The authors acknowledge the financial support of the Social Sciences and Humanities Research Council of Canada (SSHERC), Natural Sciences and Engineering Research Council of Canada (NSERC), Fonds de Recherche Nature et Technologies du Qu&#233;bec (FQRNT) and Fonds de Recherche sur la Soci&#233;t&#233; et Culture du Qu&#233;bec (FQRSC).

该协议由法国精品行业institutionnel德拉RECHERCHE AVEC DESêtreshumains（CIER）DE L&#39;魁北克大学蒙特利尔（分校），这是赞同HEC-蒙特利尔的Tech3Lab研究机构获得了道德证书。该协议描述了每个那在我们的实验室环境和设备进行了具体步骤。尽管提供精确的软件的路径，以澄清的方法，这种技术是转让，并且可以与其他专有眼睛跟踪，自动面部情感识别，皮肤电活动和脑电图设备和软件被复制。 1.设置实验室环境<ol><li>打开眼跟踪器，脑电图放大器，四个记录计算机和扬声器。 </li><li>准备录音设备的设置: <ol><li>根据制造商的recomme准备脑电图设置与所需的材料nded程序。准备脑电图软件为即将到来的参与者。启动眼球跟踪软件，并创建了软件的一个新的参与者的个人资料。开始摄像软件和摄像机。 </li><li>启动同步软件，用于在60秒标记的项目创建的具体子程序。启动生理测量软件（记录电活动），并打开该项目创建的具体布局。调整参与者的椅子来的最高水平。 </li></ol></li></ol> 2.参与者准备<ol><li>请参与者阅读并签署道德的知情同意书。 </li><li>开展颅骨测量脑电图: <ol><li>查找（按10 - 20参照系）的锆石的位置上参与者的头部。沉浸脑电图净在盐水溶液（氯化钾）（见步骤1.2.1），并按照制造商的标准启动一个定时器（10分钟）。&lt;/ LI&gt; </ol></li><li>阅读研究和实验参与者的步骤的目的，" 这项研究的目的是观察你的大脑活动，而你回答物理问题。首先，我们将安装传感器，那么你将被要求解决在电脑上10牛顿物理学的问题。我们会要求你采取45秒休息后每个问题闭上你的眼睛。每个问题之后，你会被要求对您的问题进行评估。" </li><li>告诉主体，该实验的总持续时间将是90分钟。 </li><li>左侧的顶部的两个凝胶化传感器:安装生理传感器，根据制造商的建议。 </li><li>安装EEG帽，根据制造商的建议和（根据制造商的规格）中，在40千欧的阈值执行阻抗检查。 </li></ol> 3.数据收集<ol><li>确保所有的录音软件是在准备要同步启动: <ol><li>生理学（EDA数据）:点击"开始"按钮。 </li><li>视频拍摄:点击"打开"按钮。 </li><li>眼睛跟踪:点击"暂停"按钮。 </li><li>脑电图:点击"记录"按钮。 </li><li>同步软件:点击"绿色循环"按钮。 </li></ol></li><li>眼睛注视校准: <ol><li>执行了五点屏幕上的校准，并观察参与者，而他/她跟随红点（点击"工具/设置/校准......"）。重复此过程，直到足够的精度实现时，根据制造商的标准。 </li></ol></li><li>到参与者的屏幕项目任务说明:问他/她有阅读后有任何问题，如果他/她准备开始实验。 </li><li>请参与者解决10牛顿physics问题。 </li><li>如果需要的话，在45秒休息的一个执行阻抗检查（之前没有问题5）。 </li><li>确保参与者需要全45秒突破每一个问题前（以确定基线）。 </li></ol> 4.结束数据收集<ol><li>停止数据采集上的所有​​计算机，并从参与者中删除传感器。 </li></ol> 5.在参与者都有左<ol><li>清洁脑电帽杀菌剂和整理设备，根据制造商的建议。收集保存所有的数据文件，并创建FTP服务器上的备份。 </li><li>在参与者填写电子表格:注意数据收集过程中的任何特定事件或问题。擦除从Web浏览器的所有Cookie。 </li></ol> 6，数据预处理和导出到集成软件<ol start="1"><li>脑电图<ol><li>脑电图导入数据到脑电数据分析软件: <ol><li>创建一个名为"原始数据"，"历史"和"出口"的计算机上的三个空文件夹，以原始脑电数据粘贴到新创建的原始数据文件。 </li><li>在脑电数据分析软件，点击"文件/新建项目..."，并通过点击浏览，然后选择新创建的原始数据文件选择原始数据位置。选择的"历史"和以相同的方式"导出"文件夹的位置。 </li><li>点击"确定"。 （窗口应包含所有参与者的脑电数据）。 </li></ol></li><li>预处理脑信号: <ol><li>应用一个过滤器和一个凹口（点击"变换/ IIR滤波器..."）。在窗口中，使在1.5赫兹的低截止以12dB的斜率和高截止，在50赫兹与12 dB的斜率。也使一个凹口60赫兹的频率。 </li><li>因为一个直流放大器，直流detrend信号（点击"茶nsformations / DC Detrend ......"，并启用"基于时间"的标志前100毫秒和DC连接前100毫秒）。 </li><li>执行原始数据的检查（点击"转换/原始数据的检查......"，然后选择半自动神器去除）。选择以下内容:最大电压60μV/ MS;最大最小:200μV在200毫秒的时间间隔;振幅:-400至+400μV）。 </li><li>执行自动ICA与经典球形为眨眼去除（myographic工件不需要被删除，因为其范围是感兴趣的频率的外侧）。 （点击"变换/ ICA ......"。在ICA，过程的逆ICA的末端。） </li><li>再参考（"转换/重新引用..."）的信号，并选择"共同平均"。 </li><li>导出（单击"导出/通用数据导出..."）的文本格式的信号和标记（选择".vhdr";箱）为最终Matlab的建设的参与指数的。还可以选择"写入头文件"和"写入标记文件"框。 </li></ol></li><li>导入Matlab的信号。 <ol><li>运行MATLAB等EEGLab的GUI出现，并导入一个参与者的数据在同一时间输入"eeglab"。在GUI中，选择项目菜单"文件/导入数据/使用EEGLab功能和插件/脑可见光拍摄.vhdr文件"。 </li><li>在命令窗口中粘贴一个脚本16生成一个参与指数。 注:认知参与脚本由每个测试/（阿尔法+西塔）比率的平均值内的20秒的滑动窗口前述时间T计算重复这一过程每秒钟和一个新的滑动窗口来更新索引。 </li></ol></li><li>在MS Excel，打开是在用Matlab脚本结束时生成的参与指数的文本文件和应用Z-SCOR脑电图数据E正常化允许个体间的比较。 （对于每一个值，计算该公式在Excel中:Z =（值 - 总体均值）/总标准差）。 </li><li>保存z得分参与指数信号在MS Excel的CSV文件。 （点击文件/另存为...并在格式类型选择CSV）。 </li><li> （步骤6.1.2.2。）重复步骤，对每个参与者。 </li></ol></li><li>生理学: <ol><li>在生理数据分析软件导入EDA数据。 </li><li>应用这些参数进行预处理的生理信号: <ol><li>应用对数变换正常化电导的分布按维纳布尔斯和佳士得的23方法。 </li><li>平放在一个10秒的滑动窗口24的信号。 </li></ol></li><li>在生理软件，计算上的EDA数据z得分正常化，使个体间的比较。 （Z =（值 - 总体均值）/总标准差）。 <ol><li>突出与来自EDA通道光标的所有数据。 </li><li>在顶部的菜单中，选择EDA通道，并选择"的意思是"获得整个信道的平均值。还可以选择的EDA信道和"STDDEV"，以获得整个信道的标准偏差值。 </li><li>为了计算Z值方程，点击"转换/波形数学运算......"，并选择在源1的EDA通道。选择" - "（减号），在数学运算窗口和源2.选择在目的地菜单中的"新目标"选择K和进入EDA通道的平均值（见步骤6.2.3.2）。选择"转换整个浪潮"，单击确定，然后单击"转换/波形数学......"。选择源1 EDA-K通道，选择"/"（除）在数学操作窗口，在源2选择K，在目的地和恩特选择"新建目标"r是EDA信道（步骤6.2.3.2）的标准偏差值。选择"转换整个浪潮"，然后单击确定。 </li></ol></li><li>在导出CSV文件的信号（觉醒）。 （点击文件/另存为...并在格式类型选择CSV）。 </li></ol></li><li>自动面部情感识别: <ol><li>从媒体记录到自动面部表情识别软件导入的视频数据。 （点击"文件/新建... /参与者......"。通过点击选择在项目菜单中新的参与者后，单击"文件/新建/分析/视频..."。单击放大镜旁边的分析1，然后选择所需视频文件。 <ol><li>选择脱机分析"每三架"并激活"连续校准"。 </li><li>在一个CSV文件导出数据的价。 （点击"选项/设置/记录......"，勾选"写入价值的日志文件"对话框，点击"文件/导出..."，选择其中的日志文件将被出口的位置，并勾选"保存详细的日志"框。） </li><li>打开MS Excel的CSV文件。价数据列复制SPSS软件中的一列。点击"分析/描述性统计/描述性"，然后选择刚刚粘贴的变量名。勾选"保存标准值的变量"。与z得分列会出现。复制粘贴这些z得分超过在Excel文件中的旧数据。 </li></ol></li><li>保存与z分数以CSV格式信号（价）的Excel文件。 </li></ol></li></ol> 7.数据集成和同步<ol start="1"><li>在行为分析软件: <ol><li>进口眼球追踪视频（行为参与）。 （点击"文件/导入/视频在一个新的观察......"命名新的观察和选择所需的视频文件。） </li><li>代码相关的每次视频haviors和上下文事件（时间标记，正确/错误的答案）。 </li><li>所有导入外部数据与相应的头:z得分脑电信号（认知参与）中，z得分EDA信号（情感投入），Z值价的数据（情感投入）的。 （点击"文件/导入/外部数据..."，选择相应的文件类型，并选择正确的CSV文件。） </li></ol></li><li>根据这些公式同步计算机之间的时间: <ol><li>第一标记的眼睛注视 - 从一次脑电图=时间眼睛注视+脑电图第二标记的眼睛注视时间。 </li><li>时间从时间眼睛注视的情绪认知=时间眼睛注视+面部情感第一标记 - 标记第一次在眼睛注视。 </li><li>不时在眼睛凝视时间在电活动=时间眼睛注视+第一标记的电活动 - 在眼睛注视第一标志。 </li></ol></li><li>按"输入偏移量数据按Ctrl + Shift + =221 ;,打开偏移量菜单。选择"数值偏移"到每对数据源之间秒进入时间[确定]），根据上述的计算。 </li><li>根据在研究感兴趣的变量产生一个报告。 <ol><li>选择将在报告中生成有趣的变量（点击"分析/选择数据/新配置文件数据......"）。从左边的"开始"框和"结果"框之间滑动所需要的变量，在右边。 </li><li>生成报告。 （点击"分析/数值分析/新建..."，单击"统计"，并点击"计算"检查外部数据菜单完成的平均箱。） </li></ol></li><li>将数据导出到统计分析软件，并根据该研究的目标执行分析。 </li></ol>

