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).

Questo protocollo ha ricevuto un certificato di etica dal Comitato institutionnel de la recherche avec des êtres humains (CIER) de l&#39;Université du Québec à Montréal (UQAM), che è stato approvato dal HEC-Montreal per la struttura di ricerca Tech3Lab. Il protocollo descritti i passaggi specifici che vengono eseguite nel nostro ambiente di laboratorio e delle attrezzature. Anche se i percorsi di software precise vengono forniti per chiarire la metodologia, questa tecnica è trasferibile e può essere replicato con altri proprietari di eye-tracking, il riconoscimento automatico emozione facciale, l&#39;attività elettrodermica e attrezzature elettroencefalografia e software. 1. Impostazione del Laboratorio Ambiente <ol><li> Accendere l&#39;eye-tracker, l&#39;amplificatore EEG, i quattro computer di registrazione e gli altoparlanti. </li><li> Preparare l&#39;installazione dell&#39;apparecchio di controllo: <ol><li> Preparare la configurazione EEG con il materiale necessario secondo racc del produttoreProcedure nded. Preparare il software EEG per il partecipante imminente. Avviare il software eye-tracking e creare un nuovo profilo partecipante nel software. Avviare il software di registrazione video e le telecamere. </li><li> Avviare il software di sincronizzazione con la subroutine specifica creata per il progetto con i pennarelli a 60 sec. Avviare il software di misura fisiologico (per registrare l&#39;attività elettrodermica) e aprire il layout specifico creato per il progetto. Regolare la sedia del partecipante al più alto livello. </li></ol></li></ol> 2. partecipante Preparazione <ol><li> Chiedere al partecipante di leggere e firmare il modulo di consenso etico. </li><li> Effettuare misurazioni del cranio per EEG: <ol><li> Trovare la posizione Cz sulla testa del partecipante (secondo 10-20 sistemi di riferimento). Immergere la rete EEG nella soluzione salina (cloruro di potassio) (vedi punto 1.2.1) e avviare un timer (10 min) in conformità con gli standard del produttore. &lt;/ Li&gt; </ol></li><li> Leggi l&#39;obiettivo dello studio e i passaggi della sperimentazione al partecipante, &quot;L&#39;obiettivo di questo studio è quello di osservare l&#39;attività cerebrale mentre si risponde problemi di fisica. Prima di tutto installare i sensori, poi vi verrà chiesto di risolvere 10 problemi di fisica newtoniana sul computer. Ti verrà richiesto di fare una pausa di 45 secondi dopo ogni problema con gli occhi chiusi. Dopo ogni problema, vi verrà chiesto di votare la vostra valutazione del problema. &quot; </li><li> Dì il soggetto che la durata totale dell&#39;esperimento sarà 90 min. </li><li> Installare i sensori fisiologici, secondo le raccomandazioni del fabbricante: due sensori gelificato sulla parte superiore della mano sinistra. </li><li> Installare il tappo EEG, secondo le raccomandazioni del costruttore e eseguire un controllo di impedenza con una soglia a 40 kΩ (secondo le specifiche del produttore). </li></ol> 3. Raccolta dati <ol><li> Assicurarsi che tutto il software di registrazione è pronto per essere avviato in sincronia: <ol><li> Fisiologia (dati EDA): Fare clic sul pulsante &quot;Start&quot;. </li><li> Registrazione video: Fare clic sul pulsante &quot;Apri&quot;. </li><li> Eye-tracking: Fare clic sul pulsante &quot;in attesa&quot;. </li><li> EEG: Fai clic sul pulsante &quot;record&quot;. </li><li> Il software di sincronizzazione: Fai clic sul pulsante &quot;cerchio verde&quot;. </li></ol></li><li> Occhio calibrazione sguardo: <ol><li> Eseguire un cinque punti di calibrazione sullo schermo e osservare il partecipante, mentre lui / lei segue i punti rossi (cliccare su &quot;Strumenti / Impostazioni / Calibration ...&quot;). Ripetere questa procedura fino ad ottenere sufficiente precisione, secondo gli standard del produttore. </li></ol></li><li> Istruzioni compito proiettare sul schermo del partecipante: chiedere se lui / lei ha delle domande dopo la lettura, e se lui / lei è pronto per iniziare l&#39;esperimento. </li><li> Chiedere al partecipante di risolvere 10 Newton physics problemi. </li><li> Se necessario, eseguire un controllo dell&#39;impedenza durante una delle pause 45 s (non prima problema 5). </li><li> Assicurarsi che il partecipante assume la piena 45 s pausa prima di ogni problema (per determinare la linea di base). </li></ol> 4. Fine della raccolta dei dati <ol><li> Smettere di acquisizione dei dati in tutti i computer e rimuovere i sensori dal partecipante. </li></ol> 5. Dopo che il partecipante ha lasciato <ol><li> Pulire il tappo EEG con germicida e riordinare il materiale, secondo le raccomandazioni del costruttore. Salvare tutti i file di dati raccolti e creare un backup sul server FTP. </li><li> Compilare il foglio partecipante: notare un particolare evento o un problema durante la raccolta dei dati. Cancella tutti i cookie dal browser web. </li></ol> 6. I dati di pre-processing ed esporta per il Software Integration <ol start="1"><li> EEG <ol><li> Importa dati EEG in software di analisi dati EEG: <ol><li> Creare tre cartelle vuote sul computer denominato &quot;dati grezzi&quot;, &quot;Storia&quot; e &quot;Export&quot; per incollare i dati EEG grezzi in file di dati grezzi appena creato. </li><li> Nel software di analisi dei dati di EEG, fare clic su &quot;File / Nuovo progetto ...