Name: assessing the multiple dimensions of engagement to characterize learning: a neurophysiological perspective
Uploaded: 2015-07-01T15:00:00.000+00:00
Duration: 13 min 57 s
Description: Watch this Scientific Journal Video about Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective at JoVE.com

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).

이 프로토콜은 Tech3Lab 연구 시설에 대한 HEC-몬트리올에 의해 승인 된위원회 (CCT) institutionnel 드 라 공들인 AVEC 데 êtres humains (CIER) 드 난 Université 뒤 퀘벡 몬트리올 (UQAM)에서 윤리적 인증서를 받았다. 이 프로토콜은 우리의 테스트 환경 및 장비에서 수행하는 특정 각 단계에 대해 설명합니다. 정확한 소프트웨어 경로는 방법을 명확하게하기 위해 제공되지만,이 기술은 양도 및 기타 소유권 안구 추적, 자동 안면 인식 감정, 피부 전기 활동 및 뇌파 장비 및 소프트웨어와 함께 복제 될 수있다. 실험실 환경 1. 설정 <ol><li> 눈 추적, 뇌파 증폭기, 네 개의 기록 컴퓨터와 스피커의 전원을 켭니다. </li><li> 기록 장치의 설치를 준비 : <ol><li> 제조업체의 추천!에 따라 필요한 재료와 뇌파 설치 준비nded 절차. 곧 참가자의 뇌파 소프트웨어를 준비합니다. 눈 추적 소프트웨어를 시작하고 소프트웨어의 새로운 참여자 프로파일을 생성합니다. 비디오 레코딩 소프트웨어 및 카메라를 시작. </li><li> 60 초에 마커 프로젝트에 대해 생성 된 특정 서브 루틴과 동기화 소프트웨어를 시작합니다. 생리 학적 측정 소프트웨어를 시작합니다 (피부 전기 활동을 기록하는) 프로젝트에 대해 생성 된 특정 레이아웃을 엽니 다. 가장 높은 수준으로 참가자의 의자를 조정합니다. </li></ol></li></ol> 2. 참가자 준비 <ol><li> 읽고 윤리적 동의서에 서명하기 위해 참가자를 요청합니다. </li><li> 뇌파에 대한 두개골 측정을 수행합니다 : <ol><li> (- 20 참조 시스템 (10)에 따라) 참가자의 머리에 Cz에 위치를 찾을 수 있습니다. 식염수 (염화칼륨)의 EEG 그물 담가 (단계 1.2.1 참조), 제조자의 기준에 따라 타이머 (10 분)을 시작한다. &lt;/ 리&gt; </ol></li><li> 연구의 목적 및 참가자에게 실험의 단계를 읽고, &quot;이 연구의 목적은 물리학의 문제에 대답하는 동안 뇌의 활동을 관찰하는 것입니다. 먼저 우리는 센서를 설치합니다, 당신은 컴퓨터에 10 뉴턴의 물리학 문제를 해결하라는 메시지가 표시됩니다. 우리는 당신의 눈을 가진 각각의 문제가 닫힌 후 45 초 휴식을 취하도록 요청합니다. 각각의 문제 후, 당신은 문제의 당신의 평가를 평가하게됩니다. &quot; </li><li> 실험의 총 지속 시간이 90 분 될 것입니다 제목을 알려주십시오. </li><li> 제조자의 권고에 따라, 생리 센서를 설치 두 gelified 센서 왼손 위에. </li><li> 제조업체의 권장 사항에 따라, 뇌파 캡을 설치하고 (제조업체의 사양에 따라) 40 kΩ의에서 임계 값과 임피던스 검사를 수행합니다. </li></ol> 3. 데이터 수집 <ol><li> 모든 기록 소프트웨어가 동시성에 시작 할 준비가되어 있는지 확인합니다 : <ol><li> 생리학 (EDA 데이터) : &quot;시작&quot;버튼을 클릭합니다. </li><li> 비디오 녹화 : &quot;열기&quot;버튼을 클릭합니다. </li><li> 눈 추적 : &quot;보류&quot;버튼을 클릭하십시오. </li><li> EEG : &quot;기록&quot;버튼을 클릭하십시오. </li><li> 동기화 소프트웨어 : &quot;녹색 원&quot;버튼을 클릭하십시오. </li></ol></li><li> 아이의 시선 교정 : <ol><li> 5 점에 화면 보정을 수행하고 그 / 그녀가 빨간 점을 다음과 동안 참가자를 관찰 ( &quot;도구 / 설정 / 교정을 ...