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J., Martin, I., Venables, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electrodermal+activity.">Electrodermal activity.</a> Techniques in Psychophysiology. , 3-67 (1980).</li><li>Boucsein, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Electrodermal Activity. , (2012).</li><li>Sarlo, M., Palomba, D., Buodo, G. M., Minghetti, R., Stegagno, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Blood+pressure+changes+highlight+gender+differences+in+emotional+reactivity+to+arousing+pictures.">Blood pressure changes highlight gender differences in emotional reactivity to arousing pictures.</a> Biol. Psychol. 70 (3), 188-196 (2005).</li><li>Martens, R. 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The authors have nothing to disclose.

在该协议中的关键步骤方面，首先应当指出，数据的质量始终是神经生理学收集技术的主要焦点。在这种方法中，研究助理必须特别注意​​指示主题，以尽量减少头部运动，这将干扰价监测（失去正确的杆面角度的摄像头），或产生的脑电图myographic文物。在另一方面，平衡必须保持真正的解决问题和干预措施更符合人体工程学的数据集合所做的真实性之间。同样重要的是要注意，脑电图数据收...

参与起着学习了至关重要的作用。对于克拉克和迈耶2，"所有的学习要求参与，"不管交货媒体。张某等 3还建议，增加学生的参与可以提高学习效果，如解决问题和批判性思维能力。定义参与仍然是一个挑战。在他们的文献综述，Fredricks，布鲁门菲尔德和巴黎1定义参与其多面性:"行为参与借鉴了参与的念头;它包括参与学术和社交或课外活动。 （...）情感投入包括正面和负面的反应老师，同学，学者和学校，被推定为创建联系的对象和影响力的意愿做的工作。最后，认知参与借鉴心理投资的理念;它采用体贴，并愿意尽努力NEC埃森理解复杂的思想，掌握高难度的技巧。" Fredricks，布鲁门菲尔德和巴黎1还声称，注重行为，情绪和认知，参与的概念中，可提供学习更丰富的特性。这些作者指出，研究一个健壮体分别针对接合的每个部件，但这些部件并没有被研究结合。他们还观察到的信息很少关于尺寸之间，更多的研究可能有助于规划微调教学干预的相互作用。作为在这个方向迈出的一步，本文介绍的开发是为了收集，并在学习任务分析的定量和定性数据，同步，对行为，情绪和认知参与研究的方法论。 使神经科学到教育 BehavIOR，因此行为的参与，一直是教育研究中央重点:主要集中于发生在长时间内的时间，前后测试之间的变化，在知识和行为的研究设计，并在间隔小时，周，几个月或几年。行为，情感和认知参与区分仍然是一个挑战，因为最后两个维度是没有系统观察到外部。认知和情绪必须被从观察推断或评价与自我报告的措施。从外部来看，它仍然难以确定学生是否正在试图让他们的工作尽可能快地或使用深层次的学习策略，掌握了具体的内容进行。就事实而言，Fredricks，布鲁门菲尔德和巴黎1无法找到使用认知参与的直接，客观的任何措施发表的研究报告。 在最近的技术发展神经科学领域创造了科研教育新的可能性。新的数据收集方法和神经人体工程学的领域发展分析算法看起来非常有前途的定性和定量研究的过程中学习任务。其他学科，如经济学，心理学，市场营销，和人体工程学，一直在使用神经生理学测量，以评估认知参与了一段时间4-8。神经生理学的措施，再加上高效的分析算法，允许一个学习的现象，而不会干扰它。就其性质而言，自我报告的问卷脱离学习的学生。神经生理学的措施让研究设计，以更真实的学习环境中进行。这些工具包括设备监测心脏速率，呼吸速率，血压，体温，瞳孔直径，皮肤电活动，脑电图（EEG） 等 。 <st荣&gt;聚会同步数据的行为，情绪，认知和参与作为代表性的成果继用此协议，本文将提出一个研究中，学习者需要解决，在电脑屏幕上，在机械物理学十大难题的部分结果。在以往的工作进行了9开发了这些问题。收集的数据神经生理学，而学生是解决问题和45秒休息时放松，用自己的眼睛闭上，每个​​问题之后。 如上所述，行为接合数据由软件相互作用（鼠标移动和点击），眼睛注视，性能和回答由学习者与系统而完成任务1相互作用产生的问题。一种眼球追踪系统来收集软件的交互和眼睛注视数据。性能数据（时间解决一个问题，答案的正确性）收集在调查网站被用来呈现的任务。该网站也被用来收集与改编自布拉德利和朗10调查问卷收集自我报告的数据。情感投入涉及情感的表征。据郎11，情绪的特征价方面（愉快/不愉快的）和觉醒（CALM /激起）。情感投入数据进行了相应的收集，使用量化情绪价和皮肤电活动编码器/传感器觉醒12,13自动面部表情识别软件。皮肤电活动（EDA）指的是当一个非常弱的电流稳定地在它们之间传递的两个电极之间所记录的电阻。卡西奥普，Tassinary和Berntson 14表明，记录的电阻根据被检者的觉醒而变化。因此，心理生理数据，例如化合价或觉醒，被认为是情感接合的相关因素。 <p类="jove_content"&gt;最后，认知参与数据通过脑电图（EEG）收集。脑电图措施，在头皮上，同步于脑的神经元群体的电活动。从头皮记录的电信号往往振荡和频率分量组成。按照惯例，这些频率被分组序列中，被称为带。例如，α，β和θ频带是本研究的焦点。根据神经科学的研究14，这些频带反映在大脑的特定区域不同的认知处理能力。因此，特定频率的功率谱密度（PSD）的分析，结合对警觉性和注意力大量研究7,15，使研究人员能够在任务中量化认知参与。作为米库尔卡等人 16指出，有研究表明β活度和认知警觉和alpha一个之间的间接关系之间的直接关系ðTHETA活动和警觉性。因此，教皇，鲍嘉和Bartoleme 7开发了计算的三个波段的PSD参与指数:公测/（阿尔法+ THETA）。这比在其他研究中的参与16,17,18验证。随着时间的推移表征认知参与，快速傅立叶变换（FFT）将来自每个活性部位（F3，F4，O1，O2）脑电图信号转换成一个功率谱。在时间T脑电图参与指数是由每个接合比率的平均值中时间T重复这一过程每秒钟和一个新的滑动窗口来更新索引之前的20秒的滑动窗口来计算。 由于这种方法的目的是提供接合的多个尺寸的富分析，数据同步是至关重要的。由于莱热等 19提醒读者，设备制造商强烈推荐使用每次测量工具只有一台电脑，以保证其指定的精度乐VEL。因此，当多台计算机被使用，记录在计算机之间的同步成为一个关键步骤。录音不能在完全相同的时间都被启动，并且每个数据流具有其特定的时间框架（ 例如 ，秒0的眼跟踪≠脑电图或生理数据秒0）。这是极为重要的:数据流之间的去同步化装置中的错误接合的每一维的量化。有并发的生理和行为同步录音的方式不同。这些方法可分成两种主要方法;直接和间接的20。在下一节介绍的协议是基于在那里的外部设备，一个syncbox，用于晶体管-晶体管逻辑（TTL）信号发送到所有的记录设备（ 如图1）的间接的方法。由于每一件设备具有不同的开始时间，将TTL标记被记录在日志文件中以相对<html>延迟。标记，然后用于重新对准信号，从而保证在每个记录后正确的同步。甲行为分析软件程序，它允许外部文件集成用于重新同步每个数据流的时间轴，并执行接合的每个维的定量和定性分析。 <img alt="图1" src="/files/ftp_upload/52627/52627fig1.jpg" /> 图1.架构的数据采集系统，实验室环境中的行为（眼动跟踪），情感（EDA和面部表情）和认知（EEG）参与收集数据包含多台计算机。这就提出了一个同步挑战对于那些在它们各自的计算机的时钟被引用的数据。为了能够分析所有数据以相同的参考时间，在实验室安装涉及发送TTL信号到所有数据流的syncbox。NK"&gt;点击此处查看该图的放大版本。 为了评估在同步方面的方法的精确度，45秒的暂停每个的机械物理问题之前进行了介绍。在这些暂停，受试者放松，闭上眼睛。正如在其他研究4,9,16,17,18，这些暂停应该引起在采集到的信号显著的变化:在眼球追踪立即消失（行为参与）两个眼瞳点和认知参与立刻降（EEG信号）被观察到。信号的这些特定组件被用来评估该同步的普遍有效性。最近出版的完全或部分地依赖于该同步过程，在信息系统19，人机交互21和教育9,22领域论文，提供它的有效性的证据。

