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).

Este protocolo recebeu um certificado de ética do Comité de la recherche institutionnel avec des êtres humains (CIER) de l&#39;Université du Québec à Montréal (UQAM), que foi aprovado pelo HEC-Montreal para o centro de pesquisa Tech3Lab. O protocolo descreve cada um dos passos específicos que são realizados em nosso meio ambiente e equipamentos de laboratório. Embora caminhos de software precisos são fornecidos para esclarecer a metodologia, esta técnica é transferível e pode ser replicado com outros eye-tracking proprietário, automático reconhecimento de emoções faciais, atividade electrodermal e equipamentos eletroencefalografia e software. 1. Configuração do Laboratório de Meio Ambiente <ol><li> Ligue o olho-tracker, o amplificador de EEG, os quatro computadores de gravação e alto-falantes. </li><li> Prepare a instalação do aparelho de controlo: <ol><li> Prepare a configuração do EEG com material necessário de acordo com recome do fabricanteprocedimentos NDED. Prepare o software EEG para o próximo participante. Inicie o software de rastreamento ocular e criar um novo perfil de participante do software. Inicie o software de gravação de vídeo e as câmeras. </li><li> Inicie o software de sincronização com o sub-rotina específica criada para o projeto com marcadores em 60 seg. Inicie o software de medição fisiológica (para gravar a atividade electrodermal) e abra o layout específico criado para o projeto. Ajuste cadeira do participante ao mais alto nível. </li></ol></li></ol> 2. Participante Preparação <ol><li> Peça ao participante para ler e assinar o formulário de consentimento ético. </li><li> Realizar medições do crânio para EEG: <ol><li> Encontre o local Cz na cabeça do participante (de acordo com 10-20 sistemas de referência). Mergulhe o líquido EEG na solução salina (cloreto de potássio) (veja o passo 1.2.1) e iniciar um temporizador (10 min), em conformidade com as normas do fabricante. &lt;/ Li&gt; </ol></li><li> Leia o objetivo do estudo e as etapas do experimento para o participante, &quot;O objetivo deste estudo é observar sua atividade cerebral enquanto você responder a problemas de física. Primeiro vamos instalar os sensores, em seguida, você será solicitado a resolver 10 problemas de física newtoniana no computador. Vamos pedir-lhe para tomar uma 45-sec pausa depois de cada problema com seus olhos fechados. Depois de cada problema, você será solicitado para avaliar a sua avaliação do problema &quot;. </li><li> Diga o assunto que a duração total do experimento será de 90 min. </li><li> Instalar os sensores fisiológicos, de acordo com as recomendações do fabricante: dois sensores gelificados no topo do lado esquerdo. </li><li> Instale a tampa do EEG, de acordo com as recomendações do fabricante e executar uma verificação de impedância com um limiar de 40 kW (de acordo com as especificações do fabricante). </li></ol> 3. Coleta de Dados <ol><li> Certifique-se de que todos os softwares de gravação está pronto para ser iniciado em sincronia: <ol><li> Fisiologia (dados EDA): Clique no botão &quot;Iniciar&quot;. </li><li> A gravação de vídeo: Clique no botão &quot;Abrir&quot;. </li><li> Eye-tracking: Clique no botão &quot;em espera&quot;. </li><li> EEG: Clique no botão &quot;record&quot;. </li><li> O software de sincronização: Clique no botão &quot;círculo verde&quot;. </li></ol></li><li> Olho calibração olhar: <ol><li> Realize uma calibração de cinco pontos na tela e observe o participante enquanto ele / ela segue os pontos vermelhos (clique em &quot;Ferramentas / Configurações / Calibração ...&quot;). Repita este procedimento até que uma precisão suficiente é alcançada, de acordo com as normas do fabricante. </li></ol></li><li> Instruções de tarefas do projeto na tela do participante: perguntar se ele / ela tem alguma dúvida após lê-los, e se ele / ela está pronto para iniciar a experiência. </li><li> Peça ao participante para resolver 10 newtoniana physics problemas. </li><li> Se necessário, execute uma verificação de impedância durante um dos intervalos 45 s (não antes de problema 5). </li><li> Certifique-se de que o participante assume a completa 45 s pausa antes de cada problema (para determinar a linha de base). </li></ol> 4. Fim de Coleta de Dados <ol><li> Pare de aquisição de dados em todos os computadores e remover os sensores do participante. </li></ol> 5. Depois de o Participante Has Left <ol><li> Limpe a tampa EEG com germicida e arrumar o equipamento, de acordo com as recomendações do fabricante. Salve todos os arquivos de dados coletados e criar um backup no servidor de FTP. </li><li> Preencha a planilha participante: observe qualquer evento particular ou problema durante a coleta de dados. Apagar todos os cookies do navegador da web. </li></ol> 6. Dados Pré-processamento e exportação para o Software de Integração <ol start="1"><li> EEG <ol><li> Importar dados de EEG em EEG software de análise de dados: <ol><li> Crie três pastas vazias no computador chamado &quot;dados brutos&quot;, &quot;História&quot; e &quot;Exportar&quot; para colar os dados de EEG matérias no arquivo de dados Raw recém-criado. </li><li> No EEG software de análise de dados, clique em &quot;File / New Project ...