&quot; e scegliere la posizione dati grezzi facendo clic su Sfoglia, quindi selezionare il file di dati grezzi appena creato. Scegliere la posizione della &quot;Storia&quot; e cartelle &quot;esportazione&quot; nello stesso modo. </li><li> Fare clic su &quot;OK&quot;. (La finestra dovrebbe contenere tutti i dati EEG del partecipante). </li></ol></li><li> Pre-processo il segnale del cervello: <ol><li> Applicare un filtro e un notch (cliccare su &quot;Trasformazioni / filtri IIR ...&quot;). Nella finestra, abilitare la bassa frequenza di taglio di 1.5 Hz con una pendenza di 12 dB e l&#39;alta frequenza di taglio di 50 Hz con una pendenza di 12 dB. Consentire anche una tacca alla frequenza di 60 Hz. </li><li> Poiché viene utilizzato un amplificatore DC detrend il segnale (Fare clic su &quot;Transformations / DC Detrend ... &quot;e attivare&quot; in base al tempo &quot;a 100 msec prima del segno e 100 msec prima connessione CC). </li><li> Eseguire un controllo di dati grezzi (Fare clic su &quot;/ ispezione dati grezzi Trasformazione ...&quot; e selezionare rimozione semiautomatica artefatto). Selezionare le seguenti: tensione massima di 60 mV / ms; Max-min: 200 mV a 200 intervallo ms; ampiezza: -400 a +400 mV). </li><li> Eseguire una ICA automatico con Sphering classico per la rimozione batter d&#39;occhio (artefatti myographic non devono essere rimossi perché la loro portata è al di fuori delle frequenze di interesse). (Fare clic su &quot;Trasformazioni / ICA ...&quot;. Alla fine della ICA, processo ICA inversa.) </li><li> Re-di riferimento (&quot;Trasformazioni / Re-reference ...&quot;) il segnale e selezionare &quot;medio comune&quot;. </li><li> Export (fare clic su &quot;Esporta / Generic Data Export ...&quot;) il segnale e gli indicatori in formato testo (Selezionare il &quot;.vhdr&quot;; box) per un eventuale realizzazione Matlab dell&#39;indice impegno. Selezionare anche il &quot;file di intestazione Write&quot; e scatole &quot;di file marcatore Write&quot;. </li></ol></li><li> Importare il segnale in Matlab. <ol><li> Inizia Matlab e digitare &quot;eeglab&quot;, così l&#39;interfaccia grafica di EEGLab appare e importare i dati di un partecipante alla volta. Nella GUI, selezionare la voce di menu &quot;File / Importa dati / Utilizzo delle funzioni EEGLab e plugins / Da Cervello Vis Rec .vhdr file&quot;. </li><li> Nella finestra di comando, incollare uno script 16 che genera un indice di impegno. NOTA: Lo script impegno cognitivo viene calcolato la media di ogni / (Alpha + Theta) Rapporto Beta entro un tempo T. finestra scorrevole di 20 secondi che precede Questa procedura viene ripetuta ogni secondo e una nuova finestra scorrevole viene utilizzato per aggiornare l&#39;indice. </li></ol></li><li> In Microsoft Excel, aprire il file di testo dell&#39;indice di impegno che viene generato alla fine dello script per Matlab e applicare un z-score la normalizzazione dei dati EEG per consentire il confronto interindividuale. (Per ogni valore, calcola la formula in Excel: Z = (valore - media generale) / deviazione standard generale.) </li><li> Salvare il segnale di indice di impegno z-score in un file CSV in MS Excel. (Fare clic su File / Salva con nome ... e selezionare CSV nel tipo di formato.) </li><li> Ripetere la procedura (dal punto 6.1.2.2.) Per ogni partecipante. </li></ol></li><li> Fisiologia: <ol><li> Importa dati EDA in fisiologico software di analisi dei dati. </li><li> Applicare questi parametri pre-processo del segnale fisiologico: <ol><li> Applicare una trasformazione logaritmica per normalizzare la distribuzione della conduttanza secondo Venables e Christie 23 metodo. </li><li> Piatto il segnale su una finestra 10 sec 24 scorrevole. </li></ol></li><li> All&#39;interno del software fisiologico, calcolare una normalizzazione z-score sui dati EDA per permettere il confronto interindividuale. (Z = (valore - media generale) / deviazione standard globale). <ol><li> Evidenziare tutti i dati con il cursore dal canale EDA. </li><li> Nel menu in alto, selezionare il canale EDA, e selezionare &quot;significa&quot; per ottenere il valore medio del canale generale. Selezionare anche il canale EDA e &quot;stddev&quot; per ottenere il valore della deviazione standard del canale complessiva. </li><li> Per calcolare l&#39;equazione z-score, fai clic su &quot;Trasformazione / Waveform Math ...