<table><tbody><tr><td>EGI GSN-32&amp;nbsp;</td><td>EGI</td><td></td><td>Dense array EEG</td></tr><tr><td>Netstation v.5.0</td><td>EGI</td><td></td><td>EEG data collection software: EEG is collected with 32-electrode dense array electroencephalography (dEEG) geodesic sensor net using Netstation acquisition software and EGI amplifiers (Electrical Geodesics, Inc). The vertex (recording site Cz) is the reference electrode for recording. Impedance is kept below 50 k&amp;Omega; with a sampling rate of 250 Hz.&amp;nbsp;</td></tr><tr><td>Facereader v.4</td><td>Noldus</td><td></td><td>Facial emotion recognition software</td></tr><tr><td>Syncbox</td><td>Noldus</td><td></td><td>Syncbox start the co-registration of EEG and gaze data by sending a Transistor-Transistor Logic (TTL) signal to the EGI amplifier and a keystroke signal to the Tobii Studio v 3.2.</td></tr><tr><td>Logitech C600&amp;nbsp; Webcam</td><td></td><td>960-000396</td><td>Webcam used to gather video data sent to mediarecorder and that will be analyzed in Facereader</td></tr><tr><td>The Observer XT</td><td>Noldus</td><td></td><td>Integration and synchronization software: The Noldus Observer XT (Noldus Information Technology) is used to synchronize all behavioral, emotional and cognitive engagement data.&amp;nbsp;</td></tr><tr><td>On-Screen LED illumination</td><td>Noldus</td><td></td><td>Neon positioned on computer screen in order to correctly light the face of subjects</td></tr><tr><td>MediaRecorder</td><td>Noldus</td><td></td><td>Video data collection software</td></tr><tr><td>Tobii 60X</td><td>Tobii</td><td></td><td>Collect eye-movement patterns :&amp;nbsp; used to record subjects&amp;rsquo; eye movement patterns at 60Hz during the experiment.&amp;nbsp;</td></tr><tr><td>Tobii Studio v.3.2</td><td>Tobii</td><td></td><td>Eye-tracking data collection and analysis software</td></tr><tr><td>Analyzer 2</td><td>Brainvision</td><td></td><td>EEG signal processing software</td></tr><tr><td>Acqknowledge v.4.0</td><td>Biopac</td><td>ACK100M</td><td>Physiological signal acquisition and processing software</td></tr><tr><td>Control III germicide solution</td><td>Maril Products.</td><td>10002REVA-20002-1</td><td>Disinfectant solution used with EEG helmets : recommended by EGI</td></tr><tr><td>Unipark</td><td>QuestBack AG</td><td></td><td>Online survey environment</td></tr></tbody></table>

assessing the multiple dimensions of engagement to characterize learning: a neurophysiological perspective

In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris1 defined engagement by its multiple dimensions: behavioral, emotional and cognitive. They observed that individual types of engagement had not been studied in conjunction, and little information was available about interactions or synergy between the dimensions; consequently, more studies would contribute to creating finely tuned teaching interventions. Benefiting from the recent technological advances in neurosciences, this paper presents a recently developed methodology to gather and synchronize data on multidimensional engagement during learning tasks. The technique involves the collection of (a) electroencephalography, (b) electrodermal, (c) eye-tracking, and (d) facial emotion recognition data on four different computers. This led to synchronization issues for data collected from multiple sources. Post synchronization in specialized integration software gives researchers a better understanding of the dynamics between the multiple dimensions of engagement. For curriculum developers, these data could provide informed guidelines for achieving better instruction/learning efficiency. This technique also opens up possibilities in the field of brain-computer interactions, where adaptive learning or assessment environments could be developed.

<html> 图2和3示出的屏幕截图的整合和行为，情感和认知参与数据的同步在行为分析软件应用程序的结果。在两个图中，左手区组织的研究对象和编码方案。在中间部分，视频（带有红点）示出了在任务期间被检者的眼睛注视。主体的参与行为可以根据他/她的任务中寻找和执行什么操作来推断。在较低部分，一个时间标记同步地滚动数据的三条轨道:将EDA（觉醒）及面部情感价为情感投...

Watch this Scientific Journal Video about Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective at JoVE.com

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

This paper aims to describe the techniques involved in the collection and synchronization of the multiple dimensions (behavioral, affective and cognitive) of learners&#8217; engagement during a task.

评估参与的多维度来表征学习:神经生理学角度

In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris1 defined engagement by its multiple dimensions: ...

assessing-multiple-dimensions-engagement-to-characterize-learning

Universidad San Sebastian

Research

JoVE Journal

Behavior

12.4K 次观看.  Université du Québec à Montréal. This paper aims to describe the techniques involved in the collection and synchronization of the multiple dimensions (behavioral, affective and cognitive) of learners’ engagement during a task. 

视频: Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Study Motor Skill Learning by Single-pellet Reaching Tasks in Mice

Reaching for and retrieving objects require precise and coordinated motor movements in the forelimb. When mice are repeatedly trained to grasp and retrieve food rewards positioned at a specific location, their motor performance (defined as accuracy and speed) improves progressively over time, and plateaus after persistent training. Once such reaching skill is mastered, its further maintenance does not require constant practice. Here we introduce a single-pellet reaching task to study the acquisition and maintenance of skilled forelimb movements in mice. In this video, we first describe the behaviors of mice that are commonly encountered in this learning and memory paradigm, and then discuss how to categorize these behaviors and quantify the observed results. Combined with mouse genetics, this paradigm can be utilized as a behavioral platform to explore the anatomical underpinnings, physiological properties, and molecular mechanisms of learning and memory.

