The study was approved by the ethics committee of the SWPS University of Social Science and Humanities in Warsaw (number 46/2016).
1. Choosing an experimental site for logger deployment
<ol>
	<li>Choose an indoor experimental site that will allow mounting the logger in close proximity to the light source (for adequate light changes detection) as well as to gather the data on the behavior regarding the room occupancy status (for adequate movement detection) of individual participants (i.e., one at a time).</li>
	<li>Establish the intended use of the room and its designated users (males, females or co-ed). 
	NOTE: An example of an experimental site could be a public single-stall restroom due to the fact that this type of room is frequently and individually visited by its users. Furthermore, in most instances, it is possible to specify if the room is visited by males or females, based on its designation.</li>
	<li>Visit a chosen site and note the type / number of functioning light sources along with their light switches. Check whether multiple light sources are controlled by one or multiple light switches.</li>
	<li>Check the possibilities of mounting the logger next to the light source. Ensure that the place of logger mounting is not in proximity to any kind of heating sources (e.g., heaters, windows or mirrors) to ensure that only the body heat of the room users will be recorded.</li>
	<li>Acquire any necessary written permissions from the site owner for installation of the logger and conducting the experiment. Provide the site owner with the details of the experiment, loggers&#39; type and its application in written form.</li>
</ol>
2. Logger configuration before deployment
<ol>
	<li>Download and install the dedicated software (see Table of Materials) available for Windows/Mac platforms for launching, reading out, and plotting data from data loggers. 
	NOTE: Additionally a detailed description with basic system requirements and the software manual can be found on the manufacturer&#39;s website (see Table of Materials).</li>
	<li>Connect the logger via USB cable to the computer (plug the larger end of the USB interface cable into a USB port on the computer and the smaller end of the USB interface cable into the port on the device).</li>
	<li>Launch the software.</li>
	<li>Click the Launch icon on the toolbar (or select Launch command from the device menu) which opens loggers&#39; setup window. 
	NOTE: This option will be unavailable when the logger is not connected to the computer. The Launch Logger window is divided into the following three sections: 1) logger Information which presents model, serial number, deployment number, and current battery level of the selected logger; 2) list of the sensors available for the logger; and 3) deployment configuration. From this interface, one can set specific features that will configure the logger before deployment, such as those previously mentioned: sensor configuration, configuration of data display filters, start/stop logging, and display of the LCD screen.</li>
	<li>Enter a name for the launch which will be used as the default file name during read out and saving the data recorded by the logger.</li>
	<li>Select the Light sensor. Set the measurement to log State from the drop-down list, and choose the state description off/on from the drop-down list.</li>
	<li>Select the Occupancy sensor. Set the measurement to log State from the drop-down list, and choose the state description unoccupied/occupied from the drop-down list. 
	NOTE: Occupancy and light sensor channels can be configured to log state changes or runtime. On the state change setting, the work of the logger is event-dependent. While checking every second for a state change, the logger will only record a time-stamped value (how long an event lasts, date and time) when the state change occurs. On the other hand, on the runtime configuration setting, the logger checks and records the state of the sensor status once every second.</li>
	<li>Click the Filters button to enable automatic calculation of additional values (e.g., maximum, minimum, average, or total). 
	NOTE: Step 2.8 is optional and serves for filtering data for each series during loggers&#39; readout.
	<ol>
		<li>Select the sensor type of choice. Select the type of filter and the interval to use.</li>
		<li>Edit the Name and click Create New Series. Click Done.</li>
	</ol>
	</li>
	<li>Click the Advanced button to access the sensor properties.
	<ol>
		<li>Select the Light sensor. Select Set to maximum sensitivity for calibration and click the Save button. 