&quot; e selezionare il canale EDA in Source 1. Selezionare &quot;-&quot; (meno) nella finestra operazione matematica e selezionare K in origine 2. Selezionare &quot;Nuova destinazione&quot; nel menu di destinazione e immettere il valore medio del canale EDA (vedi punto 6.2.3.2). Selezionare &quot;Trasforma intero onda&quot;, fare clic su OK e fare clic su &quot;Trasformazione / Waveform Math ...&quot;. Selezionare il canale EDA-K in origine 1, selezionare &quot;/&quot; (divisione) nella finestra operazione matematica, selezionare K in origine 2, selezionare &quot;Nuova destinazione&quot; a destinazione ed enter il valore della deviazione standard del canale EDA (passo 6.2.3.2). Selezionare &quot;Trasforma intero onda&quot; e fare clic su OK. </li></ol></li><li> Esportare il segnale (arousal) in un file CSV. (Fare clic su File / Salva con nome ... e selezionare CSV nel tipo di formato.) </li></ol></li><li> Riconoscimento automatico emozione facciale: <ol><li> Importa i dati video dal registratore video in automatico il software di riconoscimento facciale emozione. (Fare clic su &quot;File / Nuovo ... / Partecipante ...&quot;. Dopo aver selezionato un nuovo partecipante nel menu progetto cliccando su di esso, fare clic su &quot;File / Nuovo / Analisi / Video ...&quot;. Fare clic sulla lente di ingrandimento accanto a Analisi 1 e scegliere la desiderata file video. <ol><li> Selezionare l&#39;analisi offline per &quot;ogni terzo frame&quot; e &quot;Calibrazione continua&quot; attivazione. </li><li> Esportazione dati di valenza in un file CSV. (Fare clic su &quot;Opzioni / Impostazioni / registrazione ...&quot;, selezionare la casella &quot;Scrivere valore di valenza per il file di log&quot; di dialogo. Fare clic su &quot;File /Esporta ... &quot;, scegliere la posizione in cui verranno esportati i file di log, e selezionare la casella&quot; Salva scatola dettagliato log &quot;.) </li><li> Aprire il file CSV in MS Excel. Copiare la colonna di dati valenza in una singola colonna di software SPSS. Fare clic su &quot;Analizza / Descrittive Statistiche / Descrittive&quot; e selezionare il nome della variabile appena incollato. Seleziona la casella &quot;Salva valori standardizzati nelle variabili&quot;. Apparirà una colonna con un punteggio z. Copia-incolla questi z-score sopra i vecchi dati nel file Excel. </li></ol></li><li> Salvare il file Excel con le z-score del segnale (valenza) in formato CSV. </li></ol></li></ol> 7. Data Integration e sincronizzazione <ol start="1"><li> Nel software di analisi comportamentale: <ol><li> Importa video eye-tracking (aggancio del comportamento). (Fare clic su &quot;File / Importa / video in una nuova osservazione ...&quot;. Assegnare un nome alla nuova osservazione e scegliere il file video desiderato.) </li><li> Codice ogni video con essere pertinentecomporta- ed eventi contestuali (marcatori di tempo, risposte giusto / sbagliato). </li><li> Importare tutti i dati esterni con l&#39;intestazione appropriata: z-score di segnale EEG (impegno cognitivo), z-score del segnale di EDA (coinvolgimento emotivo), z-score dei dati di valenza (coinvolgimento emotivo). (Fare clic su &quot;File / Importa / dati esterni ...&quot;. Selezionare il tipo di file appropriato e selezionare il file CSV corretto.) </li></ol></li><li> Sincronizzare ora tra i computer in base a queste formule: <ol><li> Time occhio lo sguardo di tanto in EEG = tempo nell&#39;occhio sguardo + secondo marcatore in EEG - primo marcatore nello sguardo degli occhi. </li><li> Time occhio sguardo di tanto in riconoscimento delle emozioni facciali = Time occhio sguardo + primo marcatore in emozione facciale - primo marcatore nello sguardo degli occhi. </li><li> Time occhio lo sguardo di tanto in attività elettrodermica = Time occhio sguardo + primo marcatore in attività elettrodermica - primo marcatore nello sguardo degli occhi. </li></ol></li><li> Inserisci i dati di correzione premendo il tasto &quot;Ctrl + Shift + =221 ;, per aprire il menu Offset. Selezionare &quot;Offset numerico&quot; per inserire il tempo in secondi tra ogni coppia di fonti di dati [OK?]), Secondo i calcoli di cui sopra. </li><li> Generare un report in base alle variabili di interesse per lo studio. <ol><li> Selezionare le variabili interessanti che saranno generati nel report (cliccare su &quot;Analizza / Select Dati / Nuovo profilo dati ...&quot;). Da sinistra, far scorrere le variabili desiderate tra la casella &quot;Start&quot; e la casella &quot;Risultati&quot;, sulla destra. </li><li> Generare il rapporto. (Fare clic su &quot;Analizza / Analisi Numerica / Nuovo ...&quot;, cliccare su &quot;Statistiche&quot; e selezionare la casella medio nel menu Dati esterni. Chiudi facendo clic su &quot;Calcola&quot;.) </li></ol></li><li> Esportare i dati in un software di analisi statistica e di eseguire analisi in funzione degli obiettivi di studio. </li></ol>