&quot; e escolha o local de dados brutos, clicando em Procurar e selecionando o arquivo de dados brutos recém-criado. Escolha o local do &quot;Histórico&quot; e as pastas &quot;exportação&quot; da mesma forma. </li><li> Clique em &quot;OK&quot;. (A janela deve conter todos os dados de EEG do participante). </li></ol></li><li> Pré-processo o sinal do cérebro: <ol><li> Aplicar um filtro e um entalhe (clique em &quot;Transformações / filtros IIR ...&quot;). Na janela, permitir que o corte de baixa de 1,5 Hz com um declive de 12 dB eo alto ponto de corte em 50 Hz com um declive de 12 dB. Também permitem que um entalhe a 60 Hz de frequência. </li><li> Porque um amplificador DC é usado, DC retirada da tendência de sinal (Clique em &quot;Transformations / DC Eliminar tendência ... &quot;e permitir&quot; baseadas em tempo &quot;em 100 milissegundos antes do marcador e 100 ms antes de conexão DC). </li><li> Faça uma inspeção de dados brutos (Clique em &quot;Transformação / Raw inspeção de dados ...&quot; e selecione a remoção artefato semi-automático). Selecione as seguintes: tensão máxima de 60 mV / ms; Max-min: 200 mV em 200 intervalo ms; amplitude: -400 a +400 mV). </li><li> Executar uma ICA automática com esferificação clássico para remoção piscar de olhos (artefatos miográfica não precisa ser removido porque a sua gama está fora das frequências de interesse). (Clique em &quot;Transformações / ICA ...&quot;. No final do ICA, o processo inverso ICA). </li><li> Re-referência (&quot;Transformações / Re-referência ...&quot;) o sinal e selecione &quot;média comum&quot;. </li><li> Export (clique em &quot;Export / Genéricos Data Export ...&quot;) o sinal e marcadores em formato de texto (Selecione a opção &quot;.vhdr&quot;; caixa) para um eventual construção Matlab do índice de noivado. Além disso, selecione o arquivo &quot;cabeçalho Write&quot; e &quot;caixas de arquivo marcador Write&quot;. </li></ol></li><li> Importar o sinal em Matlab. <ol><li> Comece Matlab e digite &quot;eeglab&quot; para que o GUI de EEGLab aparece e importar os dados para um participante de cada vez. Na GUI, selecione item de menu &quot;Arquivo / Importar Dados / Usando funções EEGLab e plugins / Do Cérebro Vis Rec .vhdr arquivo&quot;. </li><li> Na janela de comando, cole um script 16 que gera um índice de noivado. NOTA: O script envolvimento cognitivo é calculado pela média de cada / rácio Beta (Alpha Theta +) dentro de um tempo T. janela deslizante 20 seg anterior Este procedimento é repetido a cada segundo e uma nova janela deslizante é usado para atualizar o índice. </li></ol></li><li> No MS Excel, abra o ficheiro de texto do índice de acoplamento que é gerado no final da escrita por Matlab e aplicar um z-SCORe normalização em dados de EEG para permitir a comparação intersubject. (Para cada valor, calcular esta fórmula no Excel: Z = (valor - média geral) / desvio padrão global.) </li><li> Salve o sinal indicador de engajamento z-score em um arquivo CSV em MS Excel. (Clique em Arquivo / Salvar como ... e selecione CSV no tipo de formato.) </li><li> Repetir o procedimento (a partir do passo 6.1.2.2.) Para cada participante. </li></ol></li><li> Fisiologia: <ol><li> Importar dados EDA em software de análise de dados fisiológicos. </li><li> Aplicar estes parâmetros pré-processar o sinal fisiológico: <ol><li> Aplicar uma transformação logarítmica a normalizar a distribuição da condutância como por Venables e Christie 23 método. </li><li> Plano do sinal numa janela de 10 seg 24 deslizante. </li></ol></li><li> Dentro do software fisiológico, calcular uma normalização z-score com os dados de EDA para permitir a comparação intersubject. (Z = (valor - média geral) / desvio padrão global). <ol><li> Realce todos os dados com o cursor do canal EDA. </li><li> No menu superior, selecione o canal EDA, e selecione &quot;significa&quot; para obter o valor médio do canal global. Também seleccionar o canal de EDA e &quot;o desvio-padrão&quot; para obter o valor do desvio padrão do canal global. </li><li> Para calcular a equação z-score, clique em &quot;Transformação / forma de onda matemática ...