&quot;클릭). 충분한 정확도가 달성 될 때까지 제조 업체의 기준에 따라,이 절차를 반복합니다. </li></ol></li><li> 참가자의 화면에 프로젝트 작업 지침 : 그 / 그녀가 그들을 읽은 후 질문이 있으면 물어, 그는 / 그녀는 실험을 시작할 준비가됩니다. </li><li> 10 뉴턴 P를 해결하기 위해 참가자를 요청hysics 문제. </li><li> 필요한 경우, 45 초 휴식의 일 동안 임피던스 검사를 수행 (5 전에없는 문제). </li><li> 확인 참가자가 각각의 문제 이전에 전체 45 초 휴식을 취합니다 (베이스 라인을 결정하기 위해). </li></ol> 데이터 수집 4. 끝 <ol><li> 모든 컴퓨터에 데이터 수집을 중지하고 참가자로부터 센서를 제거합니다. </li></ol> 5. 참가자가 떠난 후 <ol><li> 제조업체의 권장 사항에 따라, 살균제와 뇌파 캡을 청소하고 장비를 정돈. 수집 된 모든 데이터 파일을 저장하고 FTP 서버에 백업을 만들 수 있습니다. </li><li> 참가자 스프레드 시트를 입력 : 데이터 수집 중 특정 이벤트 나 문제가 있습니다. 웹 브라우저에서 모든 쿠키를 지 웁니다. </li></ol> 통합 소프트웨어 6. 데이터 전처리 및 내보내기 <ol start="1"><li> 뇌파 <ol><li> EEG 데이터 분석 소프트웨어로 가져 EEG 데이터 : <ol><li> 새로 생성 된 원시 데이터 파일에 원시 뇌파 데이터를 붙여 &quot;원시 데이터&quot;, &quot;역사&quot;와 &quot;수출&quot;라는 컴퓨터에 세 개의 빈 폴더를 만듭니다. </li><li> 뇌파 데이터 분석 소프트웨어에서 &quot;파일 / 새 프로젝트를 ...&quot;버튼을 클릭 한 후 새로 생성 된 원시 데이터 파일을 선택, 찾아보기를 클릭하여 원시 데이터 위치를 선택합니다. &quot;역사&quot;와 같은 방법으로 &quot;내보내기&quot;폴더의 위치를​​ 선택합니다. </li><li> &quot;확인&quot;을 클릭합니다. (윈도우는 모든 참가자의 EEG 데이터를 포함한다). </li></ol></li><li> 전처리 뇌 신호 : <ol><li> ( &quot;변환 / IIR 필터를 ...&quot;클릭) 필터 및 노치 (notch)를 적용합니다. 창에서 12 dB의 기울기와 12dB의 기울기 50 Hz에서 높은 컷오프 1.5 Hz에서 낮은 컷오프 수 있습니다. 또한 60 Hz의 주파수에서 노치 수 있습니다. </li><li> DC 증폭기를 사용하기 때문에,이 DC 신호 detrend ( &quot;트라을 클릭마커 전에 100 밀리 초와 DC 연결 전에 100 밀리 초)에서 nsformations 시간을 기준으로 / DC Detrend ... &quot;및 사용&quot; &quot;. </li><li> 원시 데이터 검사를 수행합니다 ( &quot;변환 / 원시 데이터 검사를 ...&quot;를 클릭하고 반자동 아티팩트 제거를 선택합니다). 최대 전압 60 μV / MS] 다음을 선택 최대 분 : 200 밀리 초 간격으로 200 μV; 진폭 : -400 400에 μV). </li><li> 눈 깜박임 제거를위한 고전적인 구형과 자동 ICA를 수행합니다 (그 범위는 관심있는 주파수의 외부에 있기 때문에 myographic 유물을 제거 할 필요가 없습니다). (ICA, 프로세스 역 ICA의 끝에서. &quot;... 변환 / ICA&quot;를 클릭하십시오.) </li><li> 다시 참조 ( &quot;변환 / 다시 참조 ...