		NOTE: By default, the light sensor can be auto-calibrated at the location where the logger will be deployed using the control button located on the top panel. By simply pressing the calibration button, while on the site of deployment, the loggers&#39; LCD screen will display the signal strength of the light being monitored (use this option when light levels in the experimental site are unknown prior to deployment). The sensors&#39; sensitivity can also be adjusted via option &#34;Set to Maximum/Minimum Sensitivity&#34; - if the light levels in the place of deployment are known in advance. These forms of calibration ensure an accurate readout of light changes between ON and OFF states.</li>
		<li>Select the Occupancy sensor. Select a preset timeout value (i.e., 10 s; 30 s; 1 min; 2 min; 5 min) or select Custom and enter a value in minutes and seconds if needed. Click the Save button. 
		NOTE: The timeout value specifies the period of inactivity required for the sensor to consider the area unoccupied. By default, this attribute is set to 1 min.</li>
	</ol>
	</li>
	<li>Select when to launch the logger, depending on the experimental plan: 1) immediately; 2) at intervals (available when logging runtime); 3) on a specified date/time; or 4) by manually using the start button.</li>
	<li>Select when logger should stop logging: 1) when memory fills; 2) stop at a specified date/time; 3) stop manually or 4) never stop&#8212;resulting in the newest data overwriting the oldest.</li>
	<li>Click the Start button upon finishing the configuration. Unplug the logger from the computer.</li>
</ol>
3. Deploying the logger in the field settings
<ol>
	<li>Visit the experimental site before the time the logger will start recording the data.</li>
	<li>Equip the logger with an additional fiber optic light pipe (see Table of Materials) by connecting it to the back of the logger, in order to filter out any ambient light (coming from windows or mirror reflections) and ensure the most accurate readings. 
	NOTE: The light pipe is 30.48 cm long and can be bent to gain access to hard-to-reach areas, which can be also useful in hiding the logger from the sight of any room user.</li>
	<li>Mount the logger with the light pipe next to the designated light source with the use of: 1) four built-in magnets on the back of the logger that can attach it to a magnetic surface; 2) adhesive strip that can be attached to the back of the logger to mount it on walls or other flat surfaces; 3) any double-sided tape to stick the logger to a surface; or 4) the hook-and-loop strap which can be used through the mounting loops on both sides of the logger to mount it to a curved surface. 
	NOTE: The choice of the mounting method depends on the type of surface to which the logger will be mounted.</li>
	<li>Leave the experimental site for the time of data logging set or planned.</li>
	<li>After finishing recording, revisit the experimental site and remove the logger for the purpose of data readout.</li>
</ol>
4. Data readout
<ol>
	<li>Connect the logger via USB cable to the computer and launch the analysis software package dedicated to the data logger (see Table of Materials).</li>
	<li>Click the Readout device button from the control panel or select Readout from the device menu, which will enable the logger to unload the gathered data.</li>
	<li>Choose a location and a filename or accept the default location and name to save the data. Click Save and select the sensors and/or events to display in a graph and click Plot.</li>
	<li>Select the series to view on the table data and plot. Click the All or None button to select or deselect all series, or click the checkboxes to select or deselect individual series. 
	NOTE: The table data is presented numerically using added filters that were set before the deployment. Each column corresponds to the type of data gathered. For example, the column labeled &#34;light&#34; presents the occurrences of light-switching, whereas the column labeled &#34;occupancy&#34; presents the information about the presence of movement in the field where the logger was deployed. In each column, the state changes are presented dichotomously (the number &#34;0&#34; represents the light status of off in the &#34;light&#34; column and a lack of movement in the &#34;occupancy&#34; column).</li>
	<li>Select Export table data from the control panel. Choose destination folder for the export. 
	NOTE: It is possible to perform a data readout and export it to text, comma-separated values, or spreadsheet files. Other options, such as data plotting, are also available; however, due to the fact that most researchers work on exported data and use statistical packages, we decided to present the most basic data readout. For more information refer to the loggers manual19.</li>
</ol>

