This research was conducted within the Service Innovation for Sustainable Business (SISB) grant, funded by the Swedish Knowledge Foundation (KK-stiftelsen).

The protocol outlined below is in line with the current ethics regulations of the authors&#8217; institutions. In order to ensure this, important aspects of the design are: voluntary participation, the use of ordinary shopping tasks as experimental stimuli or instructions, and no collection of personal data. However, as ethics regulations can differ between institutions, please consult the local institution&#8217;s human research ethics committee before conducting any research.
1. Experimental design and stimuli
<ol>
	<li>Define the research question.</li>
	<li>Select stimuli and task instructions based on the specific S studied in the 3S Model and the type of research question addressed.
	<ol>
		<li>Field studies: Consider relying on the shopping list procedure8,22,23, which ensures that customers will take approximately the same in-store path, since this increases the experimental control.</li>
	</ol>
	</li>
	<li>Consider incentivizing participation (e.g., through lottery tickets) to facilitate a more rapid recruitment of subjects.</li>
</ol>
2. Choice of eye-tracking equipment
<ol>
	<li>Use binocular, video-based, combined pupil/corneal reflection systems for the eye-tracking recordings.
	<ol>
		<li>Lab studies: Use a stationary system with a high sampling frequency (preferably 120 Hz or more).</li>
		<li>Field studies: Use a head-mounted mobile system with a sampling frequency of at least 30 Hz.</li>
	</ol>
	</li>
</ol>
3. Data collection procedure
<ol>
	<li>Recruit participants with normal or corrected-to-normal vision, and preferably those not wearing heavy makeup around the eyes.</li>
	<li>Use cover stories or broad statements of the study objective so that participants remain na&#239;ve about the explicit scientific purpose until after the experiment has taken place.</li>
	<li>Ensure that participants have sufficient time for the task completion. As this can vary a lot between study designs (in general, field studies are more time consuming than lab studies), please time the procedure so that a good estimate during recruitment can be provided. Here, the video recording will take approximately 15 minutes per participant: 5 minutes for the calibration procedure and 10 minutes for data collection.</li>
	<li>Give participants the study-specific task instruction(s).
	<ol>
		<li>Lab studies: Ask participants to follow the on-screen instructions and expose them to the experimental stimuli on a projector screen.</li>
		<li>Field studies: Ask participants to complete a predefined shopping task (e.g., the shopping list procedure), which naturally exposes them to a certain set of in-store stimuli.</li>
	</ol>
	</li>
	<li>Place the eye-tracking system around the participant&#8217;s head.</li>
	<li>Start the experimental session with a calibration procedure of the eye-tracking system. Follow manufacturer&#8217;s instructions for the calibration procedure.</li>
	<li>Distribute a predefined shopping list that is different between subjects. Here, olive oil and coffee are used as example products on the list.
	<ol>
		<li>Olive oil is used as the first example product on the list.&#160;Instruct consumers to choose either an expensive or a cheap version of olive oil. This task corresponds to the Shelf level of the model.</li>
		<li>Coffee is used as the second and last example product on the list. Have consumers move to the coffee section of the store. Manipulate where the coffee products are located on the shelf to investigate whether a specific coffee package is more likely than another coffee package to be chosen when located at the same place in the shelf. This corresponds to the Stock level of the model. 
		NOTE: Given that consumers will have to move and navigate throughout the store before reaching the first product and when walking between the first and the second product stated on the list, this corresponds to the Store level of our model.</li>
	</ol>
	</li>
	<li>Start the eye-tracking recordings according to the manufacturer&#8217;s instruction.</li>
	<li>Send participants to complete their task.</li>
	<li>Stop the eye-tracking recordings and debrief participants about the study purpose after completion of the experimental task(s).</li>
</ol>
4. Data processing
<ol>
	<li>Define areas of interest (AOIs) based on the study-specific research question.</li>
	<li>Select the eye-tracking metric(s) that will constitute the unit of analysis.
	<ol>
		<li>Lab studies: Create AOIs by outlining areas of the stimuli that will be the basis for automatic categorization of the eye-tracking data.</li>
		<li>Field studies: Manually categorize eye-tracking data into appropriate AOIs.</li>
	</ol>
	</li>
	<li>Export the data containing AOIs and the corresponding eye-tracking metric(s) into a suitable statistical program.</li>
	<li>Analyze the eye-tracking data with a statistical method that corresponds to the specific study purpose or hypothesis tested.</li>
</ol>

<ol>
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The authors have nothing to disclose.

