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).

This protocol received an ethical certificate from the Comit&#233; institutionnel de la recherche avec des &#234;tres humains (CIER) de l&#8217;Universit&#233; du Qu&#233;bec &#224; Montr&#233;al (UQAM) that was endorsed by HEC-Montreal for the Tech3Lab research facility. The protocol describes each of the specific steps that are performed in our lab environment and equipment. Although precise software paths are provided to clarify the methodology, this technique is transferable and can be replicated with other proprietary eye-tracking, automatic facial emotion recognition, electrodermal activity and electroencephalography equipment and software.
1. Setup of the Lab Environment
<ol>
	<li>Turn on the eye-tracker, the EEG amplifier, the four recording computers and the speakers.</li>
	<li>Prepare the setup of the recording equipment:
	<ol>
		<li>Prepare the EEG setup with required material according to the manufacturer&#8217;s recommended procedures. Prepare the EEG software for the upcoming participant. Start the eye-tracking software and create a new participant profile in the software. Start the video recording software and the cameras.</li>
		<li>Start the synchronization software with the specific subroutine created for the project with markers at 60&#160;sec. Start the physiological measurement software (to record electrodermal activity) and open the specific layout created for the project. Adjust the participant&#8217;s chair to the highest level.</li>
	</ol>
	</li>
</ol>
2. Participant Preparation
<ol>
	<li>Ask the participant to read and sign the ethical consent form.</li>
	<li>Carry out skull measurements for EEG:
	<ol>
		<li>Find the Cz location on the participant&#8217;s head (according to 10 - 20 reference systems). Immerse the EEG net in the saline solution (potassium chloride) (see step 1.2.1) and start a timer (10 min) in accordance with the manufacturer&#8217;s standards.</li>
	</ol>
	</li>
	<li>Read the objective of the study and the steps in the experiment to the participant, &#8220;The objective of this study is to observe your brain activity while you answer physics problems. First we will install the sensors, then you will be asked to solve 10 Newtonian physics problems on the computer. We will ask you to take a 45-sec&#160;break after each problem with your eyes closed. After each problem, you will be asked to rate your assessment of the problem.&#8221;</li>
	<li>Tell the subject that the total duration of the experiment will be 90 min.</li>
	<li>Install the physiological sensors, according to the manufacturer&#8217;s recommendations: two gelified sensors on the top of the left hand.</li>
	<li>Install the EEG cap, according to the manufacturer&#8217;s recommendations and perform an impedance check with a threshold at 40 k&#937; (according to the manufacturer&#8217;s specifications).</li>
</ol>
3. Data Collection
<ol>
	<li>Make sure that all the recording software is ready to be started in synchrony:
	<ol>
		<li>Physiology (EDA data): Click the &#8220;start&#8221; button.</li>
		<li>Video recording: Click the &#8220;open&#8221; button.</li>
		<li>Eye-tracking: Click the &#8220;on hold&#8221; button.</li>
		<li>EEG: Click the &#8220;record&#8221; button.</li>
		<li>Synchronization software: Click the &#8220;green circle&#8221; button.</li>
	</ol>
	</li>
	<li>Eye gaze calibration:
	<ol>
		<li>Perform a five-point on-screen calibration and observe the participant while he/she follows the red dots (click &#8220;Tools/Settings/Calibration &#8230;&#8221;). Repeat this procedure until sufficient accuracy is achieved, according to the manufacturer&#8217;s standards.</li>
	</ol>
	</li>
	<li>Project task instructions onto the participant&#8217;s screen: ask if he/she has any questions after reading them, and if he/she is ready to start the experiment.</li>
	<li>Ask the participant to solve 10 Newtonian physics problems.</li>
	<li>If needed, perform an impedance check during one of the 45 s breaks (not before problem 5).</li>
	<li>Make sure the participant takes the full 45&#160;s break before each problem (to determine the baseline).</li>
</ol>
4. End of Data Collection
<ol>
	<li>Stop data acquisition on all computers and remove the sensors from the participant.</li>
</ol>
5. After the Participant Has Left
<ol>
	<li>Clean the EEG cap with germicide and tidy up the equipment, according to the manufacturer&#8217;s recommendations. Save all the data files collected and create a backup on the FTP server.</li>