&quot;) 신호는 &quot;일반적인 평균&quot;을 선택합니다. </li><li> 수출 ( &quot;수출 / 일반 데이터 내보내기를 ...&quot;클릭) 텍스트 형식의 신호 및 마커 (선택 &quot;.vhdr&quot;; 결합 인덱스의 최종 matlab에 건설 상자). 또한 &quot;쓰기 헤더 파일&quot;및 &quot;쓰기 마커 파일&quot;상자를 선택합니다. </li></ol></li><li> 매트랩 신호를 가져옵니다. <ol><li> matlab에를 시작하고 EEGLab의 GUI를 한 번에 하나의 참여자에 대한 데이터를 표시하고 가져 오기 때문에 &quot;eeglab&quot;를 입력합니다. GUI에서 선택 항목 메뉴 &quot;EEGLab 기능과 플러그인을 사용하여 파일 / 데이터 가져 오기 / / 뇌 마주 녹화에서 .vhdr 파일&quot;. </li><li> 명령 창에서, 결합 인덱스를 생성하는 스크립트 (16)를 붙여 넣습니다. 주 :인지 결합 스크립트이 절차 20 초 슬라이딩 윈도우 선행 시간 T. 내의 각 베타 / (알파 + 쎄타) 비율의 평균으로 계산되고 초마다 새로운 슬라이딩 윈도우가 인덱스를 업데이트하는 데 사용되는 반복된다. </li></ol></li><li> MS 엑셀에서 매트랩하여 스크립트의 끝에서 생성된다 결합 지수의 텍스트 파일을 열고, Z SCOR 적용뇌파 데이터에 전자 정상화 intersubject 비교를 허용합니다. (각 값에 대해, Excel에서이 수식을 계산 : Z = (값 - 전체 평균) / 전체 표준 편차를.) </li><li> MS 엑셀에서 CSV 파일의 Z 점수 약혼 인덱스 신호를 저장합니다. (...로 저장하고 형식 유형에 CSV를 선택 / 파일을 클릭합니다.) </li><li> (단계 6.1.2.2에서.) 과정을 반복 각 참가자에 대해. </li></ol></li><li> 생리학 : <ol><li> 생리 학적 데이터 분석 소프트웨어 가져 오기 EDA 데이터. </li><li> 생체 신호를 사전 처리하려면이 매개 변수를 적용합니다 : <ol><li> 베너 블스와 크리스티 (23) 방법에 따라 전기 전도도의 분포를 정상화 로그 변환을 적용합니다. </li><li> 창 24 슬라이딩 10 초 플랫 신호. </li></ol></li><li> 생리 소프트웨어 내 intersubject 비교를 허용하도록 EDA 데이터에 Z-점수 정규화를 계산. (Z = (값 - 전체 평균) / 전체 표준 편차). <ol><li> EDA 채널에서 커서 모든 데이터를 선택합니다. </li><li> 톱 메뉴에서 EDA 채널을 선택하고 전체 채널의 평균값을 얻기 위해 &quot;평균&quot;를 선택한다. 또한 전체 채널의 표준 편차 값을 획득하기 위해 채널 및 EDA &quot;STDDEV&quot;를 선택한다. </li><li> Z 점수 방정식을 계산하기 위해 &quot;변환 / 파형 연산을 ...&quot;버튼을 클릭 소스 1의 EDA 채널을 선택합니다. &quot;-&quot;를 선택 수학 연산 창에 (마이너스) 및 대상 메뉴에서 &quot;새 대상&quot;소스 2.에 K를 선택하고 EDA 채널의 평균 값을 입력한다 (단계 6.2.3.2 참조). &quot;전체 파장 변환&quot;을 선택, 확인을 클릭하고 &quot;변환 / 파형 연산을 ...&quot;을 클릭합니다. 선택 소스 1의 EDA-K 채널을 선택합니다 &quot;/&quot;수학 연산 창에서 (나누기), 소스 (2)에서 선택 K는 목적지와 강한 게 아니에 &quot;새로운 대상&quot;을 선택EDA 채널 (단계 6.2.3.2)의 표준 편차 값을 r에. 선택 &quot;전체 파장 변환&quot;하고 확인을 클릭합니다. </li></ol></li><li> CSV 파일의 신호 (각성)를 내 보냅니다. (...로 저장하고 형식 유형에 CSV를 선택 / 파일을 클릭합니다.) </li></ol></li><li> 자동 안면 인식 감정 : <ol><li> 자동 얼굴 감정 인식 소프트웨어로 미디어 레코더에서 비디오 가져 오기 데이터. (다음 분석 1에 돋보기를 클릭하십시오. &quot;... 