In this article, we have used some of our prior research studies to illustrate a new conceptualization of the in-store search process. Specifically, our 3S Model &#8211; with its Stock, Shelf, and Store levels &#8211; offers a new way to examine customers&#8217; visual attention from a process perspective by means of eye-tracking methodology. Previous research has typically divided the in-store search process in broad terms such as navigation and decision-making1 or movement and contact2. The contribution of our 3S Model is that it captures the various layers of the retail environment and the linkages between these different S levels in a more nuanced way.
As with all research, the most critical aspect is the design of the experiments. Thus, taking the time to properly design one&#8217;s study is crucial to the success of the study. Furthermore, as the protocol described above includes the choice between lab and field settings, which is also a choice between a stationary and a head-mounted mobile eye-tracking system, this has to be considered during the design.
This protocol is limited with respect to the detailed instructions regarding the eye-tracking equipment. Since there are multiple producers of eye-tracking hardware and software, this protocol does not include any specific usage instructions as this is simply not feasible. Please consult the manual of&#160;the specific eye-tracking equipment.
From a theoretical point of view, the 3S Model allows researchers to more precisely position their studies and to narrow the aim of each experiment. By dividing the in-store search process into the three components of our model, researchers acknowledge and take into account more of the complexities of in-store decision-making. As shown by the provided sample studies, the choice of a specific product can be understood from the design of its packaging, its placement in a shelf, and the goal of the customer. Thus, it is important to understand which part of the in-store search process that is currently in focus.
From a practical perspective, the 3S Model clearly shows which parts of the in-store search process that are appropriate to investigate in the eye-tracking laboratory versus in the field. Studies under controlled lab-conditions facilitate digital manipulation of experimental stimuli on computer or projector screens and automatic coding of a large variety of eye-tracking measures with high levels of accuracy, but at the expense of low ecological validity. Such studies are better suited for examining research questions at the Stock and Shelf levels using stationary eye-tracking systems, due to the difficulty in manipulating shelf layout or packaging elements on consumer goods in real retail settings. Studies in actual field settings have high ecological validity, but lower degrees of experimental control and are typically more labor-intense as they require manual coding of eye-tracking measures (with lower levels of accuracy). Such studies are particularly well-suited for examining research questions at the Store level, but can also be used at the Shelf level, through reliance on mobile eye-tracking equipment.