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The authors have nothing to disclose.

When planning to use more than one site (for logger deployment) at the same time, it should be ensured that each site has an identical architectural layout in order to exclude the possibility of occurrence of different behavioral patterns from participants (i.e., resulting from occupancy times and light-switching possibilities). A suitable site should be equipped with one or more light sources with only one corresponding light switch, visible to the occupant. If otherwise, one should plann to use one logger fir each ligh...

One of the most commonly used measures of pro-environmental behavior in psychology are self-reports in the form of surveys, interviews, or questionnaires1. Among the reasons indicated for this trend is simply the difficulty in conducting field experiments, which usually require a fair amount of resources and precise operationalization2,3. However, the tradeoff is worth the effort since it is well established that relying on self-reporting measures can be misleading in the prediction of objective behavior4,5,6.
While trying to avoid this problem, researchers that are focused on studying energy conservation behavior generally use observational (nominal categorization of observed events, e.g., turning on/off lights) or residual (quantifiable evidence of a past behavior, e.g., energy consumption in kWh) data as measurements of dependent variables7. Although both types of measurements are valuable, observational data is most commonly used in field experiments2,3,8, particularly when their dependent variables concern light-switching behavior.
Before obtaining observational data, researchers should consider several methodological issues, which are: 1) sample representativeness; 2) the number of observers in order to exclude possible human errors; 3) inter-observer agreement in order to exclude experimenter bias; 4) observer location, which should be concealed in order to reduce the possibility of being spotted by participants; 5) clearly and specifically defined observation coding; 6) pretest of observational measures; 7) observer training; and 8) establishing systematic timing of observation9. Even though most of the mentioned issues were already addressed&#8212;for example those that concern reliability analysis10 or coding observational data11&#8212;it seems that not all of them receive much attention in articles that describe experiments on light-switching behavior.
An analysis of four studies12,13,14,15 that were chosen for their similarity in experimental context (all of them concerned light-switching behavior in public bathrooms/restrooms) showed that even though the location details in each of the studies were precise, the observation measurement details varied. Since each study employed naturalistic observation, gathering information about the behavior of participants that were the opposite sex of observers was not always possible14 due to possible interference or violation of social norms (e.g., if a male experimenter were to enter a women&#39;s restroom or vice versa). In some instances, the precise data of the participants&#39; genders were not provided15. This seems to be a limitation when taking into consideration that gender can be an important factor in predicting pro-environmental behavior16.
The biggest differences, however, emerged in the description of observers and measurement times. Even though these descriptions will naturally differ on the basis of experimental location, the precise number of observers was not always provided14. Furthermore, the exact location of observers was not explicit12,14,15 which makes it hard to conduct possible replications and ensure that participants are unaware of being observed. Across four analyzed articles, only one provided a detailed description of the observer&#39;s location13.
Moreover, the exact times of observation intervals were provided only by one study12 whereas other studies either described overall study times (with a general description of how many times on each study day the observation took place)13,15 or did not describe it at all14. This can again impede replicating and establishing whether the observation timing was systematic and sufficient for the purposes of the study aims.
The limitations of these experiments are presented as guidelines and important points that should be taken into consideration in future research. In no case it was intended to undermine the importance of these studies. The indicated areas should be considered for maximizing study operationalization in order to facilitate replications, which play an important role in psychology17,18, and simplify the conduction of field experiments. However, it is questionable whether all of the mentioned issues can be dealt with by improving observation methods that ultimately rely on human observers.