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J., Martin, I., Venables, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electrodermal+activity.">Electrodermal activity.</a> Techniques in Psychophysiology. , 3-67 (1980).</li><li>Boucsein, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Electrodermal Activity. , (2012).</li><li>Sarlo, M., Palomba, D., Buodo, G. M., Minghetti, R., Stegagno, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Blood+pressure+changes+highlight+gender+differences+in+emotional+reactivity+to+arousing+pictures.">Blood pressure changes highlight gender differences in emotional reactivity to arousing pictures.</a> Biol. Psychol. 70 (3), 188-196 (2005).</li><li>Martens, R. L., Gulikers, J., Bastiaens, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+impact+of+intrinsic+motivation+on+e-learning+in+authentic+computer+tasks.">The impact of intrinsic motivation on e-learning in authentic computer tasks.</a> J. Comput. Assist. Lear. 20 (5), 368-376 (2004).</li></ol>

The authors have nothing to disclose.

In termini di fasi critiche all&#39;interno del protocollo, si deve in primo luogo rilevare che la qualità dei dati è sempre l&#39;obiettivo principale per tecniche di raccolta neurofisiologiche. In questa metodologia, assistenti di ricerca devono prestare particolare attenzione a istruire i temi per ridurre al minimo i movimenti della testa che interferiscono con il monitoraggio di valenza (perdendo corretto angolo di fronte per la fotocamera) o generare artefatti myographic nel EEG. D&#39;altra parte, un equilibrio ...

