The authors would like to acknowledge the Royal Thai government, the European Regional Development Fund, and the Dutch provinces Gelderland and Overijssel (Grant number 2011P017004) for financial support. The content of the paper reflects only the views of the authors. The authors also appreciate help from Andrea Poelstra of GreenDino and Tobias Heffelaar of Noldus Information Technology for their valuable input on technical topics.

<ol>
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M., Botella, C., Garcia-Palacios, A., Villa, H., Perpi&#241;a, C., Alca&#241;iz, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Presence+and+Reality+Judgment+in+Virtual+Environments:+A+Unitary+Construct.">Presence and Reality Judgment in Virtual Environments: A Unitary Construct.</a> Cyberpsychol Behav. 3 (3), 327-335 (2004).</li><li>Lessiter, J., Freeman, J., Keogh, E., Davidoff, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+cross-media+presence+questionnaire:+The+ITC-Sense+of+Presence+Inventory.">A cross-media presence questionnaire: The ITC-Sense of Presence Inventory.</a> Presence-Teleop Virt. 10 (3), 282-297 (2001).</li><li>Witmer, B. G., Singer, M. 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B. 364 (1535), 3549-3557 (2009).</li><li>Zygouris, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Can+a+virtual+reality+cognitive+training+application+fulfill+a+dual+role?+Using+the+virtual+supermarket+cognitive+training+application+as+a+screening+tool+for+mild+cognitive+impairment.">Can a virtual reality cognitive training application fulfill a dual role? Using the virtual supermarket cognitive training application as a screening tool for mild cognitive impairment.</a> J. Alzheimers Dis. 44 (4), 1333-1347 (2015).</li><li>Waterlander, W. E., Mhurchu, C. N., Steenhuis, I. H. 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E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+Point-of-Sale+Tobacco+Displays+and+Graphic+Health+Warning+Signs+on+Adults:+Evidence+From+a+Virtual+Store+Experimental+Study.">Influence of Point-of-Sale Tobacco Displays and Graphic Health Warning Signs on Adults: Evidence From a Virtual Store Experimental Study.</a> Am J Public Health. 104 (5), 888-895 (2014).</li><li>van Herpen, E., Immink, V., van Den Puttelaar, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Organics+unpacked:+The+influence+of+packaging+on+the+choice+for+organic+fruits+and+vegetables.">Organics unpacked: The influence of packaging on the choice for organic fruits and vegetables.</a> Food Qual Prefer. 53, 90-96 (2016).</li><li>Ducrot, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+Different+Front-of-Pack+Nutrition+Labels+on+Consumer+Purchasing+Intentions:+A+Randomized+Controlled+Trial:+A+Randomized+Controlled+Trial.">Impact of Different Front-of-Pack Nutrition Labels on Consumer Purchasing Intentions: A Randomized Controlled Trial: A Randomized Controlled Trial.</a> Am J Prev Med. 50 (5), 627-636 (2015).</li><li>van Herpen, E., Pieters, R., Zeelenberg, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=When+demand+accelerates+demand:+Trailing+the+bandwagon.">When demand accelerates demand: Trailing the bandwagon.</a> J Consum Psychol. 19 (3), 302-312 (2009).</li><li>Berneburg, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interactive+3D+simulations+in+measuring+consumer+preferences:+Friend+or+foe+to+test+results.">Interactive 3D simulations in measuring consumer preferences: Friend or foe to test results.</a> J. interact. advert. 8 (1), 1-13 (2007).</li><li>van Herpen, E., van den Broek, E., van Trijp, H. C., Yu, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Can+a+virtual+supermarket+bring+realism+into+the+lab?+Comparing+shopping+behavior+using+virtual+and+pictorial+store+representations+to+behavior+in+a+physical+store.">Can a virtual supermarket bring realism into the lab? Comparing shopping behavior using virtual and pictorial store representations to behavior in a physical store.</a> Appetite. 107, 196-207 (2016).</li><li>Khan, V. -. J., Nuijten, K. C., Desl&#233;, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pervasive+Application+Evaluation+within+Virtual+Environments.">Pervasive Application Evaluation within Virtual Environments.</a> Proc. PECCS. , 261-264 (2011).</li><li>Rebelo, F., Duarte, E., Noriega, P., Soares, M. M., Karwowski, W., Soares, M. M., Stanton, N. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Virtual+reality+in+consumer+product+design:+Methods+and+applications.">Virtual reality in consumer product design: Methods and applications.</a> Human factors and ergonomics in consumer product design. , 381-402 (2011).</li><li>Ruppert, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+directions+in+the+use+of+virtual+reality+for+food+shopping:+Marketing+and+education+perspectives.">New directions in the use of virtual reality for food shopping: Marketing and education perspectives.</a> J Diabetes Sci Technol. 5 (2), 315-318 (2011).</li><li>Waterlander, W., Mhurchu, C. N., Steenhuis, I., Xinxing, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+use+of+virtual+reality+in+studying+complex+interventions+in+our+every-day+food+environment.">The use of virtual reality in studying complex interventions in our every-day food environment.</a> Virtual reality-Human computer interaction. , 231-260 (2012).</li><li>Waterlander, W. E., Jiang, Y., Steenhuis, I. H. M., Mhurchu, C. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+a+3D+virtual+supermarket+to+measure+food+purchase+behavior:+A+validation+study.">Using a 3D virtual supermarket to measure food purchase behavior: A validation study.</a> J Med Internet Res. 17 (4), (2015).</li><li>Mikkelsen, B., H&#248;eg, E., Mangano, L., Serafin, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Virtual+Foodscape+Simulator-gaming,+designing+and+measuring+food+behaviour+in+created+food+realities.">The Virtual Foodscape Simulator-gaming, designing and measuring food behaviour in created food realities.</a> Proc Meas Behav 2016. , (2016).</li></ol>

The authors have nothing to disclose.