파일 / 새 / 분석 / 비디오&quot;. 그것을 클릭하여 프로젝트 메뉴에서 새 참가자를 선택한 후 클릭 &quot;... 파일 / 새 ... / 참가자&quot;를 클릭하고 원하는 선택 비디오 파일. <ol><li> &quot;모든 세 번째 프레임&quot;에 대한 오프라인 분석을 선택하고 &quot;지속적인 교정&quot;을 활성화합니다. </li><li> CSV 파일 내보내기 원자가 데이터. (/. 상자 &quot;로그 파일에 원자가 값을 쓰기&quot;를 클릭 &quot;파일을 확인,&quot;... 옵션 / 설정 / 로깅 &quot;을 클릭수출 ... 자세한 로그 &quot;상자를 저장&quot;, 로그 파일을 내보낼 위치를 선택하고 &quot;.) </li><li> MS Excel에서 CSV 파일을 엽니 다. SPSS 소프트웨어의 단일 열에서 원자가 데이터 열을 복사합니다. &quot;분석한다 / Descriptives 통계 / Descriptives&quot;버튼을 클릭 방금 붙여 변수 이름을 선택합니다. &quot;변수에 표준화 된 값을 저장&quot;확인란을 선택합니다. Z 점수와 열이 나타납니다. Excel 파일에있는 오래된 데이터에 이러한 Z-점수를 복사 - 붙여 넣습니다. </li></ol></li><li> CSV 형식의 신호 (원자가)의 z 점수 Excel 파일을 저장합니다. </li></ol></li></ol> 7. 데이터 통합​​ 및 동기화 <ol start="1"><li> 행동 분석 소프트웨어에서 : <ol><li> 가져 오기 눈 추적 비디오 (행동 참여). (. &quot;... 새 관측에 파일 / 가져 오기 / 비디오&quot;를 클릭하여 새 관찰의 이름을 지정하고 원하는 비디오 파일을 선택합니다.) </li><li> 코드 관련 BE와 각 비디오haviors과 상황에 맞는 이벤트 (시간 마커, 좌 / 오답). </li><li> 적절한 헤더로 모든 외부 데이터 가져 오기 : 뇌파 신호 (인지 참여)의 Z 점수, EDA 신호 (감정적 인 참여), 원자가 데이터의 Z 점수 (감정적 인 참여)의 Z 점수. (. &quot;... 파일 / 가져 오기 / 외부 데이터&quot;를 클릭하여 해당 파일 유형을 선택하고 올바른 CSV 파일을 선택합니다.) </li></ol></li><li> 이 공식에 따라 컴퓨터 간의 시간을 동기화 : <ol><li> 시선의 첫 번째 마커 - 눈 시간 시선 + 두 번째 마커 뇌파에서의 뇌파 = 시간의 시간에서 시선. </li><li> 시선의 첫 번째 마커 - 눈의 시선 + 처음 마커 얼굴 감정에 얼굴 감정 인식 = 시간의 시간에서 시선 시간. </li><li> 시선의 첫 번째 마커 - 눈 시간 피부 전기 활동 시선 + 첫 번째 마커에 = 시간 피부 전기 활동 시간에서 시선. </li></ol></li><li> &quot;눌러 오프셋 데이터를 입력합니다 Ctrl 키를 + Shift + =221 ;,는 오프셋 메뉴를 엽니 다. 선택 &quot;수치 오프셋&quot;데이터 소스의 각 쌍 사이의 시간 (초)을 입력합니다 [OK?]) 위의 계산에 따르면. </li><li> 연구에 대한 관심의 변수에 따라 보고서를 생성합니다. <ol><li> 보고서에 생성됩니다 흥미로운 변수를 선택합니다 ( &quot;/ 데이터 선택 / 새 프로파일 데이터 분석 ...&quot;클릭). 왼쪽에서 오른쪽, &quot;시작&quot;상자와 &quot;결과&quot;상자 사이에 원하는 변수를 밀어 넣습니다. </li><li> 보고서를 생성합니다. ( &quot;통계&quot;를 클릭 &quot;... / 수치 해석 / 새로운 분석&quot;을 클릭하고 &quot;계산&quot;을 클릭하여 외부 데이터 메뉴. 마침의 평균 상자를 선택합니다.) </li></ol></li><li> 통계 분석 소프트웨어로 데이터를 내보내고 연구 목적에 따라 분석을 수행합니다. </li></ol>