For these reasons, the occupancy and light data logger (see Table of Materials) is a valuable tool that can be effectively used to gather information about a particular type of energy conservation behaviors, light-switching, without the limitations of using observers or ethical restrictions (the logger does not gather the audio-visual data). Overall, the aim of this article is to present the technical description and possibilities of one model of the occupancy and light data logger. To the authors&#39; knowledge, this is the first attempt to present this tool thoroughly in the context of its use in field experiments in psychology.
Loggers&#39; technical description 
The model of occupancy/light data logger (see Table of Materials) that was used for this article was equipped with standard memory capacity of 128 kB. The logger weights 30 g and its size is 3.66 cm &#215; 8.48 cm &#215; 2.36 cm. Additional details and the product manual can be found on the manufacturer&#39;s website19.
Control buttons, the light sensor and the battery tray are located on the top panel. The front panel consists of the occupancy sensor and an LCD screen, whereas the back panel is equipped with mounting magnets and loops (Figure 1). The USB 2.0 port is located on the bottom panel, to allow the connection of the logger to the computer with a USB cable in order to enable set-up before deployment and to later obtain readouts using analysis software package dedicated to this data logger.
The integrated light sensor (photocell) threshold is greater than 65 lx, which works with different light types (LED, CFL, fluorescent, HID, incandescent, natural) that can be found in most public spaces. Overall, the logger interprets light status changes (ON/OFF) depending on the strength of the light signal, more precisely, whether it drops below or rises above levels of the calibration threshold. It should also be noted that the sensor is secured from false detection of ON and OFF states by a built-in hysteresis level of approximately &#177;12.5%19.
A motion sensor determines whether the room is occupied or unoccupied. With the use of a pyroelectric infrared (PIR) sensor, it detects the motion of people by their body temperature (which differs from the temperature of the surroundings). The detection range of the discussed logger has a maximum of 5 m and the extended version of the logger has a range of 12 m. Horizontal detection performance works up to 94&#176; (&#177;47&#176;), and vertical up to 82&#176; (&#177;41&#176;).
The described model of occupancy/light data logger has been validated alongside Open Source Building Science Sensors and appears to provide a reliable measurement of light intensity and occupancy frequency21. Furthermore, these models of loggers have been shown useful in built-environment research, precisely in lighting applications22,23,24.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>HOBO Occupancy/Light (5m Range) Data Logger</td>
			<td>ONSET</td>
			<td>UX90-005</td>
			<td>As advertised by Onset - The HOBO UX90-005 Room Occupancy/Light Data Logger is available in a standard 128 KB memory model (UX90-005) capable of 84,650 measurements and an expanded 512KB memory version (UX90-005M) capable of over 346,795 measurements. For details and other products visit: <a href="https://www.onsetcomp.com/products/data-loggers/ux90-005" target="_blank">https://www.onsetcomp.com/products/data-loggers/ux90-005</a></td>
		</tr>
		<tr>
			<td>HOBO Light Pipe</td>
			<td>ONSET</td>
			<td>UX90-LIGHT-PIPE-1</td>
			<td>An optional fiber optic attachment or light pipe that eliminates effects of ambient light to ensure the most accurate readings. For details visit: <a href="https://www.onsetcomp.com/support/manuals/17522-using-ux90-light-pipe-1" target="_blank">https://www.onsetcomp.com/support/manuals/17522-using-ux90-light-pipe-1</a></td>
		</tr>
		<tr>
			<td>HOBOware</td>
			<td>ONSET</td>
			<td>-</td>
			<td>Setup, graphing and analysis software for Windows and Mac. There are two versions of HOBOware: HOBOware (available for free) and HOBOware Pro (paid version which allows for additional analysis with different loggers). Each of them are dedicated to HOBO loggers. For details visit: <a href="https://www.onsetcomp.com/products/software/hoboware" target="_blank">https://www.onsetcomp.com/products/software/hoboware</a></td>
		</tr>
	</tbody>
</table>