Engagement gioca un ruolo cruciale nell&#39;apprendimento. Per Clark e Mayer 2, &quot;tutto l&#39;apprendimento richiede impegno,&quot; indipendentemente dal supporto consegna. Zhang et al. 3 anche suggerito che un maggiore coinvolgimento degli studenti in grado di migliorare i risultati di apprendimento, come problem solving e capacità di pensiero critico. Definizione di impegno rimane una sfida. Nella loro revisione della letteratura, Fredricks, Blumenfeld e Parigi 1 definiti impegno per sua natura multiforme: &quot;impegno comportamentale attinge l&#39;idea della partecipazione; comprende la partecipazione alle attività accademiche e sociali o extrascolastiche. (...) L&#39;impegno emotivo comprende reazioni positive e negative per gli insegnanti, i compagni di classe, accademici, e la scuola e si presume di creare legami con un oggetto e influenza la volontà di fare il lavoro. Infine, l&#39;impegno cognitivo attinge l&#39;idea di investimento mentale; incorpora pensosità e la volontà di esercitare il nec sforzone- di comprendere idee complesse e maestri competenze difficili &quot;. Fredricks, Blumenfeld e Parigi 1 anche sostenuto che un focus sul comportamento, emozione e cognizione, all&#39;interno del concetto di impegno, può fornire una caratterizzazione più ricca di apprendimento. Questi autori hanno sottolineato che un corpo solido di ricerca affronta ogni componente di impegno separatamente, ma questi componenti non erano stati studiati in combinazione. Essi hanno inoltre osservato che poche informazioni sono disponibili riguardo le interazioni tra le dimensioni e che ulteriori studi potrebbero contribuire a programmare interventi didattici finemente sintonizzato. Come un passo in questa direzione, questo documento descrive una metodologia di ricerca che è stato sviluppato per raccogliere e analizzare i dati quantitativi e qualitativi, in modo sincrono, in impegno comportamentale, emotivo e cognitivo durante compiti di apprendimento. Portare le Neuroscienze in Education Behavior, e di conseguenza l&#39;impegno comportamentale, è stata a lungo il tema centrale di studi in materia di istruzione: progetti di ricerca incentrati principalmente sui cambiamenti delle conoscenze e comportamenti che si verificano per lunghi periodi di tempo, tra il pre e post-test, e su intervalli di ore, settimane , mesi o anni. Discriminare tra impegno comportamentale, emotivo, cognitivo e rimane una sfida, perché le ultime due dimensioni non sono sistematicamente osservabili esternamente. Cognizione e le emozioni devono o essere dedotta dalle osservazioni o valutati con misure self-report. Da un punto di vista esterno, rimane difficile stabilire se gli studenti stanno cercando di ottenere il loro lavoro fatto nel più breve tempo possibile o utilizzando strategie di apprendimento a livello profondo di padroneggiare un contenuto specifico. In punto di fatto, Fredricks, Blumenfeld e Parigi 1 sono stati in grado di trovare tutti gli studi pubblicati che utilizzano, misure oggettive diretti di impegno cognitivo. I recenti sviluppi tecnologici inil campo delle neuroscienze hanno creato nuove possibilità per la ricerca in materia di istruzione. Nuovi metodi di raccolta dei dati e algoritmi di analisi sviluppate nel campo dell&#39;ergonomia neuro sembrano molto promettenti per gli studi qualitativi e quantitativi durante le attività di apprendimento. Altre discipline, come l&#39;economia, la psicologia, marketing, e l&#39;ergonomia, hanno utilizzato le misurazioni neurofisiologiche per valutare l&#39;impegno cognitivo per qualche tempo 4-8. Misure neurofisiologici, insieme con algoritmi di analisi efficienti, permettono di studiare un fenomeno senza disturbarlo. Per loro natura, i questionari self-report disimpegnarsi studenti di apprendimento. Misure neurofisiologici permettono disegni di ricerca da svolgere in ambienti di apprendimento più autentica. Questi strumenti comprendono attrezzature per il monitoraggio della frequenza cardiaca, frequenza respiratoria, pressione arteriosa, temperatura corporea, diametro della pupilla, l&#39;attività elettrodermica, elettroencefalografia (EEG), ecc. <strong&gt; Raccolta sincronizzato dei dati su Behavioral, emotivo, cognitivo e fidanzamento Come risultati rappresentativi in ​​seguito all&#39;uso di questo protocollo, questo articolo presenta i risultati parziali di uno studio in cui gli studenti hanno dovuto risolvere, sullo schermo del computer, dieci problemi di fisica meccanica. Questi problemi sono stati sviluppati in lavori precedenti 9. Dati neurofisiologici sono stati raccolti, mentre i discenti sono stati risolvere i problemi e di relax durante un 45 s pausa, con gli occhi chiusi, dopo ogni problema. Come accennato in precedenza, i dati comportamentali impegno sono costituiti da interazioni software (movimenti del mouse e click), lo sguardo degli occhi, le prestazioni e le risposte alle domande prodotte da uno studente interagisce con il sistema, mentre realizzazione del compito 1. Un sistema di eye-tracking è stato utilizzato per raccogliere le interazioni del software e dei dati sguardo degli occhi. Prestazioni (tempo per risolvere un problema, correttezza delle risposte) sono stati raccolti su unsito web sondaggio che è stato utilizzato per presentare l&#39;attività. Questo sito è stato utilizzato anche per raccogliere dati self-report raccolti con un questionario adattato da Bradley e Lang 10. Coinvolgimento emotivo comporta caratterizzazione di emozioni. Secondo Lang 11, le emozioni sono caratterizzati in termini di valenza (piacevole / spiacevole) e l&#39;eccitazione (calma / suscitato). Dati coinvolgimento emotivo sono stati quindi raccolti, utilizzando il software di riconoscimento