Persistent practice improves the precision of coordinated movements.&#160;Here we introduce a single-pellet reaching task, which is designed to assess the learning and memory of forelimb skill in mice.&#160;

学习运动技能通过单颗粒到达任务小鼠学习

Reaching for and retrieving objects require precise and coordinated motor movements in the forelimb. When mice are repeatedly trained to grasp and ...

科研

行为学

Perspectives on Neuroscience

As usual, the main cause for progress in natural sciences are methodological developments. Of course, conceptual developments follow and go in parallel, but I think the, the tremendous expansion of the field of neurosciences was triggered by a number of methodological breakthroughs at the lowest level. These were the progresses in the domain of molecular biology, genetic engineering, which provided model systems for the study of development for the study of certain diseases.These techniques also provided extremely valuable tools for the biochemical and molecular identification of neurons. We learned about a degree of diversification of morphologically seemingly identical neurons that are endowed with different molecular machineries. All this we owe to the development of molecular biology.A second important factor I think was the development of non-invasive imaging technologies. cause this allowed us to transfer a large number of concepts that have been developed in animal experimentation to investigations in human subjects. So this is the functional magnetic resonance imaging pository emission tomography.Also the introduction of the magneto ence holography, which allowed for much better spatial resolution for the analysis of electric activity in the brain led to a substantial expansion in the field. Then I think a non negligible factor was the availability, increasing availability of of massive computer power because as neurobiology started to apply multi-site recording techniques, which I consider essential for the analysis of neuron network properties, the data became increasingly complex and it became increasingly impossible to just evaluate the data by looking at them as in the classical times when I started in the field, there were no computers. We had to do cross correlation analysis by cutting long strips of paper and moving them over the floor.So the availability of of computing power has greatly advanced at least systems physiology. I think it had a major impact on these developments. And then last but not least, the availability of these computer facilities allowed us also to treat properties of neuronal networks at a theoretical level.The dynamics exhibited by neuronal networks are so complex that only modeling studies and theoretical approaches put us in a position to formulate educated hypothesis. Also, for the search of neuronal codes, we cannot proceed without these search tools. So I think altogether these methodological developments were responsible for the great expansion of the neurosciences, which I think is unprecedented in any Scientific discipline.As far as my own Research is concerned, there are two parts in my biography. Initially I studied developmental processes in the brain. I was particularly interested in developmental processes that are shaped by experience postnatally.And the rationale for this approach was that I felt that the organization of the mature brain is so complex that we better study the developmental principles and try to make sense out of them and try to extrapolate what these developmental processes will eventually lead to. But then an accidental finding that actually happened in this very laboratory where we are sitting changed my life because I discovered inadvertently that neurons in the primary visual cortex of kittens that were awake actually sitting on the table behind me watching a drifting grating, got entrained into highly synchronous oscillatory activity that I could not attribute neither to degrading to the stimulus nor to the main switch in Germany are 50 cycles because it was an oscillation in the 40 hertz range. And so for the first time I saw that spatially distributed neurons could coordinate their activity with very high precision in the millisecond range over fairly large distances.And this triggered the idea that maybe time is used as a coding space for the coordination of distributed neuronal responses. And I found this option so interesting that I decided to neglect the so much beloved field of developmental neurobiology and invest most of our efforts into the further analysis of this phenomenon. And this is now nearly 15 years ago or more.And since then we spent most of our time with multi-site recordings and the search for spatial temporal patents in distributed neural networks because it is our firm belief that information is not only encoded in the discharge rate of neurons as is classically assumed and confirmed, but that additional information is encoded in the precise temporary relations among the individual discharges of neurons whereby zero phase lake correlations. So precise coincidence is probably only the tip of the iceberg. But an important tip because synchronous activity is particularly interesting in neuronal networks because it propagates very easily synchronizing responses is a means to enhance the saliency of neuronal responses with great temporal position.It can be done on a spike by spike basis. And the initial hypothesis was that this synchronization is used at peripheral stages of sensory processing in order to establish relations between features that are processed by spatially distributed neurons. Since then, many more labs have engaged in search for the synchronization phenomena.It started I think in Europe with colleagues like RI who applied EEG techniques to assess synchronous oscillatory activity in the brain and relate these activities to cognitive functions. And since then the the role of oscillations and synchrony has been discussed in a very controversial way. Initial doubts that this phenomenon might exist, it all have now been countered I think, and we are now all engaged in the search for functional consequences.And there is a whole plethora of hypothesis of what that could mean. Does the accumulation Of this vast amount of data that we are confronted with nowadays and that we have very great difficulties to cope with reflect true progress. It certainly does reflect progress because we get to know more and more facts and details about the organization of neurons, neuron networks and the brain as a whole.And this is a necessary consequence of the classical analytical approaches that are applied in the natural sciences. One isolates a subsystem and then describes it in ever greater detail. But this is only one half of the scientific endeavor.What we also need is the inductive strategy. This the synthesis of all those data. And what we are now confronted with is that we have a huge amount of words.We have a tremendous vocabulary that is difficult to remember, but we are lacking the syntax to put those words together into meaningful sentences in many domains. This is the case in particular in systems neuroscience. Therefore we need, I think more theory because conditions have become so complex that near verbal descriptions are probably no longer sufficient to grasp or to accomplish this, this synthetic process.We need formal descriptions, we need analysis tools that allow us to generate models and this requires very powerful computations. And then we need of course synthetic thinkers. That's probably the the most important ingredient in the moment.People who can bundle a large amount of details into meaningful hypothesis or concepts. Now one can of course foster this process by providing the appropriate environment and worldwide one sees efforts to provide such environments by generating institutions that are usually called Institute for Computational Neuroscience or Institute for Theoretical Neuroscience. For the time being, many of these places are still existing in two great in isolation and are not sufficiently linked to experimental institutions.Ideally one would have, one would like to see a very close cooperation between theoreticians and experimentalists, as has been so fruitful in physics where theoretical physicists and experimental physicists very closely collaborate freely, exchange data ideas and do this without jealousy, which is still not the case in our field. But I think biology is now approaching a threshold where it is mature enough to support theoretical approaches in a meaningful way. And here in Frankfurt, we have founded the Frankfurt Institute of Advanced Studies exactly for that purpose because I experienced that physicists and mathematicians, theoreticians in general when embedded in the experimental institute, they tend to to be marginalized.They don't have enough colleagues to talk to because biologists usually are not trained in their language. And so the idea was let's create an institute in which several model systems, biological model systems that have in common to be multi-component systems that are tightly coupled like the moon system or the brain are studied by theoreticians, hoping that these theoreticians might discover common principles in these complex self-organizing systems and that they can share analytical tools, can share tools for model building. And I think it worked out because within about a year this institute grew from zero to 120 scientists.And the special structure of this institute, which is not surprising to colleagues in America, but this is quite exceptional in Europe, is that this institute is funded exclusively by donors private funds. It is not a university institute, it is linked to the university in Frankfurt, but it is finance in a