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The authors have nothing to disclose.

프로토콜 내에서 중요한 단계의 관점에서, 첫번째 데이터 품질은 신경 생리 컬렉션 기법의 주된 초점은 항상 지적되어야한다. 이 방법에서는, 연구 조수 (카메라 올바른 얼굴 각도를 잃고) 원자가 모니터링을 방해하거나 뇌파에 myographic 아티팩트를 생성 머리의 움직임을 최소화하기 위해 주제를 지시에 특별한주의를 지불해야합니다. 한편, 균형은 더욱 인간 공학적 데이터 수집을 위해 만든 실제...

참여 학습에 중요한 역할을한다. 클라크와 메이어 2의 경우에 관계없이 전달 매체의 &quot;모든 학습, 참여가 필요합니다.&quot; 장 등. 3도 증가 학생의 참여는 문제 해결과 비판적 사고 능력 등의 ​​학습 성과를 향상시킬 수 있습니다 제안했다. 참여를 정의하는 것은 과제로 남아. 그들의 문헌 고찰에서, Fredricks, Blumenfeld 파리 1은 다각적 인 성격 참여를 정의 : &quot;행동 참여는 참여의 아이디어에 그립니다; 그것은 학문적, 사회적 또는 과외 활동에 참여를 포함한다. (...) 감정적 참여 교사, 급우, 학자, 학교에 긍정적이고 부정적인 반응을 포함하고 일을하는 목적과 영향력 의지에 관계를 만들 추정된다. 마지막으로,인지 적 참여 정신 투자의 아이디어에 그립니다; 그것은 사려 깊음과 노력 NEC를 발휘하는 의지를 통합essary 복잡한 아이디어와 마스터 어려운 기술을 이해합니다. &quot; Fredricks, Blumenfeld 파리 1은 행동, 감정 및인지에 초점이, ​​참여의 개념 내에서 학습의 풍부한 특성을 제공 할 수 있다고 주장했다. 이 저자는 연구의 강력한 몸이 따로 계약의 각 구성 요소를 해결할 수 있다고 지적하지만, 이러한 구성 요소는 함께 연구되지 ​​않았다. 또한 정보가 거의 치수 사이에 더 많은 연구가 미세하게 조정 된 교육 개입 계획에 기여할 수있는 상호 작용에 대해 사용할 수 있음을 관찰했다. 그 방향으로 단계로,이 논문 수집하고 학습을 작업하는 동안, 행동, 정서적,인지 적 참여에 대한 동기 양적 및 질적 데이터를 분석하기 위해 개발 된 연구 방법을 설명합니다. 교육에 신경 과학을 현실로 Behav주로 사전 및 사후 검사 사이의 오랜 기간에 걸쳐 발생하는 지식과 행동의 변화에​​ 초점을 맞춘 연구 설계, 그리고 시간의 간격, 주간 : IOR, 결과적으로 행동 참여는 긴 교육 연구의 핵심이되었습니다 , 달 또는 몇 년. 마지막 두 치수는 외부 체계적으로 관찰 할 수 없기 때문에, 행동 적, 정서적,인지 적 결합을 구별하는 것은 과제로 남아. 인지와 감정 중 하나를 관찰로부터 추론 또는 자기보고 대책을 평가해야합니다. 보기의 외부 점에서, 그것은 학생들이 작업을 가능한 한 빨리 또는 특정 콘텐츠를 마스터 깊은 수준의 학습 전략을 사용하는 등 완수하기 위해 노력하고 있는지 여부를 확인하기 어려운 남아있다. 사실 점에서, Fredricks, Blumenfeld 파리 1은인지 적 참여의 직접, 객관적 조치를 사용하여 발표 된 연구를 찾을 수 없습니다. 최근 기술 개발신경 과학 분야의 교육 연구에 대한 새로운 가능성을 만들었습니다. 새로운 데이터 수집 방법과 신경 인간 공학의 분야에서 개발 된 분석 알고리즘 학습 작업하는 동안 정성 및 정량 연구를위한 매우 유망한 보인다. 