	<li>Fill in the participant spreadsheet: note any particular event or problem during data collection. Erase all cookies from the web browser.</li>
</ol>
6. Data Pre-processing and Export to the Integration Software
<ol start="1">
	<li>EEG
	<ol>
		<li>Import EEG data into EEG data analysis software:
		<ol>
			<li>Create three empty folders on the computer named &#8220;Raw data&#8221;, &#8220;History&#8221; and &#8220;Export&#8221; to paste the raw EEG data into the newly created Raw data file.</li>
			<li>In the EEG data analysis software, click &#8220;File/New Project &#8230;&#8221; and choose the raw data location by clicking Browse, then selecting the newly created raw data file. Choose the location of the &#8220;History&#8221; and &#8220;Export&#8221; folders in the same way.</li>
			<li>Click &#8220;OK&#8221;. (The window should contain all the participant&#8217;s EEG data).</li>
		</ol>
		</li>
		<li>Pre-process the brain signal:
		<ol>
			<li>Apply a filter and a notch (click &#8220;Transformations/IIR filters&#8230;&#8221;). In the window, enable the low cutoff at 1.5&#160;Hz with a slope of 12 dB and the high cutoff at 50&#160;Hz with a slope of 12&#160;dB. Also enable a notch at 60 Hz frequency.</li>
			<li>Because a DC amplifier is used, DC detrend the signal (Click &#8220;Transformations/DC Detrend&#8230;&#8221; and enable &#8220;based on time&#8221; at 100&#160;msec before marker and 100&#160;msec before DC connection).</li>
			<li>Perform a raw data inspection (Click &#8220;Transformation/Raw data inspection&#8230;&#8221; and select semi-automatic artifact removal). Select the following: maximal voltage 60&#160;&#181;V/ms; Max-min: 200 &#181;V in 200&#160;ms interval; amplitude: -400 to +400 &#181;V).</li>
			<li>Perform an automatic ICA with classic sphering for eye blink removal (myographic artifacts do not need to be removed because their range is outside of the frequencies of interest). (Click &#8220;Transformations/ICA&#8230;&#8221;. At the end of the ICA, process the inverse ICA.)</li>
			<li>Re-reference (&#8220;Transformations/Re-reference&#8230;&#8221;) the signal and select &#8220;common average&#8221;.</li>
			<li>Export (click &#8220;Export/Generic Data Export&#8230;&#8221;) the signal and markers in text format (Select the &#8220;.vhdr&#8221; box) for an eventual Matlab construction of the engagement index. Also select the &#8220;Write header file&#8221; and &#8220;Write marker file&#8221; boxes.</li>
		</ol>
		</li>
		<li>Import the signal in Matlab.
		<ol>
			<li>Start Matlab and type &#8220;eeglab&#8221; so the GUI of EEGLab appears and Import the data for one participant at a time. In the GUI, select item menu &#8220;File/Import Data/Using EEGLab functions and plugins/From Brain Vis Rec .vhdr file&#8221;.</li>
			<li>In the command window, paste a script16 that generates an engagement index. &#160; 
			NOTE: The cognitive engagement script is computed by the average of each Beta/(Alpha + Theta) ratio within a 20&#160;sec&#160;sliding window preceding time T. This procedure is repeated every second and a new sliding window is used to update the index.</li>
		</ol>
		</li>
		<li>In MS Excel, open the text file of the engagement index that is generated at the end of the script by Matlab and apply a z-score normalization on EEG data to allow intersubject comparison. (For each value, compute this formula in Excel: Z=(value &#8211; overall mean)/overall standard deviation.)</li>
		<li>Save the z-score engagement index signal in a CSV file in MS Excel. (Click File/Save as &#8230; and select CSV in the format type.)</li>
		<li>Repeat the procedure (from step 6.1.2.2.) for each participant.</li>
	</ol>
	</li>
	<li>Physiology:
	<ol>
		<li>Import EDA data in physiological data analysis software.</li>
		<li>Apply these parameters to pre-process the physiological signal:
		<ol>
			<li>Apply a logarithmic transformation to normalize the distribution of the conductance as per Venables and Christie&#8217;s23 method.</li>
			<li>Flat the signal on a 10&#160;sec sliding window24.</li>
		</ol>
		</li>
		<li>Within the physiological software, compute a z-score normalization on the EDA data to allow intersubject comparison. (Z=(value &#8211; overall mean)/overall standard deviation).
		<ol>
			<li>Highlight all the data with the cursor from the EDA channel.</li>
			<li>In the top menu, select the EDA channel, and select &#8220;mean&#8221; to obtain the mean value of the overall channel. Also select the EDA channel and &#8220;stddev&#8221; to obtain the standard deviation value of the overall channel.</li>