measuring light-switching behavior using an occupancy and light data logger

Due to discrepancies between self-reported and observed pro-environmental behavior, researchers suggest the use of more direct measures of behavior. Although direct behavioral observation may increase the external validity and generalizability of a study, it may be time-consuming and be subject to experimenter or observer bias. To address these issues, the use of data loggers as an alternative to natural observation can allow researchers to conduct broad studies without interrupting the participants&#39; naturally occurring behaviors. This article describes one of such tools&#8212;the occupancy and light data logger&#8212;with its technical description, deployment protocol, and information about its possible applications in psychological experiments. The results of testing the reliability of the logger in comparison to human observation is provided alongside an example of the gathered data during a 15-day measurement in public restroom (N = 1,148) that includes: 1) room occupancy changes; 2) indoor light changes; and 3) room occupancy time.

Loggers&#39; reliability test in comparison to human observation 
In order to test the reliability of the logger in comparison to human observation, a 4 h field test was conducted in a single-stall male restroom located on the University campus. Two male observers waited outside the restroom (approximately 5 m away from the front door) and independently recorded the visitors&#39; behavior in terms of occupancy rates/times and light switching (lights left ON or OFF upon exiting). Simultaneously, two ...

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Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

This article describes a procedure for using and deploying an occupancy and light data logger which allows collecting data about light-switching behavior of participants in field settings.

Due to discrepancies between self-reported and observed pro-environmental behavior, researchers suggest the use of more direct measures of behavior. ...

measuring-light-switching-behavior-using-an-occupancy-light-data

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5.7K Views.  Maria Curie-Skłodowska University. This article describes a procedure for using and deploying an occupancy and light data logger which allows collecting data about light-switching behavior of participants in field settings.

Video: Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Social exclusion is a complex social phenomenon with powerful negative consequences. Given the impact of social exclusion on mental and emotional health, an understanding of how perceptions of social exclusion develop over the course of a social interaction is important for advancing treatments aimed at lessening the harmful costs of being excluded. To date, most scientific examinations of social exclusion have looked at exclusion after a social interaction has been completed. While this has been very helpful in developing an understanding of what happens to a person following exclusion, it has not helped to clarify the moment-to-moment dynamics of the process of social exclusion. Accordingly, the current protocol was developed to obtain an improved understanding of social exclusion by examining the patterns of event-related brain activation that are present during social interactions. This protocol allows greater precision and sensitivity in detailing the social processes that lead people to feel as though they have been excluded from a social interaction. Importantly, the current protocol can be adapted to include research projects that vary the nature of exclusionary social interactions by altering how frequently participants are included, how long the periods of exclusion will last in each interaction, and when exclusion will take place during the social interactions. Further, the current protocol can be used to examine variables and constructs beyond those related to social exclusion. This capability to address a variety of applications across psychology by obtaining both neural and behavioral data during ongoing social interactions suggests the present protocol could be at the core of a developing area of scientific inquiry related to social interactions.

Research on social exclusion has grown tremendously in recent years. As the field expands, it is imperative to develop sophisticated methodologies allowing for the simultaneous measurement of neural and behavioral outcomes during social exclusion. This protocol utilizes event-related brain potentials to record ongoing neural activity during computerized social interactions.

Social exclusion is a complex social phenomenon with powerful negative consequences. Given the impact of social exclusion on mental and emotional health, ...

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Delay discounting refers to a decline in the value of a reward when it is delayed relative to when it is immediately available. Delay discounting tasks are used to identify indifference points, which reflect equal preference for two dichotomous reward alternatives differing in both delay and magnitude. Indifference points are key to assessing the shape of a delay-discounting gradient because they allow us to isolate the effect of delay on value. For example, if at a 1 week delay and a maximum of $1,000, the indifference point is at $700 we know that, for that participant, a 1-week delay corresponds to a 30% reduction in value. This video outlines an adjusting amount delay-discounting task that identifies indifference points relatively quickly and is inexpensive and easy to administer. Once data have been collected, non-linear regression techniques are typically used to generate discounting curves. The steepness of the discounting curve reflects the degree of impulsive choice of a group or individual. These techniques have been used with a wide range of commodities and have identified populations that are relatively impulsive. For example, people with substance abuse problems discount delayed rewards more steeply than control participants. Although degree of discounting varies as a function of the commodity examined, discounting of one commodity correlates with discounting of other commodities, which suggests that discounting may be a persistent pattern of behavior1.

Delay discounting refers to a decline in the value of a reward when it is delayed relative to when it is immediately available. We outline a computer-based delay discounting task that is easy to implement and allows for the quantification of the degree of delay discounting in human participants.

Delay discounting refers to a decline in the value of a reward when it is delayed relative to when it is immediately available. Delay discounting tasks ...