The virtual store is one of the more advanced computer technologies that have been developed to create virtual environments in which people can experience and react to close-to-reality objects. Generally, the desktop virtual store consists of user-friendly interfaces that require a short time to understand. However, a number of critical points need to be accounted for. First, clear research objectives are needed beforehand to specify the starting points when building the virtual store. This includes a plan about the products; the type, placement, and number of shelves; the location of product categories on these shelves; the type and location of products within the product categories; and other elements (e.g., poster, signage, and special displays). Moreover, it is important to decide which model (2D or 3D) of a digital representation of objects will be used (see Figure 14). The 3D models are virtual representations, with height, width, and depth, in which all sides are represented in detail. In contrast, the 2D model gives the illusion of a 3D representation by presenting an object in a cube frame (3D shape), with realistic visuals of the front of the object. The other sides of the 2D models are roughly shown without detail. Different forms of representations give rise to different user experiences and different senses of immersion. The 3D model that shows all details of an object may give a higher sense of presence and immersion (PI and Psi) than the 2D model. However, the 2D model is flexible and easy for a researcher to use, and the size of the cube frame can be easily adjusted. Thus, the choice of the virtual representation depends upon the research aims. Second, after all stores are built, the researcher should run and test all versions of the virtual store by visiting each store and picking up, selecting, and returning products to verify that the data is stored correctly. Third, because the study consists of several steps, clear instructions and detailed virtual store manuals are extremely important. The instructions should indicate what participants should and should not do in each step. Fourth, the practice session is vital to familiarizing participants with the virtual store and minimizing biases generated from different computer skills. Last, researchers should be cautioned to save data as frequently as possible to avoid any potential data loss. 
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/55719/55719fig11.jpg" /> 
Figure 14: &#160;An example of a product in a 3D model (left) and a 2D model (right). When participants click on a product, the 3D model can be rotated on-screen to illustrate all sides of the product, whereas the 2D model illustrates only the front side of the product and cannot be rotated. <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The use of virtual stores in consumer research has advantages over more traditional research methods. A virtual store is a tightly controlled yet realistic environment17,19, thereby providing the internal validity of a controlled experiment while maintaining a high degree of external validity as well. It thus combines the advantages of both field and laboratory approaches20. This implies that consumer behavior can be observed and measured in a realistic context, with less concern for socially desirable answers than in other research methods, such as surveys and focus groups21. A recent study has indicated that, compared to a method of using photographs to display a store shelf, the use of virtual reality results in consumer in-store behavior that more closely resembles the behavior demonstrated in a physical store, based on several parameters(i.e. feelings of presence, type of brands selected, and responses to the location of products in the display)18. An additional advantage of using virtual reality is that changes in the store environment can be made without having to rely on complex implementation processes in real-life settings22,23. This provides flexibility for the researcher. As a result, the use of a virtual store has clear benefits when the objective of a study is to examine consumer responses to products that are not yet available in the market (e.g., in early stages of new product development), to examine consumer responses to factors that are costly or difficult to change in real life (e.g., overall store layout), and/or to examine routinized behaviors in familiar environments.
Despite the stated advantages of the virtual store, several limitations need to be carefully considered. The major limitations, at this stage of development, relate to: 1) the time and space needed per participant, 2) the potential skill-related bias, 3) the costs involved in adapting new environments, and 4) real behavior and incentives. Currently, the virtual store can be used by only one person at a time. In particular, a number of participants are sampled in a virtual laboratory or an experimental area in order to run simulations. This limitation of time and physical space for the virtual store experiment restricts sample size and types of target groups. In addition, the restriction on the types of target groups is also caused by the skills required for participants to use the computer. Gamers or younger participants are likely to be able to handle the program more efficiently than the elderly or persons with low computer skills. Another limitation of the virtual store is that the adaptation of the store and the product library is in the development stage. If one wants to use a complex store design or store elements or products that are different from the available templates (e.g., enlarging the store size or including new store elements, such as display tables), the program needs to be adjusted. Thus, cost and time are incurred for the preparation of data collection. Lastly, even though previous studies have shown that the virtual store reflects behavior in the physical store more closely than does an experiment using pictorial stimuli, participants tend to buy more products in the laboratory setup than they do in actual stores. Thus, although the use of a virtual store increases realism compared to the use of pictures, several differences from real-life behavior remain18. To be cautious, this must be considered when interpreting results from a study using the virtual store.
There is a vast range of different technological features and systems for virtual reality applications. These systems mainly vary on aspects of equipment mobility, user interfaces, and development costs. The costs for equipment and licenses vary and are subject to drastic changes due to technological developments. In general, the costs per participant are higher when more behavior data is needed with higher-level 3D simulations. The use of a different system or interface may counteract some of the mentioned limitations, but at a cost in terms of money or flexibility. Specifically, the first limitation, on the time and space needed per participant, can be counteracted by using smartphone technologies. Smartphones, in combination with a designated headset, can render a full, immersive, 360&#176; environment. Limitations on space are as low as possible, since it does not cost more space than what one would normally use. Due to the widespread use of smartphones and the low cost of designated headsets, multiple people can use it at the same time. The downside of this technology is that smartphones have a lower computing power and thus can only handle less-difficult environments. The second limitation is the potential skill-related bias, a limitation that any system must deal with. Some systems, such as the Cave system, simulate natural movements24, which potentially could reduce this bias. The Cave system uses projector screens and head tracking, which allows participants to physically move through a limited space and to orient their head arbitrarily. Such a system, however, is not or is hardly mobile and requires much more developmental and hardware costs. The third limitation, the costs that are involved with adapting the store products and environment, are dependent upon the degree of simulation. It is possible to simulate a stationary environment based on a picture, but as soon as more detail, such as a 3D world or 3D products, are needed, one is dependent upon the availability of these objects in 3D. The last limitation, the simulation of real behavior and incentives, are likely dependent on the aforementioned factors of mobility, skill bias, and, in general, the degree of immersion. Mobile units can be used in a relevant context (e.g., in the actual supermarket), thereby making the incentive and the purpose of the visit real (e.g., buying a product virtually results in actually buying the product in real life). Furthermore, it can be expected that, when the user interface closely resembles natural movement, it will better resemble real-life behavior. Lastly, the level of immersion achieved by the current virtual store is between those of a regular desktop and a semi-immersive virtual reality projection8. Since other virtual store systems are in the early stages of development, studies describing and comparing different virtual store systems are scarce. A comparison of shopping behavior under different levels of immersion is yet to be conducted.
As virtual reality has become a widely used technology, outside the scope of computer games, virtual reality technology is likely to enter the market of home users (e.g., by television, internet or mobile application). This will potentially enable researchers to do virtual reality testing outside the laboratory. Moreover, this development opens up ample opportunities to measure, research, and understand the behavior of people on a broader scale in terms of groups and areas (e.g., in developing countries or rural areas with limited accessibility to technology). The external validity of the research will consequently be enhanced. With the advancement of this technology on the consumer market, virtual reality research could further develop from supporting simulations to the direct measurement and tracking of real behavior. Just like people surfing on the web or consumers choosing in a web shop are already intensely tracked to predict or influence behavior, the same type of behavioral measures exist (and will come to exist) for simulated virtual worlds. Another potential development is foreseen in the area of generating personalized environments. Several websites are already automatically adjusted to the individual who visits them. Examples of such websites are online retailers that give suggestions based upon aspects such as location, previous purchases, and Facebook (i.e., a social media and networking platform), which personalizes not only the advertisements, but also other content to fit personal preferences. The same could happen for virtual worlds. In practice, people could, for example, select personalized supermarkets, design or choose the manner in which they would prefer to be guided (e.g., &#34;guide me towards sustainable product choices&#34;), or even limit the choices they can make (e.g., only products for people with a specific disease).
In summary, unravelling the mysteries of consumer behavior cannot be achieved by any stand-alone research method. Thus, to compare or combine insights, various data collection tools must be used. The virtual reality developments have taken great steps in the last few years. Now, it is the time to link these methods to traditional methods so that new insights can emerge. There are multiple options of the virtual store, all with their respective advantages and disadvantages. The virtual store described here is unique in that there is an easy editor to build a virtual store that includes a range of options in order to collect behavioral data. An example of research with the virtual store presented here lays the groundwork as a universal way of measuring consumer behavior in virtual-reality research.