&quot; e selecione o canal EDA na Fonte 1. Selecione &quot;-&quot; (menos) na janela de operação matemática e escolha K na fonte 2. Selecione &quot;Novo destino&quot; no menu de destino e insira o valor médio do canal EDA (veja o passo 6.2.3.2). Selecione &quot;Transform onda inteira&quot;, clique em OK e clique em &quot;Transformação / forma de onda matemática ...&quot;. Selecione o canal EDA-K na fonte 1, selecione &quot;/&quot; (dividir) na janela de operação matemática, selecione K na fonte 2, selecione &quot;Novo destino&quot; em destino e enter do valor do desvio padrão do canal de EDA (passo 6.2.3.2). Selecione &quot;Transformar onda inteira&quot; e clique em OK. </li></ol></li><li> Exportar o sinal (excitação) em um arquivo CSV. (Clique em Arquivo / Salvar como ... e selecione CSV no tipo de formato.) </li></ol></li><li> Automatic facial emoção reconhecimento: <ol><li> Importar dados de vídeo do gravador de mídia no software de reconhecimento facial automático emoção. (Clique em &quot;Arquivo / Novo ... / participantes ...&quot;. Depois de selecionar um novo participante no menu projeto, clicando sobre ela, clique em &quot;Arquivo / Novo / Análise / Video ...&quot;. Clique na lupa ao lado de Análise 1 e escolher o desejado arquivo de vídeo. <ol><li> Selecione uma análise offline para &quot;cada terceira frame&quot; e ative &quot;calibração contínua&quot;. </li><li> Exportar dados de valência em um arquivo CSV. (Clique em &quot;Opções / Configurações / Logging ...&quot;, marque a opção &quot;Escrever valor de valência para o arquivo de log&quot; caixa. Clique em &quot;Arquivo /Export ... &quot;, escolha o local onde os arquivos de log serão exportados, e marque a opção&quot; Salvar registro detalhado caixa &quot;.) </li><li> Abra o arquivo CSV em MS Excel. Copie a coluna de dados de valência em uma única coluna de software SPSS. Clique em &quot;Analyse / Descriptives Estatísticas / Descriptives&quot; e selecione o nome da variável recém-colado. Marque a caixa &quot;Salvar valores padronizados em variáveis&quot;. Uma coluna com um z-score irá aparecer. Copie e cole esses escores z ao longo dos velhos dados no arquivo Excel. </li></ol></li><li> Salve o arquivo do Excel com os escores z do sinal (valência) em formato CSV. </li></ol></li></ol> 7. Data Integration e sincronização <ol start="1"><li> Em software de análise comportamental: <ol><li> Importação eye-tracking vídeos (de noivado comportamental). (Clique em &quot;Arquivo / Importar / Video em uma nova observação ...&quot;. O nome do novo observação e escolha o arquivo de vídeo desejado.) </li><li> Código cada vídeo com ser pertinentehaviors e eventos contextuais (marcadores de tempo, respostas certo / errado). </li><li> Importar todos os dados externos com o cabeçalho apropriado: z-score do sinal EEG (envolvimento cognitivo), z-score de sinal EDA (envolvimento emocional), z-score de dados de valência (envolvimento emocional). (Clique em &quot;Arquivo / Importar / dados externos ...&quot;. Selecione o tipo de arquivo apropriado e selecione o arquivo CSV correta.) </li></ol></li><li> Sincronizar o tempo entre os computadores de acordo com estas fórmulas: <ol><li> Tempo no olho olhar de vez em EEG = Tempo no olhar olho + segundo marcador no EEG - primeiro marcador no olhar olho. </li><li> Tempo no olhar olho de vez em emoção facial reconhecimento = Tempo no olhar olho + primeiro marcador na emoção facial - primeiro marcador no olhar olho. </li><li> Tempo no olho olhar de vez em atividade electrodermal = Tempo no olhar olho + primeiro marcador na atividade electrodermal - primeiro marcador no olhar olho. </li></ol></li><li> Digite os dados de correção pressionando &quot;Ctrl + Shift + =221 ;, para abrir o menu de deslocamento. Selecione &quot;deslocamento numérica&quot; ​​para entrar no tempo em segundos entre cada par de fontes de dados [OK?]), De acordo com os cálculos acima referidos. </li><li> Gerar um relatório de acordo com as variáveis ​​de interesse no estudo. <ol><li> Selecione variáveis ​​interessantes que serão gerados no relatório (clique em &quot;Analisar / Select Data / Novo Perfil de Dados ...&quot;). A partir da esquerda, deslize as variáveis ​​desejadas entre a caixa &quot;Iniciar&quot; e &quot;Resultados&quot; caixa, no lado direito. </li><li> Gerar o relatório. (Clique em &quot;Analisar / Análise Numérica / New ...&quot;, clique em &quot;Estatísticas&quot; e marque a caixa média no menu de dados externa. Concluir, clicando em &quot;Calcular&quot;.) </li></ol></li><li> Exportar os dados para software de análise estatística e realizar a análise de acordo com os objetivos do estudo. </li></ol>