			<li>To compute the z-score equation, click &#8220;Transformation/Waveform Math &#8230;&#8221; and select the EDA channel in Source 1 . Select &#8220;&#8211;&#8221; (minus) in the mathematical operation window and select K in source 2. Select &#8220;New destination&#8221; in the destination menu and enter the mean value of the EDA channel (see step 6.2.3.2). Select &#8220;Transform entire wave&#8221;, click OK and click &#8220;Transformation /Waveform Math &#8230;&#8221;. Select the EDA-K channel in source 1, select &#8220;/&#8221; (divide) in the mathematical operation window, select K in source 2, select &#8220;New destination&#8221; in destination and enter the standard deviation value of the EDA channel (step 6.2.3.2). Select &#8220;Transform entire wave&#8221; and click OK.</li>
		</ol>
		</li>
		<li>Export the signal (arousal) in a CSV file. (Click File/Save as &#8230; and select CSV in the format type.)</li>
	</ol>
	</li>
	<li>Automatic facial emotion recognition:
	<ol>
		<li>Import video data from the media recorder into automatic facial emotion recognition software. (Click &#8220;File/New&#8230;/Participant&#8230;&#8221;. After selecting a new participant in the project menu by clicking on it, click &#8220;File/New/Analysis/Video&#8230;&#8221;. Click the magnifying glass next to Analysis 1 and choose the desired video file.
		<ol>
			<li>Select an offline analysis for &#8220;every third frame&#8221; and activate &#8220;continuous calibration&#8221;.</li>
			<li>Export valence data in a CSV file. (Click &#8220;Options/Settings/Logging&#8230;&#8221;, check the &#8220;Write valence value to the log file&#8221; box. Click &#8220;File/Export&#8230;&#8221;, choose the location where the log files will be exported, and check the &#8220;Save detailed log&#8221; box.)</li>
			<li>Open the CSV file in MS Excel. Copy the valence data column in a single column of SPSS software. Click &#8220;Analyse/Descriptives Statistics/Descriptives&#8221; and select the just-pasted variable name. Check the box &#8220;Save standardized values in variables&#8221;. A column with a z-score will appear. Copy-paste these z-scores over the old data in the Excel file.</li>
		</ol>
		</li>
		<li>Save the Excel file with the z-scores of the signal (valence) in CSV format.</li>
	</ol>
	</li>
</ol>
7. Data Integration and Synchronization
<ol start="1">
	<li>In behavioral analysis software:
	<ol>
		<li>Import eye-tracking videos (behavioral engagement). (Click &#8220;File/Import/Video in a New Observation&#8230;&#8221;. Name the new observation and choose the desired video file.)</li>
		<li>Code each video with pertinent behaviors and contextual events (time markers, right/wrong answers).</li>
		<li>Import all external data with the appropriate header: z-score of EEG signal (cognitive engagement), z-score of EDA signal (emotional engagement), z-score of valence data (emotional engagement). (Click &#8220;File/Import/External Data&#8230;&#8221;. Select the appropriate file type and select the correct CSV file.)</li>
	</ol>
	</li>
	<li>Synchronize time between computers according to these formulas:
	<ol>
		<li>Time in eye gaze from time in EEG = Time in eye gaze + second marker in EEG &#8211; first marker in eye gaze.</li>
		<li>Time in eye gaze from time in facial emotion recognition = Time in eye gaze + first marker in facial emotion &#8211; first marker in eye gaze.</li>
		<li>Time in eye gaze from time in electrodermal activity = Time in eye gaze + first marker in electrodermal activity &#8211; first marker in eye gaze.</li>
	</ol>
	</li>
	<li>Enter the offset data by pressing &#8220;Ctrl + Shift + =&#8221;, to open the Offset menu. Select &#8220;Numerical offset&#8221; to enter the time in seconds between each pair of data sources [OK?]), according to the calculations above.</li>
	<li>Generate a report according to the variables of interest in the study.
	<ol>
		<li>Select interesting variables that will be generated in the report (click &#8220;Analyze/Select Data/New Profile Data&#8230;&#8221;). From the left, slide the desired variables between the &#8220;Start&#8221; box and the &#8220;Results&#8221; box, on the right.</li>
		<li>Generate the report. (Click &#8220;Analyze/Numerical Analysis/New&#8230;&#8221;, click &#8220;Statistics&#8221; and check the mean box in the external data menu. Finish by clicking &#8220;Calculate&#8221;.)</li>
	</ol>
	</li>
	<li>Export the data into statistical analysis software and perform analysis according to the study objectives.</li>
</ol>