경제학, 심리학, 마케팅, 인체 공학 같은 다른 분야는 약간의 시간이 4-8에 대한인지 적 참여를 평가하기 위해 신경 생리 학적 측정을 사용하고있다. 효율적인 분석 알고리즘과 결합 된 신경 생리 학적 측정은, 하나를 교란시키지 않고 현상을 연구 할 수있다. 자신의 성격으로 자기보고 설문 학습에서 학생들을 분리. 신경 생리학 대책 연구 디자인은 더 확실한 학습 환경에서 수행되도록 허용한다. 이러한 도구 등 심박수를 모니터링 장비, 호흡 속도, 혈압, 체온, 동공 직경, 피부 전기 활동, 뇌파 (EEG)를 포함한다. <st감정적 행동에 룽&gt; 수집 동기화 된 데이터,인지 참여 대표 결과이 프로토콜의 사용을 다음과 같이, 본 논문에서는 학습자가 컴퓨터 화면에, 기계 물리학 열 문제를 해결했다있는 연구의 일부 결과를 발표 할 예정이다. 이러한 문제는 이전의 연구 (9) 개발되었다. 그들의 눈은 각각의 문제 후, 폐쇄와 학습자, 문제를 해결하고 45 초 휴식 시간 동안 휴식을하는 동안 신경 생리 학적 데이터를 수집 하였다. 위에서 언급 한 바와 같이, 행동 약혼 데이터는 작업 일을 달성하는 동안 시스템과 상호 작용하는 학습자에 의해 생성 된 질문에 대한 소프트웨어의 상호 작용 (마우스 움직임과 클릭), 시선, 성능과 답변으로 구성되어 있습니다. 안구 추적 시스템은 소프트웨어 상호 작용과 시선 데이터를 수집하기 위해 사용되었다. 성능 데이터 (문제를 해결하기 위해 시간, 응답의 정확성)을 수집 하였다과제를 제시하는 데 사용 된 조사 사이트. 이 웹 사이트는 또한 브래들리와 랭 (10)에서 적응 설문 조사와 수집 된 자기보고 데이터를 수집하는 데 사용되었다. 감정적 참여 감정의 특성을 포함한다. 랭 (11)에 따르면, 감정 원자가의 관점에서 특징 (즐거운 / 불쾌한)과 각성 (진정 / 자극). 감정적 결합 데이터는 따라서 감정적 원자가와 각성 12,13 용 피부 전기 활동 인코더 / 센서를 정량화 자동 안면 감정 인식 소프트웨어를 사용하여 수집 하였다. 피부 전기 활동 (EDA)는 매우 약한 전류가 지속적으로 그들 사이에 전달되는 두 개의 전극 사이에 기록 된 전기 저항을 의미한다. Cacioppo, Tassinary 및 Berntson (14)는 기록 된 저항 주체의 각성에 따라 다릅니다 것으로 나타났다. 따라서, 이러한 원자가 또는 각성 등의 정신 생리 학적 데이터는, 감정적 인 참여의 상관 관계로 간주됩니다. <p클래스 = &quot;jove_content은&quot;&gt; 마지막으로,인지 약혼 데이터는 뇌파 (EEG)를 통해 수집된다. 뇌파 측정은, 두피에, 뇌의 뉴런 그룹의 전기 활동을 동기화. 두피에서 기록 된 전기 신호들은 진동 및 주파수 요소로 구성된다. 규칙에 따라,이 주파수는 밴드로 알려진 시퀀스로 분류됩니다. 예를 들어, 알파, 베타 및 세타 밴드는이 연구의 초점이다. 신경 과학적 연구 (14)에 따르면,이 대역은 뇌의 특정 영역에 서로 다른인지 적 처리 능력을 반영한다. 따라서, 주의력 및 관심에 많은 연구와 결합 7,15 특정 주파수의 전력 스펙트럼 밀도 (PSD)의 분석은, 연구자들은 작업 도중인지 결합을 정량화 할 수있다. Mikulka 등. (16)가 언급 한 바와 같이, 연구는 베타 활동과인지 경보 및 알파 사이의 간접적 인 관계 사이의 직접적인 관계를 보여 주었다D 세타 활동 및 경보. 베타 / (알파 + 세타) : 따라서, 교황, 보가트와 Bartoleme 7 세 밴드의 PSD를 계산 약혼 지수를 개발했다. 