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The authors have nothing to disclose.

Em termos de etapas críticas no âmbito do protocolo, ele deve primeiro ser salientado que a qualidade dos dados é sempre o foco principal para técnicas de coleta de neurofisiológicos. Nesta metodologia, assistentes de investigação devem prestar especial atenção à instrução dos assuntos para minimizar movimentos da cabeça que irão interferir com a monitorização de valência (perdendo ângulo de face correta para a câmera) ou geram artefatos miográfica no EEG. Por outro lado, um equilíbrio deve ser mant...

Engagement desempenha um papel crucial na aprendizagem. Para Clark e Mayer 2, &quot;toda a aprendizagem requer engajamento&quot;, independentemente dos meios de comunicação de entrega. Zhang et al. 3 também sugeriram que o aumento do envolvimento do aluno pode melhorar os resultados de aprendizagem, tais como resolução de problemas e habilidades de pensamento crítico. Definindo engajamento continua a ser um desafio. Em sua revisão da literatura, Fredricks, Blumenfeld e Paris 1 definido engajamento por sua natureza multifacetada: &quot;engajamento comportamental baseia-se na idéia de participação; que inclui o envolvimento em atividades acadêmicas e sociais ou extracurriculares. (...) O envolvimento emocional engloba reações positivas e negativas para os professores, colegas, acadêmicos e escola e é presumida para criar laços com um objeto e influência vontade de fazer o trabalho. Finalmente, envolvimento cognitivo baseia-se na idéia de investimento mental; ele incorpora consideração e vontade de exercer o esforço neccessário para compreender idéias complexas e dominar as habilidades difíceis &quot;. Fredricks, Blumenfeld e Paris 1 também alegou que um foco em comportamento, emoção e cognição, dentro do conceito de engajamento, pode fornecer uma caracterização mais rica de aprendizado. Estes autores apontam que um corpo robusto de pesquisa aborda cada componente de envolvimento separadamente, mas esses componentes não foram estudadas em conjunto. Eles também observaram que há pouca informação disponível sobre as interações entre as dimensões e que mais estudos poderão contribuir para o planejamento de intervenções de ensino afinado. Como um passo nesse sentido, este trabalho descreve uma metodologia de pesquisa que foi desenvolvido para coletar e analisar dados quantitativos e qualitativos, de forma síncrona, no engajamento comportamental, emocional e cognitiva durante tarefas de aprendizagem. Trazendo as Neurociências em Educação Behavior e, consequentemente, o engajamento comportamental, tem sido o foco central de estudos em educação: projetos de pesquisa focados principalmente em mudanças no conhecimento e comportamento que ocorrem ao longo de grandes períodos de tempo, entre pré e pós-testes, e ao longo de intervalos de horas, semanas , meses ou anos. Discriminando entre engajamento comportamental, emocional, cognitivo e continua a ser um desafio, porque as duas últimas dimensões não são sistematicamente observável externamente. Cognição e as emoções ou deve ser inferida a partir de observações ou avaliados com medidas de auto-relato. De um ponto de vista externo, continua a ser difícil determinar se os alunos estão a tentar fazer o seu trabalho o mais rapidamente possível ou utilizando estratégias de aprendizagem de nível profundo de dominar um conteúdo específico. Na verdade, Fredricks, Blumenfeld e Paris 1 foram incapazes de encontrar qualquer estudos publicados utilizando medidas diretas, objetivas de envolvimento cognitivo. Os desenvolvimentos tecnológicos recentes emo domínio das neurociências criaram novas possibilidades de pesquisa em educação. Novos métodos de coleta de dados e algoritmos de análise desenvolvidos no campo da ergonomia neuro parece muito promissor para estudos qualitativos e quantitativos durante as tarefas de