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The authors have nothing to disclose.

In terms of critical steps within the protocol, it should first be pointed out that data quality is always the main focus for neurophysiological collection techniques. In this methodology, research assistants must pay special attention to instructing the subjects to minimize head movements that will interfere with valence monitoring (losing correct face angle for the camera) or generate myographic artifacts in the EEG. On the other hand, a balance must be maintained between the authenticity of real problem solving and in...

Engagement plays a crucial role in learning. For Clark and Mayer2, &#8220;all learning requires engagement,&#8221; regardless of delivery media. Zhang et al.3 also suggested that increased student engagement can improve learning outcomes, such as problem solving and critical thinking skills. Defining engagement remains a challenge. In their literature review, Fredricks, Blumenfeld and Paris1 defined engagement by its multifaceted nature: &#8220;Behavioural engagement draws on the idea of participation; it includes involvement in academic and social or extracurricular activities. (&#8230;) Emotional engagement encompasses positive and negative reactions to teachers, classmates, academics, and school and is presumed to create ties to an object and influence willingness to do the work. Finally, cognitive engagement draws on the idea of mental investment; it incorporates thoughtfulness and willingness to exert the effort necessary to comprehend complex ideas and master difficult skills.&#8221;
Fredricks, Blumenfeld and Paris1 also claimed that a focus on behavior, emotion, and cognition, within the concept of engagement, may provide a richer characterization of learning. These authors pointed out that a robust body of research addresses each component of engagement separately, but these components had not been studied in conjunction. They also observed that little information is available about interactions between the dimensions and that more studies could contribute to planning finely tuned teaching interventions. As a step in that direction, this paper describes a research methodology that was developed to gather and analyze quantitative and qualitative data, synchronously, on behavioral, emotional and cognitive engagement during learning tasks.
Bringing the Neurosciences into Education
Behavior, and consequently behavioral engagement, has long been the central focus of studies in education: research designs focused mainly on changes in knowledge and behavior occurring over long periods of time, between pre- and post-tests, and over intervals of hours, weeks, months or years. Discriminating between behavioral, emotional, and cognitive engagement remains a challenge because the last two dimensions are not systematically observable externally. Cognition and emotions must either be inferred from observations or assessed with self-report measures. From an external point of view, it remains difficult to determine whether students are trying to get their work done as quickly as possible or using deep-level learning strategies to master a specific content. In point of fact, Fredricks, Blumenfeld and Paris1 were unable to find any published studies using direct, objective measures of cognitive engagement.
Recent technological developments in the field of neurosciences have created new possibilities for research in education. New data collection methods and analysis algorithms developed in the field of neuroergonomics seem very promising for qualitative and quantitative studies during learning tasks. Other disciplines, such as economics, psychology, marketing, and ergonomics, have been using neurophysiological measurements to assess cognitive engagement for some time4-8. Neurophysiological measures, coupled with efficient analysis algorithms, allow one to study a phenomenon without disturbing it. By their nature, self-report questionnaires disengage students from learning. Neurophysiological measures allow research designs to be carried out in more authentic learning environments. These tools include equipment to monitor heart rate, breathing rate, blood pressure, body temperature, pupil diameter, electrodermal activity, electroencephalography (EEG), etc.
Gathering Synchronized Data on Behavioral, Emotional, and Cognitive Engagement
As representative outcomes following the use of this protocol, this paper will present partial results of a study in which learners had to solve, on a computer screen, ten problems in mechanical physics. These problems were developed in previous work9. Neurophysiological data were collected while the learners were solving the problems and relaxing during a 45&#160;s break, with their eyes closed, after each problem.
As mentioned above, behavioral engagement data consist of software interactions (mouse movements and clicks), eye gaze, performance and answers to questions produced by a learner interacting with the system while accomplishing the task1. An eye-tracking system was used to collect software interactions and eye gaze data. Performance data (time to solve a problem, correctness of answers) were collected on a survey website that was used to present the task. This website was also used to gather self-report data collected with a questionnaire adapted from Bradley and Lang10. Emotional engagement involves characterization of emotions. According to Lang11, emotions are characterized in terms of valence (pleasant/unpleasant) and arousal (calm/aroused). Emotional engagement data were accordingly collected, using automatic facial emotion recognition software that quantifies emotional valence and an electrodermal activity encoder/sensor for arousal12,13. Electrodermal activity (EDA) refers to the recorded electrical resistance between two electrodes when a very weak electrical current is steadily passed between them. Cacioppo, Tassinary and Berntson14 showed that the resistance recorded varies according to the subject&#8217;s arousal. Thus, psychophysiological data, such as valence or arousal, are considered as correlates of emotional engagement.