different way for the simple reason that it was impossible to install such an interdisciplinary enterprise within the conventional faculties because what happens there is truly I the disciplinary, it crosses faculties and they seem to be realizable only by having a erect and an own building and gather people who come from very different disciplines under the same roof. Even though those Terms have been taken from physical systems initially, they still grasp some of the characteristics of of of the brain because the brain clearly is a self-organizing system.There's no conductor in the brain to coordinate all the distributed processes. The brain develops in a self organizing way through interaction between genes and environment. There is no instructor, there is no blueprint, a priori.And finally the term applies very well to what we observe in complex systems all the time, namely that complex systems tend to exhibit properties that cannot be described or inferred from the properties of the components. And the brain is a particularly nice example for this fact, which we know from all complex systems there's nothing special about the brain. Something that is special about the brain is that we have on the one side material processes, neuron interactions that can be described from a third person perspective as any other process in our environment.And that emergence from these neuronal interactions are mental phenomena, psychological phenomena such as emotions, thoughts, decisions. So there are realities which I would like to address as social realities which emerge or come into being by the interaction between brains that communicate with each other, mirror each other, ascribe properties to each other, could replace brains by persons, doesn't really matter because what makes the person is finally the brain in its in its bodily setting and due to these interactions phenomena emerge that are to be taken as real phenomena, they are realities, but they have to be described in a different language as the phenomena that bring them forth that are at their origin, namely neuronal interactions. And therefore I think that this is a classical case of emergence where something new is generated by a complex system, nobody would ever be able to infer from the knowledge, complete knowledge of neurons, and even from complete knowledge of neuron networks, that they are capable of developing value systems when they interact With each other.I think the main Problem in our efforts to repair brains, apart from the fact that neurons do not easily regenerate in the central nervous system, they don't like to extend processes once they are severed for reasons that we do not know. Maybe there was no Evo evolutionary pressure on the development of such mechanisms because brain damage that really leads to cell death in in a large scale was usually not compatible with survival. The great problem in restoring neuron networks that have been damaged is that it is difficult to recapitulate the developmental process.The mature brain is characterized by an extremely sophisticated circuitry that has developed step by step in a complex self-organizing process, guided by the dialogue between the genes and the increasingly more differentiated cellular environment that changes throughout development. And this process is a essentially bottom up process. Peripheral structures mature first, then they go through critical periods and the connectivity becomes fixed so that the, the higher stages can rely upon what has already been built and then they organize themselves as a function of what's already there.And this process goes on in human beings until age 20 and then the circuitry crystallizes and then we have to live with the brains that we have got at that point of time. There's further modification by learning and neuroplasticity, but this is limited. Now if one were to replace neurons at higher levels of processing, for example, by injection of stem cells or so these neurons we know already, they can differentiate, they the precursors can become neurons, but the problem is will they find the partners with which they have to interact?Will the information still be there That was available during development that guided the axon growth, that guided the formation of specific connections. And we know that to, to a large extent, this information is no longer available because the genes that one needs in order to express the necessary marker molecules, they get switched off once development comes to an end. And what is probably necessary is that one not only supplies cells that can differentiate into neurons and of course also ggl cells, but that one also switches on or helps the organism to switch on the genes that have been active during the developmental process that lead to the expression of all the molecular machinery that is required to tell the cells with whom they should connect, where they should connect.And what of course cannot be reinstalled easily is all the idiosyncratic wiring and the adjustment of synaptic game that is due to learning processes. The newly integrated neurons will of course have no souvenir, no recollection of the experience that has been imprinted into the morphology and the structure and the function of neurons that have disappeared. So there are certain limits to the ability to restore.And as we realize in the moment, there are certain places in the brain where this seems to pose less of a problem because nature has foreseen lifelong regeneration of neurons and apparently kept the machinery alive that is needed in order to promote this ongoing regeneration and insertion of neurons as in hippocampus and in the olfactory bulb. But the other structures where this is probably causing great problems, the first replacements that have been attempted and have had certain success even though it was not satisfactory yet, where the modulatory systems where topological specificity is not that much a problem. These systems are functioning more like endocrine or paracrine systems that liberate certain transmitters that are required in order to keep a certain tous of excitability.These systems are more easily replaceable because specificity is less of a problem, but the future will show how far we get there. Certainly very promising approaches in reinstalling oxon outgrowth in several spinal cords by inhibiting factors that inhibit outgrowth in the adult and by activating mechanisms that come to action during development and promote outgrowth. There are encouraging successes in this field, but one should also consider very seriously the option of neuronal prosthetics to record neuronal activity from places where it is still available and then through mechanical devices and computer interfaces activate actors that can translate this activity into action for the motor system.Or conversely, on the sensory side, cochlear implants already work admirably well. There are now attempts to develop retinal implants in order to reinstall rudimentary visual functions in people who have retinal degeneration. There's also a very promising option to insert light gated iron channels into retinal cells so that cells that are normally not light sensitive, which are the ganglion cells for example, or the bipolar cells become light sensitive so that one could in principle reins store some function that is lost when photoreceptors degenerate by simply transferring light sensitivity to second or third order neurons.This is already possible in animal experimentation. It might be another promising avenue to pursue. I think Europe has a very strong tradition in the development of concepts end of 19th century helm those seminal figures who have provided the grounds for later experimental neurobiology to some extent.They also are the grand grandfathers of a substantial number of systems physiologists who have been active in Europe. I think there is a good tradition in systems neuroscience in Europe has always been in the times when, when linear systems theory was still at its peak cybernetics, as it was called Europe, had a few very strong exponents such as rehad for example. Then the, there was something like, not iconic turn, but the molecular turn in Europe as well as anywhere else, molecular biology took over and replaced many of the systems oriented approaches.Partly because initially it had been cheaper than to work with whole animals, partly because non-invasive technology was not available and one couldn't do certain things in animals, which can now be done in human subjects. But I think there's a strong renaissance in systems physiology even though in Europe we have to fight a hard battle against animal protectionists concerning primary research, this is a real critical issue. The labs that can, they still get the license to work with primates become less and less and those who still have the license.This is the case with us where we have, we are exposed very much to public critique, but still systems neuroscience is having a renaissance. And I think there are good reasons for that because we will need it more and more in order to make sense out of the plethora of data that are collected by the properties of neurons and small circuits. And these data can only be integrated if we have concepts derived from systems neuroscience on the nature of the neuronal code, on the cooperation of areas in the brain, on global coordination of processes, on distributed processing.All those questions are heatedly debated in the moment and we just need more experiments along those lines. And I think Europe is not in a bad position to do this. Israel as well, one should not forget the contribution, also conceptual contribution of our colleagues in Israel, which is substantial.