aprendizagem. Outras disciplinas, tais como economia, psicologia, marketing e ergonomia, têm vindo a utilizar medições neurofisiológicas para avaliar o envolvimento cognitivo por algum tempo 4-8. Medidas neurofisiológicas, juntamente com algoritmos de análise eficientes, permitem estudar um fenômeno sem perturbá-la. Pela sua natureza, os questionários de auto-relato desengatar alunos de aprendizagem. Medidas neurofisiológicas permitir que projetos de pesquisa a ser realizada em ambientes de aprendizagem mais autênticas. Essas ferramentas incluem equipamento para monitorar a frequência cardíaca, frequência respiratória, pressão arterial, temperatura corporal, diâmetro da pupila, a atividade electrodermal, eletroencefalografia (EEG), etc. <strong&gt; Gathering Sincronizado Dados sobre comportamental, emocional, cognitivo e Engajamento Como resultados representativos na sequência da utilização deste protocolo, este documento irá apresentar os resultados parciais de um estudo no qual os alunos tinham de resolver, em uma tela de computador, dez problemas em física mecânica. Estes problemas foram desenvolvidos em trabalhos anteriores 9. Dados neurofisiológicos foram coletadas enquanto os alunos foram resolvendo os problemas e relaxante durante uma pausa de 45 s, com os olhos fechados, depois de cada problema. Como mencionado acima, os dados de noivado comportamentais consistem de interações de software (movimentos e cliques do mouse), o olhar dos olhos, desempenho e respostas a perguntas produzidos por um aluno interagir com o sistema ao realizar a tarefa 1. Um sistema de rastreamento ocular foi utilizado para coletar interações de software e dados do olhar olho. Os dados de desempenho (tempo para resolver um problema, correção de respostas) foram coletados em umsite pesquisa que foi utilizado para apresentar a tarefa. Este site também foi usado para reunir dados de auto-relato recolhidos com um questionário adaptado de Bradley e Lang 10. Envolvimento emocional envolve a caracterização das emoções. De acordo com Lang 11, as emoções são caracterizados em termos de valência (agradável / desagradável) e excitação (calma / despertado). Dados envolvimento emocional foram devidamente recolhidos, usando software de reconhecimento facial automático emoção que quantifica valência emocional e uma atividade codificador / sensor electrodermal para despertar 12,13. Actividade Eletrodermal (EDA) refere-se à resistência eléctrica entre os dois eléctrodos gravada quando uma corrente eléctrica é muito fraco continuamente passados ​​entre eles. Cacioppo, Tassinary Berntson e 14 mostraram que a resistência gravado varia de acordo com a excitação do sujeito. Assim, os dados psicofisiológicas, como valência ou excitação, são considerados como correlatos de envolvimento emocional. <pclass = &quot;jove_content&quot;&gt; Finalmente, os dados de noivado cognitivas são coletados por meio de eletroencefalografia (EEG). Medidas de EEG, no couro cabeludo, a atividade elétrica dos grupos de neurônios no cérebro sincronizado. Sinais elétricos gravados a partir do couro cabeludo são muitas vezes oscilatório e composta por componentes de frequência. Por convenção, essas freqüências são agrupadas em seqüências, conhecido como bandas. Por exemplo, alfa, beta e teta bandas são o foco deste estudo. De acordo com estudos neurocientíficos 14, essas bandas refletem diferentes habilidades de processamento cognitivo em áreas específicas do cérebro. Assim, a análise da densidade de potência espectral (PSD) de frequências específicas, combinados com numerosos estudos sobre o estado de alerta e 7,15 atenção, permite aos investigadores quantificar envolvimento cognitivo durante uma tarefa. Como Millar et al. 16 observou, a pesquisa mostrou uma relação direta entre a atividade beta e agilidade cognitiva e uma relação indireta entre um alfad atividade theta e agilidade. Assim, o Papa, Bogart