Several models of the in-store search process have been offered throughout the years in the fields of retailing, marketing, and consumer behavior. Common conceptualizations of this search process are dichotomizations into navigation and decision-making1 or into movement and contact2, respectively, in which customers move and navigate inside the store to reach a desired area where they finally decide which particular items to purchase or interact with employees helping them to make informed choices. While we see the value of such conceptualizations, they do not really capture the various layers of the retail environment and their influence on customers&#8217; in-store search behavior.
Thus, the aim of this article is to present an alternative model of the in-store search process, hereinafter referred to as the 3S Model, which captures and discusses customers&#8217; visual attention at three distinct levels, from micro to macro: Stock, Shelf, and Store. According to our conceptualization, the Stock level represents a particular product for sale (i.e., a stock keeping unit; SKU), and includes customers&#8217; visual attention towards specific stimuli on product packaging, such as brand logos, textual elements, and pictorial elements. Next, at the intermediate Shelf level, the focus is not on elements located on specific products or SKUs, but rather on the area surrounding such units and how the configuration of that area can influence customers&#8217; visual attention. Apart from shelves and shelf layout, this level also includes, for example, in-store displays and another point-of-purchase material. Finally, the Store level represents the entire store environment, everything included, with SKUs and shelves both acting as building blocks. The emphasis on this last level is to shed light on customers&#8217; movements and navigation throughout the store, depending on their specific shopping tasks, cognitive capacity, and ability to acquire information.
In what follows, we give three eye-tracking examples, each from one of the above-mentioned S-levels, that jointly illustrate&#160;how the in-store search process can be understood and studied from the Stock, Shelf, and Store levels with respect to the suitable research topics, methodologies, and analyses.
Stock Level
Previous research has argued for two distinct views on how to organize textual and pictorial packaging elements in the best possible way: one based on recall3 and the other based on preference4,5,6. According to the recall view, the optimal packaging design should be to locate textual elements on the right side of a package and pictorial elements on the left side, since people tend to recall these element types better when located in such a way3. In contrast, the preference view postulates that it should be more advantageous to locate textual elements on the left side of a package and pictorial elements on the right side, since people prefer such an element organization and find it more aesthetically appealing5,6. While recall and preference are both important variables influencing consumer choice, these variables do not provide any insights regarding how a package should be designed for customers to quickly detect its different packaging elements. This is important since choices of packaged products depend on whether the products can capture customers&#8217; attention and convey an adequate message within a very limited time7,8,9,10. Our previous publication 11, therefore aimed to examine how the placement (left vs. right) of textual and pictorial packaging elements influences detection time toward these element types.
Shelf Level
Shelf space in the retail environment is an important strategic tool that increases the possibility for products to be seen and sold12. Valenzuela and Raghubir13 showed that premium products tend to be located on the top of the shelf and budget products on the bottom. Hence, experiences from the store&#8217;s positioning scheme helps to form consumer beliefs about vertical spatial position. Supporting this notion, the same authors later showed that vertical positioning is a diagnostic cue used by the customer in value judgment, with products on the top perceived to have higher value than products on the bottom14. Although they did not specifically test the influence of spatial beliefs on information processing, they argued that beliefs about vertical positioning reflect heuristic rather than systematic processing. This anticipated relationship indicates that value judgments made from vertical positioning are fast and frugal15. Heuristic processing of spatial information suggests that customers&#8217; visual attention will be guided towards vertical positions believed to contain a certain value. Therefore, if a customer is looking for a premium product, visual attention should be guided upwards, independent of whether premium products are placed on the top vertical position or not. Consequently, if vertical positioning is a diagnostic cue in value judgment, then visual attention towards vertical shelf levels should vary depending on activated beliefs and independent of actual content16. The aim was to explore how beliefs about spatial positioning (e.g., expensive is up and cheap is down) influences customers&#8217; visual search for premium and budget alternatives, respectively.
Store Level
A visit to a store usual entails making a series purchase decision. It is, therefore, important to investigate the purchase decisions as a part of a larger task in addition to investigating the process of a single decision. Previous research on customer decision-making has shown that customers make product choices in a matter of seconds7 from a very small subset of all available products17,18. It is noteworthy that even though customers arrive at the store with their experiences, preferences, and shopping goals, it has been estimated that 80% of the purchase decision are made in the store during the shopping trip19. It has been proposed that this process is an example of the efficacy of using heuristic decision strategies20. There are a few studies investigating the visual process of choosing a product from a single shelf21&#160;but they have not looked at a decision as a part of a greater whole and to what extent one decision influences subsequent decisions. Our paper, therefore, investigated to what extent the complexity of an initial purchase decision (specific vs. non-specific) influences visual attention during the next decision as this is the reality of most decisions made in a store 22.
The protocol described here is organized in the same chronological order as a typical research study. First, the definition of a research question and the study design is described, after which the choice of eye-tracking equipment is delineated. Next, the different steps of the data collection procedure are explained and, lastly, the data processing is outlined. Throughout the protocol, differences in procedure due to lab or field-based data collection are clearly stated.