It is undeniable that understanding consumers&#39; in-store behavior is of critical importance to achieve effective retail marketing. To aid in this understanding, advanced virtual reality technology, known as the virtual store, can enable studies of consumer behavior using computationally created virtual environments. The virtual-store approach uses a virtual reality system to generate realistic and immersive three-dimensional virtual store environments in which people can interact with the objects in the store. In such virtual store environments, people experience artificially created sensory experiences. Virtual store environments can be either realistic representations of store environments that exist in reality, or imaginary store environments. In addition, the virtual store can be seen as an intermediate tool between traditional consumer research (i.e., text-based surveys, focus groups, or lab experiments), controlled field experiments (i.e., in mock store environments), and field studies (i.e., video captures, personal observations, or tests of product sales promotion)1.
Virtual reality applications have considerable research history. As early as 1965, Sutherland2&#160;described his &#34;ultimate display&#34; concept, which includes a virtual world that provides sound and tactile feedback. Originally, attention was mainly focused on the technological hardware, but as this does not provide insights into the effects of virtual reality systems, attention has shifted to the human experience3,4. The sense of &#34;presence,&#34; of being in the computer-generated world, has consequently become a key to virtual-reality experiences5,6. Presence has been defined as the &#34;subjective experience of being in an environment, even when one is physically situated in another&#34;.7 From this point of view, &#34;sense of presence&#34; can be retrieved from a participant and refers to the extent to which a person perceives him/herself to be in an environment. Alternatively, Slater8 has distinguished between the concepts of presence and immersion, called &#34;place illusion&#34; (PI) and &#34;plausibility illusion&#34; (Psi). PI relates to having a sensation of being in a real place. It is assessed by a set of valid actions or responses that participants can perform to change their perceptions or the environment (e.g., moving the head and eye to change the gaze direction or grasping some object to move it). PI is high when a similar set of responses to change perceptions are required in the virtual reality system compared to the response expected in an equivalent physical environment. Psi accounts for what is perceived in the virtual reality, referring to the illusion that it is actually occurring. A vital component that can lead to Psi is for the virtual reality to provide the illusion that events in the virtual environment over which a participant does not have direct control refer directly to him/herself. Psi can be measured by tracing any actions or responses that people manifest in response to changes in the virtual reality that originated from outside. For example, if people&#39;s heart rates increase when they see an avatar in the virtual environment, this can represent a similar reaction to the real world. Thus, this virtual reality system provides high Psi.
The virtual store technology has been introduced in business and academics to serve several purposes. It can be used as a managerial aid, for instance to assist category managers of companies in developing a shelf plan for their products. Virtual stores also have their use in clinical settings, to measure emotional responses to food for patients with an eating disorder1 or as a screening tool for mild cognitive impairment9. A more common use of virtual stores in research, however, is to assess consumer in-store behavior and consumer responses to changes in the store environment, such as price changes10,11,12, different setups of point-of-sale displays13, different packaging options14, different nutritional labels on the backsides of product packages15, and stock levels16. In addition, the virtual store is currently used to help create and test public health interventions to stimulate healthier food choices among children17. Due to various benefits stated previously, virtual store technology and hardware are in rapid development. Therefore, this paper will focus on the human experience and describe the essential elements of studies using virtual reality in general. All essential information obtained from the current virtual store system will be demonstrated.
Currently available virtual store systems can be briefly categorized as: 1) non-immersive (e.g., desktop), 2) semi-immersive (e.g., projection, CAVE-systems), and 3) fully-immersive (e.g., head-mounted displays). Each system likely brings different levels of immersion, presence, PI, and Psi depending upon the support system. However, because the measures of immersion, presence, PI, and Psi are bound to the specific sensorimotor contingencies that each system supports, a comparison of these indicators across different systems has been deemed impossible8. In recent years, desktop virtual stores have received more attention and have been used increasingly in research. Even though the virtual store has been regarded as a promising tool for in-store consumer behavior research, expertise on how to use such a virtual store is required to ensure the timely and correct preparation and implementation of experiments. However, up to now, reported studies that comprehensively describe the procedure to conduct virtual store experiments are very scarce. Therefore, this work aims to describe a protocol for conducting consumer research with the desktop virtual store, which is of vital importance.
Generally, research with a virtual store requires: 1) equipment to display the virtual environment, 2) an editor program to enable researchers to build the virtual environment, 3) a virtual representation of the studied object (e.g., several elements of a store and products), 4) a consumer interface to navigate the virtual environment and make choices, 5) procedures for running the data collection itself, and 6) a data management system that facilitates data storage and analysis. Most of these will likely be managed by a virtual shop company and a programmer. Researchers should know: 1) how to create a retail store for an experiment in an editor program, 2) how to run data collection with the consumer interface, and 3) how to organize all outputs in the data management program and export outputs to be put into a statistical program. The current paper will address this information by giving detailed protocol steps for conducting experiments with the desktop virtual store. Additionally, advantages and limitations of using the virtual store in consumer research will be discussed. The detailed protocol described in this paper can be used to help researchers start and conduct virtual store research.
The desktop virtual store used in this paper requires hardware (i.e., personal computers (PC), liquid-crystal display (LCD) screens, a three-dimensional (3D) space navigator, a mouse, and a keyboard) and software (i.e., to design a shop and to shop like a consumer in a 3D virtual store). This particular system has been used in prior studies14,18.