e Bartoleme 7 desenvolveram um índice de noivado que calcula o PSD de três bandas: beta / (alfa + teta). Esse índice foi validado em outros estudos sobre o envolvimento 16,17,18. Para caracterizar o envolvimento cognitivo ao longo do tempo, uma transformada de Fourier rápida (FFT) converte o sinal de EEG de cada local activo (F3, F4, O1, O2) num espectro de potência. O índice de acoplamento de EEG no tempo T é calculado pela média de cada razão de acoplamento dentro de uma janela deslizante de 20 seg precedente tempo T. Este procedimento é repetido a cada segundo e uma nova janela de correr é utilizado para actualizar o índice. Uma vez que o objectivo desta metodologia é fornecer uma rica análise das múltiplas dimensões de engajamento, a sincronização de dados é crucial. Como Leger et al. 19 lembrar aos leitores, fabricantes de equipamentos altamente recomendável utilizar apenas um computador por ferramenta de medição para garantir a sua precisão especificada level. Assim, quando vários computadores estão empregados, a sincronização entre computadores de gravação torna-se um passo crítico. As gravações não podem ser iniciadas ao mesmo tempo exato, e cada fluxo de dados tem o seu período de tempo específico (por exemplo, sec 0 de olho rastreamento ≠ sec 0 de EEG ou dados fisiológicos). Isso é extremamente importante: dessincronização entre fluxos de dados significa erros na quantificação de cada dimensão de engajamento. Existem diferentes formas de sincronizar gravações fisiológicas e comportamentais simultâneas. Estes métodos podem ser divididos em duas abordagens principais; direta e indireta 20. O protocolo apresentado na próxima seção é baseada em uma abordagem indireta, onde um dispositivo externo, um syncbox, é usado para enviar sinais de lógica transistor-transistor (TTL) para todos os equipamentos de gravação (como mostrado na Figura 1). À medida que cada peça de equipamento tem uma hora de início diferente, os marcadores TTL são registrados nos arquivos de log com um parente<html> atraso. Os marcadores são então usadas para realinhar os sinais e, assim, assegurar uma sincronização apropriada depois de cada gravação. Um programa de software de análise comportamental que permite a integração de arquivo externo é usado para re-sincronizar o cronograma de cada fluxo de dados e para realizar a análise quantitativa e qualitativa de cada dimensão de engajamento. <img alt="Figura 1" src="/files/ftp_upload/52627/52627fig1.jpg" /> Figura 1. Arquitetura do Sistema de Coleta de Dados. O ambiente de laboratório no qual comportamental (eye-tracking), emocional (EDA e emoção facial) e dados cognitivos (EEG) de noivado são recolhidos contém muitos computadores. Isto levanta um desafio para a sincronização de dados que são referenciados em seus respectivos relógios dos computadores. Para ser capaz de analisar todos os dados no mesmo tempo de referência, a configuração do laboratório envolve um syncbox que envia sinais TTL para todos os fluxos de dados.nk &quot;&gt; Clique aqui para ver uma versão maior desta figura. Para avaliar a precisão da metodologia em termos de sincronização, 45 seg pausas foram introduzidas antes de cada um dos problemas de física mecânica. Durante essas pausas, os sujeitos tiveram para relaxar e fechar os olhos. Como visto em outros estudos 4,9,16,17,18, essas pausas devem induzir variações significativas no sinal coletado: os dois pontos pupila do olho em olho-tracking imediatamente desaparecer (engajamento comportamental) e uma queda imediata no envolvimento cognitivo (EEG sinal) é observada. Estes componentes específicos do sinal são usadas para avaliar a eficácia geral da sincronização. A recente publicação de documentos que total ou parcialmente dependem desse procedimento de sincronização, nas áreas de sistemas de informação 19, interações homem-máquina 21 e educação 9, 22, fornece provas da sua eficácia.