Finally, cognitive engagement data are collected through electroencephalography (EEG). EEG measures, on the scalp, the synchronized electrical activity of groups of neurons in the brain. Electrical signals recorded from the scalp are often oscillatory and composed of frequency components. By convention, these frequencies are grouped in sequences, known as bands. For example, alpha, beta and theta bands are the focus of this study. According to neuroscientific studies14, these bands reflect different cognitive processing abilities in specific areas of the brain. Thus, the analysis of the power spectral density (PSD) of specific frequencies, combined with numerous studies7,15 on alertness and attention, allows researchers to quantify cognitive engagement during a task. As Mikulka et al.16 noted, research has shown a direct relationship between beta activity and cognitive alertness and an indirect relationship between alpha and theta activity and alertness. Thus, Pope, Bogart and Bartoleme7 developed an engagement index that computes the PSD of three bands: beta / (alpha + theta). This ratio was validated in other studies on engagement16,17,18. To characterize cognitive engagement over time, a fast Fourier transform (FFT) converts the EEG signal from each active site (F3, F4, O1, O2) into a power spectrum. The EEG engagement index at time T is computed by the average of each engagement ratio within a 20&#160;sec&#160;sliding window preceding time T. This procedure is repeated every second and a new sliding window is used to update the index.
Since the aim of this methodology is to provide a rich analysis of the multiple dimensions of engagement, data synchronization is crucial. As Leger et al.19 remind readers, equipment manufacturers strongly recommend using only one computer per measurement tool to guarantee their specified precision level. Thus, when multiple computers are employed, synchronization between recording computers becomes a critical step. The recordings cannot all be started at the exact same time, and each data stream has its specific time frame (e.g., sec&#160;0 of eye tracking &#8800; sec&#160;0 of EEG or physiological data). This is extremely important: desynchronization between data streams means errors in the quantification of each dimension of engagement. There are different ways of synchronizing concurrent physiological and behavioral recordings. These methods may be divided into two main approaches; direct and indirect20. The protocol presented in the next section is based on an indirect approach where an external device, a syncbox, is used to send transistor-transistor logic (TTL) signals to all the recording equipment (as shown in Figure 1). As each piece of equipment has a different start time, the TTL markers are recorded in the log files with a relative delay. Markers are then used to realign the signals and thus ensure proper synchronization after each recording. A behavioral analysis software program that allows external file integration is used to re-synchronize the timeline of each data stream and to perform quantitative and qualitative analysis of each dimension of engagement.
<img alt="Figure 1" src="/files/ftp_upload/52627/52627fig1.jpg" /> 
Figure 1. Architecture of the Data Collection System. The lab environment in which behavioral (eye-tracking), emotional (EDA and facial emotion) and cognitive (EEG) engagement data are collected contains many computers. This raises a synchronization challenge for data that are referenced on their respective computer clocks. To be able to analyze all data in the same reference time, the lab setup involves a syncbox that sends TTL signals to all data streams. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/52627/52627fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
To evaluate the precision of the methodology in terms of synchronization, 45&#160;sec&#160;pauses were introduced before each of the mechanical physics problems. During these pauses, subjects had to relax and to close their eyes. As seen in other studies4,9,16,17,18, these pauses should induce significant variations in the collected signal: the two eye pupil dots in eye-tracking immediately disappear (behavioral engagement) and an immediate drop in cognitive engagement (EEG signal) is observed. These specific components of the signal are used to evaluate the general validity of the synchronization. The recent publication of papers that fully or partially rely on this synchronization procedure, in the fields of information systems19, human-machine interactions21 and education9, 22, provides evidence of its effectiveness.

<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.

Figures 2 and 3 show screenshots of the results of the integration and synchronization of behavioral, emotional and cognitive engagement data in a behavioral analysis software application. In both figures, the left-hand section organizes the research subjects and the coding scheme. In the middle section, a video (with red dots) shows the subject&#8217;s eye gaze during the task. The subject&#8217;s behavioral engagement can be inferred based on what he/she is looking at during the task and what actions a...

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.

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 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;

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

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.

Biology

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.

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

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

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

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

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

Neuroscience

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.

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.

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 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

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

Education

JoVE Science Education

Advanced Biology

An Introduction to Learning and Memory

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

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

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

Psychology

Behavioral Science

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.

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

Neuropsychology

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.

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

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