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			<td height="63" style="height:63px;width:196px;">Virtual Supermarket Software</td>
			<td style="width:89px;">GreenDino BV</td>
			<td style="width:143px;">http://www.greendino.nl/virtual-labs.html</td>
			<td style="width:229px;">This software consists of editor, product library and consumer interface.&#160;</td>
		</tr>
		<tr height="105">
			<td height="105" style="height:105px;width:196px;">Data Management Software: Observer XT&#160;</td>
			<td style="width:89px;">Noldus Information Technology</td>
			<td style="width:143px;">http://www.noldus.com/human-behavior-research/products/the-observer-xt&#160;</td>
			<td style="width:229px;">This software records observational data and facilitates the exportation of researcher-specified data sets using filters</td>
		</tr>
		<tr height="130">
			<td height="130" style="height:131px;">3D SpaceNavigator</td>
			<td style="width:89px;">3Dconnexion</td>
			<td style="width:143px;">http://www.3dconnexion.eu/index.php?id=26&#38;redirect2=www.3dconnexion.eu</td>
			<td style="width:229px;">A 3D SpaceNavigator allows participants to walk and make turns in the virtual store. In addition, it can be used by participants to adjust their eye-level during a shopping trip.</td>
		</tr>
		<tr height="126">
			<td height="126" style="height:126px;width:196px;">3D moddeling software (e.g. Blender or 3DS Max)</td>
			<td style="width:89px;">Blender Foundation / Autodesk</td>
			<td style="width:143px;">https://www.blender.org/ http://www.autodesk.nl/products/3ds-max/overview</td>
			<td style="width:229px;">In case 3D models need to be made or adjusted 3D modeling software is needed. Many objects can be found online under different licencing agreements.&#160;</td>
		</tr>
		<tr height="105">
			<td height="105" style="height:105px;width:196px;">Contract Reseach&#160;</td>
			<td style="width:89px;">Wageningen Univeristy and Research</td>
			<td style="width:143px;">http://www.wur.nl/en/Expertise-Services/Research-Institutes/Economic-Research.htm</td>
			<td style="width:229px;">The socio-economic research institute (Wageningen Economic Research)&#160; with experience in conducting the consumer research with the virtual store.&#160;</td>
		</tr>
	</tbody>
</table>

using a virtual store as a research tool to investigate consumer in-store behavior

People&#39;s responses to products and/or choice environments are crucial to understanding in-store consumer behaviors. Currently, there are various approaches (e.g., surveys or laboratory settings) to study in-store behaviors, but the external validity of these is limited by their poor capability to resemble realistic choice environments. In addition, building a real store to meet experimental conditions while controlling for undesirable effects is costly and highly difficult. A virtual store developed by virtual reality techniques potentially transcends these limitations by offering the simulation of a 3D virtual store environment in a realistic, flexible, and cost-efficient way. In particular, a virtual store interactively allows consumers (participants) to experience and interact with objects in a tightly controlled yet realistic setting. This paper presents the key elements of using a desktop virtual store to study in-store consumer behavior. Descriptions of the protocol steps to: 1) build the experimental store, 2) prepare the data management program, 3) run the virtual store experiment, and 4) organize and export data from the data management program are presented. The virtual store enables participants to navigate through the store, choose a product from alternatives, and select or return products. Moreover, consumer-related shopping behaviors (e.g., shopping time, walking speed, and number and type of products examined and bought) can also be collected. The protocol is illustrated with an example of a store layout experiment showing that shelf length and shelf orientation influence shopping- and movement-related behaviors. This demonstrates that the use of a virtual store facilitates the study of consumer responses. The virtual store can be especially helpful when examining factors that are costly or difficult to change in real life (e.g., overall store layout), products that are not presently available in the market, and routinized behaviors in familiar environments.