展望神经

生物学

Combined Shuttle-Box Training with Electrophysiological Cortex Recording and Stimulation as a Tool to Study Perception and Learning

Shuttle-box avoidance learning is a well-established method in behavioral neuroscience and experimental setups were traditionally custom-made; the necessary equipment is now available by several commercial companies. This protocol provides a detailed description of a two-way shuttle-box avoidance learning paradigm in rodents (here Mongolian gerbils; Meriones unguiculatus) in combination with site-specific electrical intracortical microstimulation (ICMS) and simultaneous chronical electrophysiological in vivo recordings. The detailed protocol is applicable to study multiple aspects of learning behavior and perception in different rodent species.
Site-specific ICMS of auditory cortical circuits as conditioned stimuli here is used as a tool to test the perceptual relevance of specific afferent, efferent and intracortical connections. Distinct activation patterns can be evoked by using different stimulation electrode arrays for local, layer-dependent ICMS or distant ICMS sites. Utilizing behavioral signal detection analysis it can be determined which stimulation strategy is most effective for eliciting a behaviorally detectable and salient signal. Further, parallel multichannel-recordings using different electrode designs (surface electrodes, depth electrodes, etc.) allow for investigating neuronal observables over the time course of such learning processes. It will be discussed how changes of the behavioral design can increase the cognitive complexity (e.g. detection, discrimination, reversal learning).

Shuttle-box avoidance learning is well-established in behavioral neuroscience. This protocol describes how shuttle-box learning in rodents can be combined with site-specific electrical intracortical microstimulation (ICMS) and simultaneous chronical in vivo recordings as a tool to study multiple aspects of learning and perception.

联合穿梭盒子训练与电的Cortex记录和刺激作为一种工具来研究感知和学习

Shuttle-box avoidance learning is a well-established method in behavioral neuroscience and experimental setups were traditionally custom-made; the ...

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first language development. While many studies of statistical learning are conducted within a single domain or modality, recent evidence suggests that this skill may differ based on the context in which the stimuli are presented. In addition, few studies investigate learning as it unfolds in real-time, rather focusing on the outcome of learning. In this protocol, we describe an approach for identifying the cognitive and neural basis of statistical learning, within an individual, across domains (linguistic vs. non-linguistic) and sensory modalities (visual and auditory). The tasks are designed to cast as little cognitive demand as possible on participants, making it ideal for young school-aged children and special populations. The web-based nature of the behavioral tasks offers a unique opportunity for us to reach more representative populations nationwide, to estimate effect sizes with greater precision, and to contribute to open and reproducible research. The neural measures provided by the functional magnetic resonance imaging (fMRI) task can inform researchers about the neural mechanisms engaged during statistical learning, and how these may differ across individuals on the basis of domain or modality. Finally, both tasks allow for the measurement of real-time learning, as changes in reaction time to a target stimulus is tracked across the exposure period. The main limitation of using this protocol relates to the hour-long duration of the experiment. Children might need to complete all four statistical learning tasks in multiple sittings. Therefore, the web-based platform is designed with this limitation in mind so that tasks may be disseminated individually. This methodology will allow users to investigate how the process of statistical learning unfolds across and within domains and modalities in children from different developmental backgrounds.

Presented here is a protocol introducing a set of child-friendly statistical learning tasks geared towards examining children&#8217;s learning of temporal statistical patterns across domains and sensory modalities. The developed tasks collect behavioral data using the web-based platform and task-based functional magnetic resonance imaging (fMRI) data for examining neural engagement during statistical learning.

通过在线平台和神经成像技术测量学龄儿童跨模式和领域的统计学习

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first ...

神经科学

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

fNIRS hyperscanning is widely used to detect the neurobiological underpinnings of social interaction. With this technique, researchers qualify the concurrent brain activity of two or more interactive individuals with a novel index called inter-brain synchrony (IBS) (i.e., phase and/or amplitude alignment of the neuronal or hemodynamic signals across time). A protocol for conducting fNIRS hyperscanning experiments on collaborative learning dyads in a naturalistic learning environment is presented here. Further, a pipeline of analyzing IBS of oxygenated hemoglobin (Oxy-Hb) signal is explained. Specifically, the experimental design, the process of NIRS data recording, data analysis methods, and future directions are all discussed. Overall, implementing a standardized fNIRS hyperscanning pipeline is a fundamental part of second-person neuroscience. Also, this is in line with the call for open-science to aid the reproducibility of research.

The protocol for conducting fNIRS hyperscanning experiments on collaborative learning dyads in a naturalistic learning environment is outlined. Further, a pipeline to analyze the Inter-Brain Synchrony (IBS) of oxygenated hemoglobin (Oxy-Hb) signals is presented.

开放式协作学习中的脑间同步:fNIRS超扫描研究

fNIRS hyperscanning is widely used to detect the neurobiological underpinnings of social interaction. With this technique, researchers qualify the ...