facciale automatico emozione che quantifica valenza emotiva e un&#39;attività encoder / sensore elettrodermica per l&#39;eccitazione 12,13. Dell&#39;attività elettrodermica (EDA) si riferisce alla resistenza elettrica registrato tra due elettrodi quando una corrente elettrica molto debole è in costante passati tra loro. Cacioppo, Tassinary e Berntson 14 hanno dimostrato che la resistenza registrata varia secondo l&#39;eccitazione del soggetto. Pertanto, i dati psicofisiologici, come la valenza o l&#39;eccitazione, sono considerati correlati di coinvolgimento emotivo. <pclass = &quot;jove_content&quot;&gt; Infine, i dati di fidanzamento cognitive vengono raccolti attraverso elettroencefalografia (EEG). Misure EEG, sul cuoio capelluto, l&#39;attività elettrica di gruppi di neuroni nel cervello sincronizzati. I segnali elettrici registrati dal cuoio capelluto sono spesso oscillatorio e composto di componenti di frequenza. Per convenzione, queste frequenze sono raggruppate in sequenze, noto come bande. Ad esempio, alfa, beta e theta bande sono al centro di questo studio. Secondo gli studi neuroscientifici 14, queste bande riflettono differenti capacità di elaborazione cognitiva in aree specifiche del cervello. Pertanto, l&#39;analisi della densità spettrale di potenza (PSD) di frequenze specifiche, combinato con numerosi studi 7,15 su vigilanza e l&#39;attenzione, permette ai ricercatori di quantificare l&#39;impegno cognitivo durante un compito. Come ha osservato Mikulka et al. 16, la ricerca ha dimostrato una relazione diretta tra l&#39;attività di beta e la vigilanza cognitiva e una relazione indiretta tra alpha und attività theta e la vigilanza. Quindi, il Papa, Bogart e Bartoleme 7 sviluppato un indice di impegno che calcola il PSD di tre bande: beta / (alpha + theta). Questo parametro è stato convalidato in altri studi sul coinvolgimento 16,17,18. Per caratterizzare l&#39;impegno cognitivo nel tempo, una trasformata rapida di Fourier (FFT) converte il segnale EEG da ciascun sito attivo (F3, F4, O1, O2) in uno spettro di potenza. L&#39;indice di impegno EEG al tempo T viene calcolata la media di ciascun rapporto di impegno entro una finestra scorrevole 20 sec il precedente tempo T. Questa procedura viene ripetuta ogni secondo e una nuova finestra scorrevole è utilizzato per aggiornare l&#39;indice. Poiché l&#39;obiettivo di questa metodologia è quello di fornire una ricca analisi delle molteplici dimensioni di ingaggio, sincronizzazione dei dati è cruciale. Come Leger et al. 19 ricordare ai lettori, i produttori di apparecchiature consiglia vivamente di utilizzare un solo computer per ogni strumento di misurazione per garantirne la precisione specificata level. Così, quando più computer sono impiegati, la sincronizzazione tra i computer di registrazione diventa un passaggio fondamentale. Le registrazioni non possono tutti essere iniziato al tempo stesso esatto, e ogni flusso di dati ha il suo determinato periodo di tempo (ad esempio, sec 0 di eye-tracking ≠ sec 0 di EEG o dati fisiologici). Ciò è estremamente importante: desincronizzazione tra i flussi di dati significa errori nella quantificazione di ogni dimensione di impegno. Ci sono diversi modi di sincronizzazione registrazioni fisiologiche e comportamentali concomitanti. Questi metodi possono essere suddivisi in due approcci principali; diretta e indiretta 20. Il protocollo presentato nella sezione successiva è basato su un approccio indiretta quando un dispositivo esterno, un syncbox, viene utilizzato per inviare logica transistor-transistor (TTL) segnali a tutto l&#39;apparecchio di controllo (come mostrato in Figura 1). Come ogni pezzo di equipaggiamento ha un orario di inizio diverso, i marcatori TTL sono registrati nei file di registro con un parente<html> ritardo. Marcatori vengono poi utilizzati per riallineare i segnali e garantire la corretta sincronizzazione dopo ogni registrazione così. Un programma software di analisi comportamentale che consente l&#39;integrazione file esterno viene utilizzato per ri-sincronizzare la cronologia di ogni flusso di dati e di eseguire analisi quantitativa e qualitativa di ogni dimensione di impegno. <img alt="Figura 1" src="/files/ftp_upload/52627/52627fig1.jpg" /> Figura 1. Architettura del sistema di raccolta dei dati. L&#39;ambiente di laboratorio in cui comportamentale (eye-tracking), emotivo (EDA ed emozione facciale) e dati (EEG) di fidanzamento cognitivi sono raccolte contiene molti computer. Questo solleva una sfida di sincronizzazione per i dati a cui fa riferimento sui rispettivi orologi dei computer. Per essere in grado di analizzare tutti i dati nello stesso tempo di riferimento, l&#39;impostazione del laboratorio comporta un syncbox che invia segnali TTL a tutti i flussi di dati.nk &quot;&gt; Clicca qui per vedere una versione più grande di questa figura. Per valutare la precisione del metodo in termini di sincronizzazione, sono stati introdotti 45 sec pause prima ciascuno dei problemi fisica meccanica. Durante queste pause, i soggetti dovevano rilassarsi e chiudere gli occhi. Come si è visto in altri studi 4,9,16,17,18, queste pause dovrebbero indurre variazioni significative del segnale raccolto: i due punti pupilla dell&#39;occhio di eye-tracking subito scompaiono (impegno comportamentale) e un calo immediato impegno cognitivo (EEG segnale) si osserva. Questi componenti specifici del segnale sono utilizzati per valutare la validità generale della sincronizzazione. La recente pubblicazione di articoli che completamente o parzialmente basano su questa procedura di sincronizzazione, nel campo dei sistemi informativi 19, interazioni uomo-macchina 21 e di formazione 9, 22, fornisce la prova della sua efficacia.