The virtual store displayed using a PC with three 42-in LCD screens has been applied to examine the effects of supermarket layout on consumer shopping behavior (e.g., total shopping time, movement duration and speed, total number of products examined, and total number of products purchased) and perceived shopping experience. The virtual store enables the researcher to flexibly modify the attributes of store shelves (i.e., shelf length and shelf orientation) and to examine these effects in a laboratory setting.
As an example, results from the store layout study are provided. In the study, supermarket stores were built using 4 different layouts, in which shelf length (short versus long shelves) and shelf orientation (paralleled arrangement versus unparalleled arrangement) were varied. These stores are depicted in Figure 7.
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/55719/55719fig7.jpg" /> 
Figure 7: Pictures of four store layouts in the store layout experiment. The layouts differ in shelf length and shelf orientation: 1) store with long and parallel shelves, 2) store with short and parallel shelves, 3) store with long and non-parallel shelves, and 4) store with short and non-parallel shelves. <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The study was performed in accordance with the &#34;Generic Protocol Food Choice Simulator&#34; and approved by the Social Sciences Ethics Committee of Wageningen University. All participants signed an informed consent form prior to participating in the experiments. In the present example, participants (n = 241, 71% female) were divided into four groups; each group visited one of four store layouts. Participants were trained on how to use the virtual store in a practice session. Next, they completed a shopping motivation manipulation task that asked them to recall shopping trips with either hedonic or utilitarian shopping motivations. Subsequently, the participants started the main test, in which they were requested to shop for a dinner using a shopping list. Participants were asked to imagine that they were shopping with either hedonic or utilitarian motivation (the same motivation as in the previous recall task was assigned). The shopping list consisted of fixed-choice (8 pre-determined types of products) and free-choice products (undetermined products from the fruit and vegetable category). The free-choice products were used to test the effects of store layout on the number of products purchased. Once the participants finished shopping, they filled in a computer-based questionnaire to evaluate their shopping experiences, perceptions about the store, and willingness to revisit the store.
The data management program recorded shopping behavior (e.g., total shopping time, moving speed, and total number of products purchased). Afterwards, variables were exported from the data management program to 3 separated tables: Table 1, Table 2, and Table 3. Table 1 presents the total shopping time, the total number of products examined, and the total number of products purchased by each participant. Table 2 presents the total movement duration (i.e., shopping time) that was selected from a filter of speeds higher than 0.001 m/s. Table 3 presents the moving speed that can subsequently be used to calculate the walking distance (walking distance (m) = average moving speed (m/s) x total moving time (s)).
<img alt="Table 1" src="/files/ftp_upload/55719/55719tbl1.jpg" /> 
Table 1: Examples of shopping-related behavioral data from each participant (i.e., total shopping time, total number of products examined, total number of products purchased, and total number of products returned), exported from the data management program. All shopping-related behavioral data from each participant should be organized in one row before transferring it to SPSS or other statistical programs. This exported data will be stored to the file called &#34;Behavioral data&#34; in the export folder of the data management program. <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719tbl1large.jpg" target="_blank">Please click here to view a larger version of this table.</a>
<img alt="Table 2" src="/files/ftp_upload/55719/55719tbl2.jpg" /> 
Table 2: Examples of movement-related data (i.e., the moving speed and the walking position of each participant), exported from the data management program. The movement-related data is selected when participants moved with speeds higher than 0.100 m/s. This selection filters out all data that occurred when participants stood still. All movement-related data from each participant should be organized in one row before being transferred to SPSS or other statistical programs. This exported data will be stored to a file called &#34;Numerical data&#34; in the export folder of the data management program. <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719tbl2large.jpg" target="_blank">Please click here to view a larger version of this table.</a>
<img alt="Table 3" src="/files/ftp_upload/55719/55719tbl3.jpg" /> 
Table 3: Examples of movement duration (indicated in the shopping duration column), exported from the data management program. The movement duration is retrieved from the behavioral data table that filters out the time during which participants did not move (speed &#60;0.100 m/s). This duration is shorter than the total shopping duration. The exported data will be stored to a file called &#34;Behavioral data&#34; in the export folder of the data management program. <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719tbl3large.jpg" target="_blank">Please click here to view a larger version of this table.</a>
Once the data was exported, univariate ANOVA was applied to analyze the effects of shelf length and shelf orientation on in-store shopping behavior. The effects of store layout can be presented in various forms, such as bar charts and tables.
Figure 8 displays the total number of products examined and the total number of products purchased in the supermarkets with different store layouts. The results from the virtual store confirmed that store layout attributes, specifically the interaction of shelf length and shelf orientation, influenced the number of products examined (F (1,237) = 4.66, p &#60; .05, &#951;p&#178; = .02) and the number of products purchased (F (1,237) = 3.47, p = .06, &#951;p&#178; = .01). The findings showed that when shelves were placed in parallel, the length of the shelves did not affect the number of products examined (Mshort &#177; SDshort = 16.12 &#177; 5.37, Mlong &#177; SDlong = 17.12 &#177; 5.99, F (1,237) = 0.81, p = .37, &#951;p&#178; = .00), nor the number of products purchased (Mshort &#177; SDshort = 12.00 &#177; 2.77, Mlong &#177; SDlong = 12.22 &#177; 2.37, F (1,237) = 0.24, p = .63, &#951;p&#178; = .00). In contrast, when the orientation of the shelves was unparalleled, shorter shelf lengths stimulated a higher number of products examined (Mshort &#177; SDshort = 17.62 &#177; 6.48, Mlong &#177; SDlong = 15.23 &#177; 6.45, F (1,237) = 4.65, p &#60; .05, &#951;p&#178; = .02) and purchased than longer shelf lengths (Mshort &#177; SDshort = 12.30 &#177; 2.15, Mlong &#177; SDlong = 11.35 &#177; 2.37, F (1,237) = 4.61, p &#60; .05, &#951;p&#178; = .02).
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/55719/55719fig8.jpg" /> 
Figure 8: The total number of products examined (left) and the total number of products purchased (right) in a supermarket with different store layouts (short versus long shelves placed in a paralleled or in an unparalleled orientation). The total number of products examined (packages or items) increased every time the participants clicked on a product. This number differs from the total number of products purchased (packages or items), by which the number of products in the purchase basket was recorded. Participants were allowed to return any selected products. p &#60;0.10+, p &#60;0.05*, p &#60;0.01**, p &#60;0.001*** <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In addition to product choice behaviors, the virtual store can also record time and movement-related behaviors, such as, the shopping time and the walking distance. Figure 9 and Figure 10 show the effects of shelf attributes on the shopping time and walking distance of participants, respectively.
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/55719/55719fig9.jpg" /> 
Figure 9: Total shopping time (s) participants spent in the supermarket with different shelf lengths and shelf orientations. The total shopping time accounts for the time participants spent between entering the store and leaving the store. The data management program also allows researchers to filter out the time that participants spent in a specific area. p &#60;0.10+, p &#60;0.05*, p &#60;0.01**, p &#60;0.001*** <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/55719/55719fig10.jpg" /> 
Figure 10: The walking distance of participants in the supermarket with different shelf lengths and shelf orientations. The walking distance was determined by multiplying the moving time (s) with the average shopping speed (m/s). The duration of the moving time used to calculate walking distance differs from the total shopping time because the moving time is exclusively recorded during participant movement. In contrast, the total shopping time accounts for the movement time and the time spent viewing and selecting products. Thus, the total moving time can be attained by only selecting the time during which participants move faster than 0.100 m/s. p &#60;0.10+, p &#60;0.05*, p &#60;0.01**, p &#60;0.001*** <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In addition to the effects of shelf attributes, the current research also focuses on shopping motivations to understand their influence on in-store shopping behavior. The results reveal significant main effects of shopping motivations on all in-store behavioral variables. Consumers with a hedonic motivation searched for (i.e., clicked on) (Mhedonic &#177; SDhedonic = 17.97 &#177; 6.93) and purchased more products (Mhedonic &#177; SDhedonic = 12.25 &#177; 2.42) than consumers with a utilitarian motivation (products examined: Mutilitarian &#177; SDutilitarian = 15.10 &#177; 4.82, products purchased: Mutilitarian &#177; SDutilitarian = 11.69 &#177; 2.43, see Figure 11). They also spent more time (Mhedonic &#177; SDhedonic = 607.18 &#177; 205.07 s, Mutilitarian &#177; SDutilitarian = 480.94 &#177; 134.25 s, see Figure 12) and walked longer distances (Mhedonic &#177; SDhedonic = 89.87 &#177; 31.15 m, Mutilitarian &#177; SDutilitarian = 80.73 &#177; 34.08 m, see Figure 13). The interaction effect of shopping motivation and store shelf attributes was not significant.
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/55719/55719fig12.jpg" /> 
Figure 11: The total number of products examined (left) and the total number of products purchased (right) by participants with utilitarian and hedonic shopping motivation. The number of products examined and purchased are presented across all store layouts. Participants were assigned to shops under either utilitarian or hedonic shopping motivation prior to a shopping task. The shopping motivation was manipulated by a motivation manipulation task and a shopping situation. p &#60;0.10+, p &#60;0.05*, p &#60;0.01**, p &#60;0.001*** <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 13" class="xfigimg" src="/files/ftp_upload/55719/55719fig13.jpg" /> 
Figure 12: Total shopping time (s) spent in the supermarkets by participants with utilitarian or hedonic shopping motivation. The total shopping time accounts for the entire time that participants with different shopping motivations spent in the virtual supermarket across all store layouts. p &#60;0.10+, p &#60;0.05*, p &#60;0.01**, p &#60;0.001*** <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig13large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 14" class="xfigimg" src="/files/ftp_upload/55719/55719fig14.jpg" /> 
Figure 13: The distance that participants with utilitarian and hedonic shopping motivation walked. This figure shows the average walking distance across all store layouts. p &#60;0.10+, p &#60;0.05*, p &#60;0.01**, p &#60;0.001*** <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig14large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior

This paper describes the use of a desktop virtual store to create virtual shopping environments to investigate in-store consumer behavior. A description of the protocol to build and run experiments, example results from an experiment concerning store layout, and important considerations when conducting virtual store experiments are presented.

People&#39;s responses to products and/or choice environments are crucial to understanding in-store consumer behaviors. Currently, there are various ...

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11.2K Views.  Wageningen University and Research. This paper describes the use of a desktop virtual store to create virtual shopping environments to investigate in-store consumer behavior. A description of the protocol to build and run experiments, example results from an experiment concerning store layout, and important considerations when conducting virtual store experiments are presented.

Video: Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior

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 ...

Vagus Nerve Stimulation as a Tool to Induce Plasticity in Pathways Relevant for Extinction Learning

Extinction describes the process of attenuating behavioral responses to neutral stimuli when they no longer provide the reinforcement that has been maintaining the behavior. There is close correspondence between fear and human anxiety, and therefore studies of extinction learning might provide insight into the biological nature of anxiety-related disorders such as post-traumatic stress disorder, and they might help to develop strategies to treat them. Preclinical research aims to aid extinction learning and to induce targeted plasticity in extinction circuits to consolidate the newly formed memory. Vagus nerve stimulation (VNS) is a powerful approach that provides tight temporal and circuit-specific release of neurotransmitters, resulting in modulation of neuronal networks engaged in an ongoing task. VNS enhances memory consolidation in both rats and humans, and pairing VNS with exposure to conditioned cues enhances the consolidation of extinction learning in rats. Here, we provide a detailed protocol for the preparation of custom-made parts and the surgical procedures required for VNS in rats. Using this protocol we show how VNS can facilitate the extinction of conditioned fear responses in an auditory fear conditioning task. In addition, we provide evidence that VNS modulates synaptic plasticity in the pathway between the infralimbic (IL) medial prefrontal cortex and the basolateral complex of the amygdala (BLA), which is involved in the expression and modulation of extinction memory.

Vagus nerve stimulation (VNS) has emerged as a tool to induce targeted synaptic plasticity in the forebrain to modify a range of behaviors. This protocol describes how to implement VNS to facilitate the consolidation of fear extinction memory.

Extinction describes the process of attenuating behavioral responses to neutral stimuli when they no longer provide the reinforcement that has been ...

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Studies investigating how people represent themselves and their own body often use variants of &#34;ownership illusions&#34;, such as the traditional rubber-hand illusion or the more recently discovered enfacement illusion. However, these examples require rather artificial experimental setups, in which the artificial effector needs to be stroked in synchrony with the participants&#39; real hand or face&#8212;a situation in which participants have no control over the stroking or the movements of their real or artificial effector. Here, we describe a technique to establish ownership illusions in a setup that is more realistic, more intuitive, and of presumably higher ecological validity. It allows creating the virtual-hand illusion by having participants control the movements of a virtual hand presented on a screen or in virtual space in front of them. If the virtual hand moves in synchrony with the participants&#39; own real hand, they tend to perceive the virtual hand as part of their own body. The technique also creates the virtual-face illusion by having participants control the movements of a virtual face in front of them, again with the effect that they tend to perceive the face as their own if it moves in synchrony with their real face. Studying the circumstances that illusions of this sort can be created, increased, or reduced provides important information about how people create and maintain representations of themselves.