Feeding Experimentation Device (FED): Construction and Validation of an Open-source Device for Measuring Food Intake in Rodents

Food intake measurements are essential for many research studies. Here, we provide a detailed description of a novel solution for measuring food intake in mice: the Feeding Experimentation Device (FED). FED is an open-source system that was designed to facilitate flexibility in food intake studies. Due to its compact and battery powered design, FED can be placed within standard home cages or other experimental equipment. Food intake measurements can also be synchronized with other equipment in real-time via FED's transistor-transistor logic (TTL) digital output, or in post-acquisition processing as FED timestamps every event with a real-time clock. When in use, a food pellet sits within FED's food well where it is monitored via an infrared beam. When the pellet is removed by the mouse, FED logs the timestamp onto its internal secure digital (SD) card and dispenses another pellet. FED can run for up to 5 days before it is necessary to charge the battery and refill the pellet hopper, minimizing human interference in data collection. Assembly of FED requires minimal engineering background, and off-the-shelf materials and electronics were prioritized in its construction. We also provide scripts for analysis of food intake and meal patterns. Finally, FED is open-source and all design and construction files are online, to facilitate modifications and improvements by other researchers.

Feeding Experimentation Device FED: Construction and Validation of an Open-source Device for Measuring Food Intake in Rodents

Feeding Experimentation Device (FED) is an open-source device for measuring food intake in mice. FED can also synchronize food intake measurements with other techniques via a real-time digital output. Here, we provide a step-by-step tutorial for the construction, validation, and usage of FED.

Food intake measurements are essential for many research studies. Here, we provide a detailed description of a novel solution for measuring food intake in ...

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Progressive and irreversible muscle atrophy characterizes Spinal Muscular Atrophy (SMA) and other similar muscle disorder diseases. Objective assessment of muscle functions is an essential and important, although challenging, prerequisite for successful clinical trials. Current clinical rating scales restrain the movement abnormalities to certain predefined coarse-grained individual items. The Kinect 3-D sensor has emerged as a low-cost and portable motion sensing technology used to capture and track people&#39;s movement in many medical and research fields. A novel approach using this 3-D sensor was developed and a game-like test was designed to objectively measure the upper limb function of patients with SMA. The prototype test targeted joint movement capability. While sitting in a virtual scene, the patient was instructed to extend, flex, and lift the whole arm in order to reach and place some objects. Both kinematic and spatiotemporal characteristics of upper limb movement were extracted and analyzed, e.g., elbow extension and flexion angles, hand velocity, and acceleration. The first study included a small cohort of 18 ambulant SMA patients and 19 age- and gender-matched healthy controls. A comprehensive analysis of arm movement was achieved; however, no significant difference between the groups were found due to the mismatch of patient&#39;s capability and the test difficulty. Based on this experience, a second version of the test consisting of a modified version of the first game with increased difficulties and a second game targeting muscle endurance were designed and implemented. The new test has not been conducted in any patient groups yet. Our work has demonstrated the potential capability of the 3-D sensor in assessing such muscle function and suggested an objective approach to complement the clinical rating scales.

An objective measure of muscle functions is challenging especially in children. Based on a commercially available digital 3-D sensor, a child-friendly gaming test was developed to assess upper limb function for clinical trials.

Progressive and irreversible muscle atrophy characterizes Spinal Muscular Atrophy (SMA) and other similar muscle disorder diseases. Objective assessment ...

Measuring Active and Passive Tameness Separately in Mice

Domesticated animals such as dogs and laboratory mice show a high level of tameness, which is important for humans to handle them easily. Tameness has two behavioral components: a reluctance to avoid humans (passive tameness) and a motivation to approach humans (active tameness). To quantify these components in mice, we previously developed behavioral tests for active tameness, passive tameness, and the willingness to stay on a human hand, each designed to be completed within 3 min. The data obtained were used for selective breeding, with a large number of mice analyzed per generation. The active tameness test measures the movement of the mouse toward a human hand and the contact it engages in. The passive tameness test measures the duration of time that a mouse tolerates human touch. In the stay-on-hand test, a mouse is placed on a human hand and touched slowly using the thumb of that hand; the duration of time that the animal remains on the hand is measured. Here, we describe the test set-up and apparatus, explain the procedures, and discuss the appropriate data analysis. Finally, we explain how to interpret the results.