이 비율은 참여 16,17,18 다른 연구에서 확인되었다. 시간이 지남에 따라인지 맞물림을 특성화하기 위해, 고속 푸리에 변환 (FFT)는 파워 스펙트럼으로 각 활성 부위 (F3, F4, O1, O2)의 EEG 신호를 변환한다. 시간 T에서 EEG 결합 지수는이 절차는 매 초마다 새로운 슬라이딩 윈도우가 인덱스를 업데이트하는 데 사용되는 반복 시간 T. 선행 20 초 슬라이딩 윈도우 내의 각각의 결합 비의 평균값으로 계산된다. 이 방법의 목적은 결합의 여러 치수 풍부한 분석을 제공하기 때문에, 데이터 동기화가 중요하다. 레제 등. (19)가 독자를 생각 나게으로, 장비 제조 업체는 강하게 제작 그들의 지정된 정밀도를 보장하기 위해 측정 도구 당 하나의 컴퓨터를 사용하는 것이 좋습니다VEL. 따라서, 여러 대의 컴퓨터를 사용하는 경우, 기록 시스템 간의 동기화는 중요한 단계가된다. 녹음은 모두 동일한 시점에 개시 될 수 없으며, 각각의 데이터 스트림 (예를 들면, 눈의 0 초 EEG 또는 생리 데이터의 초 0 ≠ 트래킹)는 특정 기간을 갖는다. 이것은 매우 중요하다 : 데이터 스트림들 사이의 맞물림은 동기 이탈의 각 차원의 정량의 오차를 의미한다. 동시 생리 및 행동 기록을 동기화하는 여러 가지 방법이 있습니다. 이러한 방법은 두 가지 방식으로 나눌 수있다; 직접 및 간접 (20). 다음 절에서 제시된 프로토콜은 (도 1에 도시 된 바와 같이) 외부 장치가 syncbox, 모든 기록 장치에 트랜지스터 - 트랜지스터 논리 (TTL) 신호를 전송하는 데 사용되는 간접적 인 접근법에 기초한다. 기기의 각 부분은 서로 다른 시작 시간을 같이, TTL 마커 상대적으로 로그 파일에 기록된다<html> 지연. 마커는 그 다음 신호를 재정렬함으로써 각각의 기록 후에 적절한 동기화를 보장하기 위해 사용된다. 외부 파일의 통합을 허용 행동 분석 소프트웨어 프로그램은 각각의 데이터 스트림의 타임 라인을 재 동기화 및 맞물림의 각 차원의 정성 및 정량 분석​​을 위해 사용된다. <img alt="그림 1" src="/files/ftp_upload/52627/52627fig1.jpg" /> 데이터 수집 시스템의 그림 1. 아키텍처. 테스트 환경은 행동 (눈 추적)에서, 수집 감정 (EDA와 얼굴 감정),인지 (EEG) 약혼 데이터는 많은 컴퓨터가 포함되어 있습니다. 이것은 각각의 컴퓨터 시계에 참조되는 데이터에 대한 동기화 문제를 발생시킵니다. 동일 기준 시간에 모든 데이터를 분석 할 수있게하기 위해서, 실험 셋업은 모든 데이터 스트림들에 TTL 신호를 송신 syncbox을 포함한다.&quot;NK&gt;이 그림의 더 큰 버전을 보려면 여기를 클릭하십시오. 동기화 환산 방법의 정밀도를 평가하기 위해 45 초의 일시 정지 기계적 물리 문제 각각 전에 도입되었다. 이러한 일시 정지하는 동안, 대상은 긴장을하고 자신의 눈을 감고 있었다. 다른 연구 4,9,16,17,18에서 볼 수 있듯이,이 일시 정지 수집 된 신호에 상당한 변화를 유도해야한다 : 두 눈의 동공 점 눈 추적 사라 즉시 (행동 참여)과인지 적 참여에 즉시 드롭 (뇌파 신호)이 관찰된다. 이러한 특정 신호의 구성 요소의 동기화 장군 유효성을 평가하는 데 사용된다. 완전히 또는 부분적으로 정보 시스템 (19), 인간 - 기계 상호 작용 (21), 교육 (9), (22)의 분야에서,이 동기화 절차에 의존 논문의 최근 간행물은, 그 효과의 증거를 제공한다.