<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> Le figure 2 e 3 mostrano schermate dei risultati dell&#39;integrazione e sincronizzazione dei dati comportamentali, emotivi e cognitivi impegno in un&#39;applicazione software di analisi comportamentale. In entrambe le figure, la parte sinistra organizza i soggetti di ricerca e lo schema di codifica. Nella sezione centrale, un video (con punti rossi) mostra l&#39;occhio lo sguardo del soggetto durante il compito. Impegno comportamentale del soggetto può essere dedotta sulla base di quello che lui...

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.

Valutare le molteplici dimensioni di ingaggio per caratterizzare Learning: una prospettiva neurofisiologica

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

Research

JoVE Journal

Behavior

12.4K Views.  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. 

Video: 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;

Studio Skill Motor Learning by Single-pellet attività Raggiungere in topi

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

Ricerca

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.

Prospettive sulla Neuroscienze

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.

Combinato Shuttle-Box Training con elettrofisiologiche Cortex registrazione e stimolazione come strumento per studiare percezione e apprendimento

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.

Misurazione dell'apprendimento statistico tra modalità e domini nei bambini in età scolare tramite una piattaforma online e tecniche di neuroimaging

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

Neuroscienze

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.

Inter-Brain Synchrony in Open-Ended Collaborative Learning: uno studio 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.

Valutazione dell'eccitabilità corticospinale durante il comportamento di raggiungimento diretto all'obiettivo

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

Un introduzione all'apprendimento e alla memoria

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

Didattica

JoVE Science Education

Psicologia

Scienze comportamentali

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

Introduzione alla neurofisiologia

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

Biologia avanzata

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.

Prospettive sulla 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.

Prospettive sulla psicologia cognitiva

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

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