Tameness in animals includes a reduction of avoidance responses to humans (passive tameness) and an increase in active approaches to humans (active tameness). Here, we describe detailed protocols for three behavioral tests (active tameness, passive tameness, and stay-on-hand tests) to separately measure the active and passive tameness of mice.

Domesticated animals such as dogs and laboratory mice show a high level of tameness, which is important for humans to handle them easily. Tameness has two ...

An Experimental Paradigm for Measuring the Effects of Ageing on Sentence Processing

Previous studies have found that older adults have greater difficulties in processing syntactically complex sentences than younger adults. However, the exact regions where the difficulties arise have not been fully identified. In this study, a maze task was implemented to investigate how older adults and younger adults differentially processed two types of sentences with different levels of syntactic complexity, namely subject relative clauses and object relative clauses. Participants were asked to choose between two alternatives at each segment of the sentences. The task required participants to engage in a strictly incremental mode of processing. The reading times for each segment were recorded, allowing the quantification of the difficulty of sentence reading. The task allowed us to identify the exact locations of the processing difficulty and thus facilitate a more accurate assessment of the age-related decline in sentence processing. The results indicate that the effect of ageing was found mainly at the head nouns, but not at other regions of the sentences, a finding which suggests that the maze task is an effective method to identify the exact location of the ageing effect on sentence processing. The implications of this experimental paradigm for investigating the effect of ageing on sentence processing are discussed.

Here, we present a protocol to examine the age-related decline in sentence processing using a maze task that allowed us to localize the processing difficulty at each word of the sentences during reading.

Previous studies have found that older adults have greater difficulties in processing syntactically complex sentences than younger adults. However, the ...

Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization

Alcohol use disorder (AUD) remains a serious problem in our society. To develop effective interventions for addiction, it is important to understand the underlying neurobiological mechanisms, for which diverse experimental approaches and model systems are needed. The main ingredient of alcoholic beverages is ethanol, which causes adaptive changes in the central nervous system and behavior upon chronic intake. Behavioral sensitization (i.e., escalated responses) in particular represents a key adaptive change underlying addiction. Most ethanol-induced behavioral sensitization studies in animal models have been conducted on the locomotor activating effect of ethanol. A prominent effect of ethanol is behavioral disinhibition. Behavioral sensitization to the disinhibition effect of ethanol, however, is underrepresented. To address this issue, we developed the Flypub assay that allows measuring the escalated increase in disinhibited courtship activities upon recurring ethanol exposure in Drosophila melanogaster.&#160;Here, we report the step-by-step Flypub assay including assembly of ethanol exposure chambers, setup of the assay station, criteria for fly care and collection, ethanol delivery, quantification of disinhibited courtship activities, data processing and statistical analysis. Also provided are how to troubleshoot critical steps, overcome limitations and expand its utility to assess additional ethanol-induced behaviors. The Flypub assay in combination with powerful genetic tools in Drosophila melanogaster will facilitate the task of discovering the mechanism underlying ethanol-induced behavioral sensitization.

The Flypub assay measures the behaviors that the fruit fly Drosophila melanogaster displays under the influence of ethanol. The assay can be readily mastered by experimenters at all levels and applied to various vaporized stimuli, facilitating substance abuse and addiction studies.

Alcohol use disorder (AUD) remains a serious problem in our society. To develop effective interventions for addiction, it is important to understand the ...

An Improved Assay and Tools for Measuring Mechanical Nociception in Drosophila Larvae