<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> 행동 분석 소프트웨어 어플리케이션에 통합하고, 감정적 인 행동인지 결합 데이터의 동기화의 결과를도 2 및도 3의 스크린 샷을 도시한다. 양 도면에서, 좌측 부분은 조사 대상 및 코딩 방식을 조직. 중간 부분에서 (붉은 점)가 비디오를 작업하는 동안 피사체의 눈에 시선을 보여줍니다. 피사체의 행동 참여는 촬영 그가 / 그녀가 작업하는 동안보고되고있는 작업을 기...

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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.

참여의 여러 차원을 평가하는 것은 학습의 특성을 : 신경 생리학 관점

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

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Behavior

12.4K 조회수.  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. 

비디오: 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;

마우스의 단일 펠릿 도달 작업을 통한 학습 연구 모터 기술

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

연구

행동분석학

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.

신경 과학에 대한 관심

생물학

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.

인식 및 학습을 연구하는 도구로 전기 생리 텍스 녹화 및 자극과 셔틀 박스 교육을 결합

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.

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

신경과학

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.

개방형 협업 학습의 뇌 간 싱크로니: fNIRS 하이퍼스캐닝 연구

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.

목표 지향적 도달 행동 중 피질 척수 흥분성 평가

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

Learning and Memory 소개

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

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JoVE Science Education

심리학

행동과학

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

신경 생리학 소개

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

심화생물학

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.

신경 심리학에 대한 관점

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

신경 심리학

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.

인지 심리학에 대한 관점

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

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