Here, we describe virtual-hand and virtual-face illusion paradigms that can be used to study body-related self-perception/-representation. They have already been used in various studies to demonstrate that, under specific conditions, a virtual hand or face can be incorporated into one&#39;s body representation, suggesting that body representations are rather flexible.

Studies investigating how people represent themselves and their own body often use variants of &#34;ownership illusions&#34;, such as the traditional ...

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by observation or passive movement of trainee&#39;s hands by a robotic device). This obviously presents a major challenge in the rehabilitation of a paretic limb since voluntary control of physical movement is limited. Here, we describe a novel training scheme we have developed that has the potential to circumvent this major challenge. We exploited the voluntary control of one hand and provided real-time movement-based manipulated sensory feedback as if the other hand is moving. Visual manipulation through virtual reality (VR) was combined with a device that yokes left-hand fingers to passively follow right-hand voluntary finger movements. In healthy subjects, we demonstrate enhanced within-session performance gains of a limb in the absence of voluntary physical training. Results in healthy subjects suggest that training with the unique VR setup might also be beneficial for patients with upper limb hemiparesis by exploiting the voluntary control of their healthy hand to improve rehabilitation of their affected hand.

We describe a novel virtual-reality based setup which exploits voluntary control of one hand to improve motor-skill performance in the other (non-trained) hand. This is achieved by providing real-time movement-based sensory feedback as if the non-trained hand is moving. This new approach may be used to enhance rehabilitation of patients with unilateral hemiparesis.

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by ...

Using the FishSim Animation Toolchain to Investigate Fish Behavior: A Case Study on Mate-Choice Copying In Sailfin Mollies

Over the last decade, employing computer animations for animal behavior research has increased due to its ability to non-invasively manipulate the appearance and behavior of visual stimuli, compared to manipulating live animals. Here, we present the FishSim Animation Toolchain, a software framework developed to provide researchers with an easy-to-use method for implementing 3D computer animations in behavioral experiments with fish. The toolchain offers templates to create virtual 3D stimuli of five different fish species. Stimuli are customizable in both appearance and size, based on photographs taken of live fish. Multiple stimuli can be animated by recording swimming paths in a virtual environment using a video game controller. To increase standardization of the simulated behavior, the prerecorded swimming path may be replayed with different stimuli. Multiple animations can later be organized into playlists and presented on monitors during experiments with live fish.
In a case study with sailfin mollies (Poecilia latipinna), we provide a protocol on how to conduct a mate-choice copying experiment with FishSim. We utilized this method to create and animate virtual males and virtual model females, and then presented these to live focal females in a binary choice experiment. Our results demonstrate that computer animation may be used to simulate virtual fish in a mate-choice copying experiment to investigate the role of female gravid spots as an indication of quality for a model female in mate-choice copying.
Applying this method is not limited to mate-choice copying experiments but can be used in various experimental designs. Still, its usability depends on the visual capabilities of the study species and first needs validation. Overall, computer animations offer a high degree of control and standardization in experiments and bear the potential to &#39;reduce&#39; and &#39;replace&#39; live stimulus animals as well as to &#39;refine&#39; experimental procedures.

Using the FishSim Animation Toolchain to Investigate Fish Behavior: A Case Study on Mate-Choice Copying In Sailfin Mollies

Using the novel FishSim Animation Toolchain, we present a protocol for non-invasive visual manipulation of public information in the context of mate-choice copying in sailfin mollies. FishSim Animation Toolchain provides an easy-to-use framework for the design, animation and presentation of computer-animated fish stimuli for behavioral experiments with live test fish.

Over the last decade, employing computer animations for animal behavior research has increased due to its ability to non-invasively manipulate the ...

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

As many scholars and practitioners study personalization and relationship marketing, it is important to provide personalization such as mass customization through marketing technology. The purpose of this study is to examine how to conduct consumer research using an online survey and analysis of data. This study examines consumers&#39;&#160;perceived benefits while customizing a product as well as emotional product attachment, attitudes toward a customization program, and loyalty intentions in the context of online retailing. In addition, this study investigates how consumer responses are different based on individual characteristics such as fashion innovativeness. An online survey company in South Korea recruited 290 female apparel shoppers who purchased apparel online. To enhance external validity, this study used an existing retail website with a well-established mass customization program. After completing the customization program, participants complete the online questionnaire. Structural equation modeling (SEM) and latent mean analyses (LMAs) are then performed for analyses. This study stresses the importance of testing measurement invariance for mean comparisons. Before the SEM and LMA, this study follows the hierarchy of invariance tests (configural invariance test, metric invariance test, and scalar invariance test), which are not considered by traditional approaches such as ANOVA. These statistical analyses provide applicability of the invariance test procedures and LMA to consumer behaviors. The conclusions of mean differences have integrity and validity because they are guided by a sophisticated statistical procedure to ensure measurement invariance.

Presented here is a protocol to examine consumer responses toward mass customization in the context of online retailing. The protocol details the online survey procedure and how to analyze data using structural equation modeling and group differences using latent mean analyses.

As many scholars and practitioners study personalization and relationship marketing, it is important to provide personalization such as mass customization ...

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

The Cholesky decomposition method is the gold standard used in the field of behavioral genetics. The method is popular because it is easy to program and solve. Using this method, researchers can explore individual differences in longitudinal relations of different variables across multiple time points. The method allows investigators to decompose variance into (1) unique&#160;genetic, shared and non-shared environmental effects that arise at specific time points as well as (2) overlapping genetic, shared and non-shared environmental effects that carry over from one time point to another. However, the method does not identify the mechanisms or origins underlying these effects. The current report focuses on application of the Cholesky decomposition method in the field of educational psychology. Specifically, it discusses individual differences in longitudinal relations between kindergarten letter knowledge, kindergarten phonological awareness, first grade word-level reading skills, and seventh grade reading comprehension.

