LastName>Zhang</LastName>
        <Affiliation>School of Mechatronic Engineering, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jing</FirstName>
        <LastName>Wang</LastName>
        <Affiliation>Department of Health Management, Aerospace Center Hospital, Peking University Aerospace School of Clinical Medicine</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yue</FirstName>
        <LastName>Shi</LastName>
        <Affiliation>Beijing Big-IQ Medical Equipment Co., Ltd.</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Ruoshui</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>School of Mechatronic Engineering, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Chunlin</FirstName>
        <LastName>Li</LastName>
        <Affiliation>School of Biomedical Engineering, Capital Medical University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Duanduan</FirstName>
        <LastName>Chen</LastName>
        <Affiliation>School of Life Science, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>School of Interdisciplinary Science and Engineering in Health Systems, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Guo</FirstName>
        <LastName>Gongyao</LastName>
        <Affiliation>School of Life Science, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yifei</FirstName>
        <LastName>Xu</LastName>
        <Affiliation>School of Life Science, Beijing Institute of Technology</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>Methods by which to achieve non-invasive deep brain stimulation via temporally interfering with electric fields have been proposed, but the precision of the positioning of the stimulation and the reliability and stability of the outputs require improvement. In this study, a temporally interfering electrical stimulator was developed based on a neuromodulation technique using the interference modulation waveform produced by several high-frequency electrical stimuli to treat neurodegenerative diseases. The device and auxiliary software constitute a non-invasive neuromodulation system. The technical problems related to the multichannel high-precision output of the device were solved by an analog phase accumulator and a special driving circuit to reduce crosstalk. The function of measuring bioimpedance in real time was integrated into the stimulator to improve effectiveness. Finite element simulation and phantom measurements were performed to find the functional relations among the target coordinates, current ratio, and electrode position in the simplified model. Then, an appropriate approach was proposed to find electrode configurations for desired target locations in a detailed and realistic mouse model. A mouse validation experiment was carried out under the guidance of a simulation, and the reliability and positioning accuracy of temporally interfering electric stimulators were verified. Stimulator improvement and precision positioning solutions promise opportunities for further studies of temporally interfering electrical stimulation.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">electrical stimulation</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">temporally interfering</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">finite element method</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">simulation</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">mouse</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
  <Article>
    <Journal>
      <PublisherName>Frontiers Media</PublisherName>
      <JournalTitle>Acta Medica Okayama</JournalTitle>
      <Issn>1663-4365</Issn>
      <Volume>12</Volume>
      <Issue/>
      <PubDate PubStatus="ppublish">
        <Year>2020</Year>
        <Month/>
      </PubDate>
    </Journal>
    <ArticleTitle>Enhancing Working Memory Based on Mismatch Negativity Neurofeedback in Subjective Cognitive Decline Patients: A Preliminary Study</ArticleTitle>
    <FirstPage LZero="delete">263</FirstPage>
    <LastPage/>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="N">Guangying</FirstName>
        <LastName>Pei</LastName>
        <Affiliation>School of Life Science, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Ruoshui</FirstName>
        <LastName>Yang</LastName>
        <Affiliation>School of Mechatronical Engineering, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Zhongyan</FirstName>
        <LastName>Shi</LastName>
        <Affiliation>School of Life Science, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Guoxin</FirstName>
        <LastName>Guo</LastName>
        <Affiliation>School of Life Science, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Shujie</FirstName>
        <LastName>Wang</LastName>
        <Affiliation>School of Life Science, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Miaomiao</FirstName>
        <LastName>Liu</LastName>
        <Affiliation>Graduate School of Natural Science and Technology, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yuxiang</FirstName>
        <LastName>Qiu</LastName>
        <Affiliation>School of Life Science, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Jinglong</FirstName>
        <LastName>Wu</LastName>
        <Affiliation>Faculty of Engineering, Okayama University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Ritsu</FirstName>
        <LastName>Go</LastName>
        <Affiliation>School of Mechatronical Engineering, Beijing Institute of Technology</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Yin</FirstName>
        <LastName>Han</LastName>
        <Affiliation>Department of Neurology, Xuanwu Hospital, Capital Medical University</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="N">Tianyi</FirstName>
        <LastName>Yan</LastName>
        <Affiliation>School of Life Science, Beijing Institute of Technology</Affiliation>
      </Author>
    </AuthorList>
    <PublicationType/>
    <ArticleIdList>
      <ArticleId IdType="doi"/>
    </ArticleIdList>
    <Abstract>Mismatch negativity (MMN) is suitable for studies of preattentive auditory discriminability and the auditory memory trace. Subjective cognitive decline (SCD) is an ideal target for early therapeutic intervention because SCD occurs at preclinical stages many years before the onset of Alzheimer's disease (AD). According to a novel lifespan-based model of dementia risk, hearing loss is considered the greatest potentially modifiable risk factor of dementia among nine health and lifestyle factors, and hearing impairment is associated with cognitive decline. Therefore, we propose a neurofeedback training based on MMN, which is an objective index of auditory discriminability, to regulate sensory ability and memory as a non-pharmacological intervention (NPI) in SCD patients. Seventeen subjects meeting the standardized clinical evaluations for SCD received neurofeedback training. The auditory frequency discrimination test, the visual digital N-back (1-, 2-, and 3-back), auditory digital N-back (1-, 2-, and 3-back), and auditory tone N-back (1-, 2-, and 3-back) tasks were used pre- and post-training in all SCD patients. The intervention schedule comprised five 60-min training sessions over 2 weeks. The results indicate that the subjects who received neurofeedback training had successfully improved the amplitude of MMN at the parietal electrode (Pz). A slight decrease in the threshold of auditory frequency discrimination was observed after neurofeedback training. Notably, after neurofeedback training, the working memory (WM) performance was significantly enhanced in the auditory tone 3-back test. Moreover, improvements in the accuracy of all WM tests relative to the baseline were observed, although the changes were not significant. To the best of our knowledge, our preliminary study is the first to investigate the effects of MMN neurofeedback training on WM in SCD patients, and our results suggest that MMN neurofeedback may represent an effective treatment for intervention in SCD patients and the elderly with aging memory decline.</Abstract>
    <CoiStatement>No potential conflict of interest relevant to this article was reported.</CoiStatement>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">mismatch negativity</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">neurofeedback</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">working memory</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">subjective cognitive decline</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Alzheimer's disease</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">early intervention</Param>
      </Object>
    </ObjectList>
    <ReferenceList/>
  </Article>
</ArticleSet>