Assessing Corticospinal Excitability During Goal-Directed Reaching Behavior

Reaching is a widely studied behavior in motor physiology and neuroscience research. While reaching has been examined using a variety of behavioral manipulations, there remain significant gaps in the understanding of the neural processes involved in reach planning, execution, and control. The novel approach described here combines a two-dimensional reaching task with transcranial magnetic stimulation (TMS) and concurrent electromyography (EMG) recording from multiple muscles. This method allows for the noninvasive detection of corticospinal activity at precise time points during the unfolding of reaching movements. The example task code includes a delayed response reaching task with two possible targets displayed &#177; 45&#176; off the midline. Single pulse TMS is delivered on the majority of task trials, either at the onset of the preparatory cue (baseline) or 100 ms prior to the imperative cue (delay). This sample design is suitable for investigating changes in corticospinal excitability during reach preparation. The sample code also includes a visuomotor perturbation (i.e., cursor rotation of &#177; 20&#176;) to investigate the effects of adaptation on corticospinal excitability during reach preparation. The task parameters and TMS delivery can be adjusted to address specific hypotheses about the state of the motor system during reaching behavior. In the initial implementation, motor evoked potentials (MEPs) were successfully elicited on 83% of TMS trials, and reach trajectories were recorded on all trials.

Reaching is a fundamental skill that allows humans to interact with the environment. Several studies have aimed to characterize reaching behavior using a variety of methodologies. This paper offers an open-source application of transcranial magnetic stimulation to assess the state of corticospinal excitability in humans during reaching task performance.

评估目标导向到达行为期间的皮质脊髓兴奋性

Reaching is a widely studied behavior in motor physiology and neuroscience research. While reaching has been examined using a variety of behavioral ...

An Introduction to Learning and Memory

Learning is the process of acquiring new information and memory is the retention or storage of that information. Different types of learning, such as non-associative and associative learning, and different types of memory, such as long-term and short-term memory, have been associated with human behaviors. Studying these components in detail helps behavioral scientists understand the neural mechanisms behind these two complex phenomena.
JoVE's overview on learning and memory introduces common terminologies and a brief outline of concepts in this field. Then, key questions asked by behavioral scientists and prominent tools such as fear conditioning and fMRI are discussed. Finally, actual experiments dealing with aging, eradication of traumatic memories, and improvising learning are reviewed.

Learning and Memory: Types; Behavioral Tests &amp; Techniques

学习和记忆简介

Learning is the process of acquiring new information and memory is the retention or storage of that information. Different types of learning, such as ...

教育

JoVE Science Education

心理学

行为科学

An Introduction to Neurophysiology

Neurophysiology is broadly defined as the study of nervous system function. In this field, scientists investigate the central and peripheral nervous systems at the level of whole organs, cellular networks, single cells, or even subcellular compartments. A unifying feature of this wide-ranging discipline is an interest in the mechanisms that lead to the generation and propagation of electrical impulses within and between neurons. This subject is important not only for our understanding of the fascinating processes driving human thought, but also for our ability to diagnose and treat disorders related to nervous system malfunction. This video will provide an introduction to the field of neurophysiology, beginning with a brief history of neurophysiological research that showcases landmark studies like Galvani&#8217;s observations of twitching frog legs and Eccles&#8217;s discovery of the chemical synapse. Next, key questions asked by neurophysiologists are introduced, followed by an overview of some prominent experimental tools used to answer those questions. The methods presented range from techniques used to investigate single cells, like patch clamping, to those that can measure activity across large regions of the brain, like electroencephalography (EEG). Finally, applications of neurophysiological research are discussed, including the development of brain-machine interfaces that allow for device control using thought.

Introduction to Neurophysiology: Methods &amp; Applications

神经生理学导论

 Neurophysiology is broadly defined as the study of nervous system function. In this field, scientists investigate the central and peripheral nervous ...

高级生物学

神经科学的基本知识

Perspectives on Neuropsychology

Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel&#8212; University of Southern California
Neuropsychology is a complex field, as it investigates how mental processes are executed in the brain&#8212;events that integrate concepts from biochemistry, biology, psychology, and neuroscience. Although the multidisciplinary nature of neuropsychology prepares young learners for a variety of careers, it also poses a challenge in that it forces students to study concepts outside of their comfort zone. For example, a psychology major may have difficulty grasping neuroanatomy&#8212;a challenging topic in itself&#8212;given that the brain is a complicated, three-dimensional organ that is typically represented two-dimensionally in textbooks.
This JoVE collection in Neuropsychology introduces major concepts and methods in the field, and showcases how advances in imaging technology have allowed us to look inside the brain and visualize its structure and function. Importantly, these videos are also meant to reassure students that you don&#8217;t need to be an expert in all aspects of neuropsychology to understand how the brain shapes our experiences, behaviors, and social interactions in everyday life. For instance, the video "Decision-making and the Iowa Gambling Task" explores how damage to a specific region of the brain can affect an individual&#8217;s proclivity for risky decisions, like stealing.
These JoVE videos in Neuropsychology provide the perfect introduction to this complex topic, and also explore some of the potential, exciting future directions in this field. With further advances in technology, one day researchers might even be able to visualize dynamic changes in a person&#8217;s brain outside of a laboratory setting&#8212;for example, when they are sitting in a classroom listening to a lecture on neuropsychology.

神经心理学的观点

Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel&#8212; University of Southern California
Neuropsychology is a complex field, as it ...

神经心理学

Perspectives on Cognitive Psychology

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University

As an Assistant Professor of Psychology and Cognitive Science at Johns Hopkins University, I teach Introduction to Cognitive Psychology. This large class primarily consists of freshman and sophomores majoring in related fields, like Psychology, Neuroscience, or Cognitive Science. One of the challenges I face in the classroom is teaching students to appreciate how data are obtained in the process of performing experiments. Students have difficulty in appreciating the chronology of experiments as they unfold in time.
This JoVE collection in Cognitive Psychology makes the experimental timeline absolutely clear and encourages students to understand the trajectory and not just jump to conclusions. The videos also present experiments as paradigms&#8212;to illustrate how researchers can implement tasks in different ways&#8212;depending on the questions at hand. Most of these questions translate to the real world and the limits of our cognition. For example, the video Measuring Verbal Working Memory Span demonstrates our limited memory capacity, which explains the difficulty in remembering long shopping lists.
These JoVE videos in Cognitive Psychology provide a perfect place for instructors to start if they are interested in demonstrating tasks in class. Better yet, the videos can be used to get students involved&#8212;to execute the procedures and to even be the participants. Thus, the collection perfectly introduces lab components to a Psychology class.

认知心理学的视角

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University

As an Assistant Professor of Psychology and Cognitive Science at Johns Hopkins ...