This paper demonstrates use of the gold standard method in behavioral genetics, the Cholesky decomposition method, to estimate unique, overlapping genetic and environmental influences on different variables to answer longitudinally motivated research questions.

The Cholesky decomposition method is the gold standard used in the field of behavioral genetics. The method is popular because it is easy to program and ...

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

To cross a road successfully, individuals must coordinate their movements with moving vehicles. This paper describes use of a walking simulator in which people walk on a treadmill to intercept gaps between two moving vehicles in an immersive virtual environment. Virtual reality allows for a safe and ecologically varied investigation of gap crossing behavior. Manipulating the initial starting distance can further the understanding of a participant&#8217;s speed regulation while approaching a gap. The speed profile may be assessed for various gap crossing variables, such as initial distance, vehicle size, and gap size. Each walking simulation results in a position/time series that can inform how velocity is adjusted differently depending on the gap characteristics. This methodology can be used by researchers investigating pedestrian behavior and behavioral dynamics while employing human participants in a safe and realistic setting.

This protocol describes use of a walking simulator that serves as a safe and ecologically valid method to study pedestrian behavior in the presence of moving traffic.

To cross a road successfully, individuals must coordinate their movements with moving vehicles. This paper describes use of a walking simulator in which ...

Simultaneous Monitoring of Wireless Electrophysiology and Memory Behavioral Test as a Tool to Study Hippocampal Neurogenesis

Brainwaves amplitude obtained from electroencephalography (EEG) has been well-recognized as a basis for cognitive capacity, memory, and learning on animals and humans. Adult neurogenesis mechanism is also linked to memory and learning improvement. Traditionally, researchers used to assess learning and memory parameters in rodent models by behavioral tasks. Therefore, the simultaneous monitoring of behavioral changes and EEG is particularly interesting in correlating data between brain activity and task-related behaviors. However, most of the equipment required to perform both studies are either complex, expensive, or uses a wired setup network that hinders the natural animals&#8217; movement. In this study, EEG was recorded with a wireless electrophysiology device along with the execution of a novel object recognition task (NORT). The animal&#8217;s behavior was monitored simultaneously by a video tracking system. Both recordings were analyzed offline by their timestamps which were synched to link EEG signals with the animal&#8217;s actions. Subjects consist of adult Wistar rats after medium-term environmental enrichment treatment. Six skull screw electrodes were fixed in pairs on both hemispheres over frontal, central, and parietal regions and were referenced to an electrode located posterior of the nasal bone. NORT protocol consists of exposing the animal to two identical objects for 10 min. After 2 h and 24 h, one of the objects was replaced with a novel one. Exploration time for each object was monitored by a behavioral tracking software (BTS) and EEG data recording. The analysis of the EEG synced with behavioral data consists of estimations of alpha and beta relative band power and comparisons between novel object recognition versus familiar object exploration, between three experimental stages. In this manuscript, we have discussed electrodes manufacturing process, epidural electrodes implantation surgery, environmental enrichment protocol, NORT protocol, BTS setup, EEG &#8211; BTS coupling for simultaneous monitoring in real-time, and EEG data analysis based on automatic events detection.

The protocol presented here provides information on the simultaneous electroencephalography (EEG) and behavioral assessment in real-time. We have discussed all steps involved in this protocol as an attractive solution for researchers in many fields of neuroscience, particularly in learning and memory areas.

Brainwaves amplitude obtained from electroencephalography (EEG) has been well-recognized as a basis for cognitive capacity, memory, and learning on ...

Using Enclosed Y-Mazes to Assess Chemosensory Behavior in Reptiles

Reptiles utilize a variety of environmental cues to inform and drive animal behavior such as chemical scent trails produced by food or conspecifics. Decrypting the scent-trailing behavior of vertebrates, particularly invasive species, enables the discovery of cues that induce exploratory behavior and can aid in the development of valuable basic and applied biological tools. However, pinpointing behaviors dominantly driven by chemical cues versus other competing environmental cues can be challenging. Y-mazes are common tools used in animal behavior research that allow quantification of vertebrate chemosensory behavior across a range of taxa. By reducing external stimuli, Y-mazes remove confounding factors and present focal animals with a binary choice. In our Y-maze studies, a scenting animal is restricted to one arm of the maze to leave a scent trail and is removed once scent-laying parameters have been met. Then, depending on the trial type, either the focal animal is allowed into the maze, or a competing scent trail is created. The result is a record of the focal animal&#39;s choice and behavior while discriminating between the chemical cues presented. Here, two Y-maze apparatuses tailored to different invasive reptile species: Argentine black and white tegu lizards (Salvator merianae) and Burmese pythons (Python bivittatus) are described, outlining the operation and cleaning of these Y-mazes. Further, the variety of data produced, experimental drawbacks and solutions, and suggested data analysis frameworks have been summarized.

Y-mazes enable researchers to determine the relevance of specific stimuli that drive animal behavior, especially isolated chemical cues from a variety of sources. Careful design and planning can yield robust data (e.g., discrimination, degree of exploration, numerous behaviors). This experimental apparatus can provide powerful insight into behavioral and ecological questions.

Reptiles utilize a variety of environmental cues to inform and drive animal behavior such as chemical scent trails produced by food or conspecifics. ...

Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior

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Chapters in this video

Combined Shuttle-Box Training with Electrophysiological Cortex Recording and Stimulation as a Tool to Study Perception and Learning

Vagus Nerve Stimulation as a Tool to Induce Plasticity in Pathways Relevant for Extinction Learning

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

Using the FishSim Animation Toolchain to Investigate Fish Behavior: A Case Study on Mate-Choice Copying In Sailfin Mollies

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Simultaneous Monitoring of Wireless Electrophysiology and Memory Behavioral Test as a Tool to Study Hippocampal Neurogenesis

Using Enclosed Y-Mazes to Assess Chemosensory Behavior in Reptiles