<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> Figuras 2 e 3 mostram screenshots dos resultados da integração e sincronização dos dados de noivado comportamentais, emocionais e cognitivas em uma aplicação de software de análise comportamental. Em ambas as figuras, a seção da mão esquerda organiza os sujeitos da pesquisa e do esquema de codificação. Na seção do meio, um vídeo (com pontos vermelhos) mostra olhar olho do sujeito durante a tarefa. Engajamento comportamental do indivíduo pode ser inferida com base no que ele / ela e...

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.

Avaliando as múltiplas dimensões do noivado a Caracterizar Aprendizagem: uma perspectiva neurofisiológica

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 visualizações.  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. 

Vídeo: 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;

Estudo Motor Habilidade Aprendizagem por Single-pelota Tarefas atingindo em Ratos

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

Comportamento

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.

Perspectivas sobre Neuroscience

Biologia

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.

Combinado Shuttle-Box Formação com eletrofisiológica Cortex Gravação e Estimulação como Ferramenta para o Estudo de Percepção e Aprendizagem

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.

Medindo a aprendizagem estatística entre modalidades e domínios em crianças em idade escolar através de uma plataforma online e técnicas de neuroimagem

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

Neurociência

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.

Sincronia inter-cerebral em aprendizagem colaborativa aberta: um estudo fNIRS-Hyperscanning

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.

Avaliando a excitabilidade corticoespinhal durante o comportamento de alcance direcionado a objetivos

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

Uma introdução ao aprendizado e à memória

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

Education

JoVE Science Education

Psicologia

Ciências Comportamentais

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

Uma Introdução à Neurofisiologia

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

Biologia Avançada

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.

Perspectivas sobre Neuropsicologia

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

Neuropsicologia

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.

Perspectivas sobre psicologia cognitiva

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

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