Published assays for mechanical nociception in Drosophila have led to variable assessments of behavior. Here, we fabricated, for use with Drosophila larvae, customized metal nickel-titanium alloy (nitinol) filaments. These mechanical probes are similar to the von Frey filaments used in vertebrates to measure mechanical nociception. Here, we demonstrate how to make and calibrate these mechanical probes and how to generate a full behavioral dose-response from subthreshold (innocuous or non-noxious range) to suprathreshold (low to high noxious range) stimuli. To demonstrate the utility of the probes, we investigated tissue damage-induced hypersensitivity in Drosophila larvae. Mechanical allodynia (hypersensitivity to a normally innocuous mechanical stimulus) and hyperalgesia (exaggerated responsiveness to a noxious mechanical stimulus) have not yet been established in Drosophila larvae. Using mechanical probes that are normally innocuous or probes that typically elicit an aversive behavior, we found that Drosophila larvae develop mechanical hypersensitization (both allodynia and hyperalgesia) after tissue damage. Thus, the mechanical probes and assay that we illustrate here will likely be important tools to dissect the fundamental molecular/genetic mechanisms of mechanical hypersensitivity.

An Improved Assay and Tools for Measuring Mechanical Nociception in Drosophila Larvae

The goal of this protocol is to show how to perform an improved assay for mechanical nociception in Drosophila larvae. We use the assay here to demonstrate that mechanical hypersensitivity (allodynia and hyperalgesia) exists in Drosophila larvae.

Published assays for mechanical nociception in Drosophila have led to variable assessments of behavior. Here, we fabricated, for use with Drosophila ...

Investigating Migraine-Like Behavior Using Light Aversion in Mice

Migraine is a complex neurological disorder characterized by headache and sensory abnormalities, such as hypersensitivity to light, observed as photophobia. Whilst it is impossible to confirm that a mouse is experiencing migraine, light aversion can be used as a behavioral surrogate for the migraine symptom of photophobia. To test for light aversion, we utilize the light/dark assay to measure the time mice freely choose to spend in either a light or dark environment. The assay has been refined by introducing two critical modifications: pre-exposures to the chamber prior to running the test procedure and adjustable chamber lighting, permitting the use of a range of light intensities from 55 lux to 27,000 lux. Because the choice to spend more time in the dark is also indicative of anxiety, we also utilize a light-independent anxiety test, the open field assay, to distinguish anxiety from light-aversive behavior. Here, we describe a modified test paradigm for the light/dark and open field assays. The application of these assays is described for intraperitoneal injection of calcitonin gene-related peptide (CGRP) in two mouse strains and for optogenetic brain stimulation studies.

Rodents are not able to report migraine symptoms. Here, we describe a manageable test paradigm (light/dark and open field assays) to measure light aversion, one of the most common and bothersome symptoms in patients with migraines.

Migraine is a complex neurological disorder characterized by headache and sensory abnormalities, such as hypersensitivity to light, observed as ...

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Academic leaders all over the world are encouraging the use of active methodologies in teaching, especially in higher education. The reason for this is that social changes are happening at an ever-increasing rate, and they require students and teachers to develop digital skills. This is especially significant for health sciences degrees, in which future graduates must have effective problem-solving skills. To respond to this challenge, the use of a project-based learning (PBL) methodology, together with various monitoring techniques based on the use of Educational Data Mining (EDM) and mixed methods, will provide teachers with information about the effectiveness of the methodology and guide the implementation of personalized educational responses.
This study provides a protocol for the application of the PBL methodology in e-Learning and blended-Learning (b-Learning) teaching modalities for health sciences students studying occupational therapy in higher education. In addition, statistical techniques for the analysis of covariance and unsupervised learning allow differences to be detected between the two teaching modalities, thus specifying their effectiveness in terms of a range of variables related to behavioral patterns, performance, and satisfaction. Data visualization also helps in understanding the qualitative aspects of the learning process. These data will help teachers to produce more effective proposals for the implementation of the PBL methodology based on the based on the context of the teaching-learning process. Therefore, this protocol offers many resources and materials to help teachers implement the PBL methodology in e-Learning and b-Learning teaching methods.

Here, we present a protocol for the behavioral analysis of a project-based learning methodology for health sciences students (20-56 years old). The protocol facilitates the comparison of the participants' performance in e-Learning versus blended-Learning (b-Learning) through a monitoring tool. The results are analyzed using Educational Data Mining and qualitative techniques.

Academic leaders all over the world are encouraging the use of active methodologies in teaching, especially in higher education. The reason for this is ...