<table border="0" cellpadding="0" cellspacing="0" style="width:733px;" width="733">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Eye tracker</td>
			<td style="width:117px;">Tobii Technology</td>
			<td style="width:157px;">Tobii X120 Eye Tracker</td>
			<td style="width:243px;">Stationary eye-tracking system</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Eye tracker</td>
			<td style="width:117px;">Tobii Technology</td>
			<td style="width:157px;">Tobii Glasses</td>
			<td>Head-mounted eye-tracking system</td>
		</tr>
	</tbody>
</table>

spotlighting customers' visual attention at the stock, shelf and store levels with the 3s model

Several models of the in-store search process exist in the fields of retailing, marketing, and consumer-based research. The present article presents a new conceptualization of this search process, which captures customers&#8217; visual attention at three distinct levels of analysis: Stock, Shelf, and Store. We refer to this conceptualization as the 3S Model and illustrate its usefulness through three eye-tracking studies, one from each level of analysis. Our experimental examples, which range from manipulating certain stimuli on a single product (e.g., the placement of textual and pictorial packaging elements) to manipulating the entire shopping trip for customers during their stay in a store (e.g., through more or less specific shopping tasks), highlight the broad applicability of this alternative approach for understanding customers&#8217; in-store search behavior. Thus, our model can be seen as a helpful tool for researchers interested in how to conduct experimental eye-tracking studies that shed light on the perceptual processes preceding product choices and purchase decisions. The 3S Model is equally suitable in controlled lab conditions and under ecologically valid settings in the real retail environment. Furthermore, it can be used from the micro level, with a focus on the meaningful metrics on a particular product, through the intermediate level, with the emphasis on the area surrounding products in shelves and other in-store spaces, all the way to the macro level, examining customers&#8217; navigational paths throughout a store as a function of their shopping tasks, cognitive capacity, or ability to acquire in-store information.

Stock Level Findings 
A total of 185 participants had complete eye-tracking recordings and were included in the study. We based our analysis on those participants who detected the packaging element within the time limit of 7.0 seconds. Thus, our dependent variable was time to first fixation (TTFF), which in this case represents the time it took from stimulus exposure until participants detected and thus fixated on the packaging element in question (measured in milliseconds but depicted in seconds). Fixations are the most commonly reported data points in eye-tracking research and are valid measures of visual attention24,25,26,27. TTFF did not differ between the two textual elements (F &#60; 1), and these stimuli did not interact with location to influence TTFF (F &#60; 1). Therefore, we combined them into a single text condition to facilitate parsimonious analyses, after which we conducted a 2 (Location: Left, Right) &#215; 2 (Stimuli: Textual, Pictorial) between-subjects Analysis of Variance (ANOVA) on TTFF. The ANOVA revealed no main effect of Location (F &#60; 1), no main effect of Stimuli (F(1, 114) = 1.09, p = .30), but did reveal a statistically significant two-way interaction (F(1, 114) = 4.46, p = .011). Inspection of cell means revealed that the pictorial packaging element was detected quicker when located on the right (M = 2.27) versus left (M = 3.82) side on the package, whereas the textual packaging elements were detected quicker when located on the left (M = 2.08) versus right (M = 3.01) side on the package; see Figure 1. Thus, the results on detection time for textual and pictorial packaging elements support the element organization advocated by the preference view5,6 rather than the recall view3 and suggest that preference may be function of easy information acquisition.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58846/58846fig1v2.jpg" /> 
Figure 1: TTFF in seconds as a function of packaging element (textual, pictorial) and location (left, right). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58846/58846fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Shelf Level Findings
A total of 128 participants had complete eye-tracking recordings and were included in the study. The dependent variable was TTFF on target, which here means the time it took from stimulus exposure until participants fixated on either a premium product or a budget product, depending on their randomly assigned experimental condition and the shelf configuration (again measured in milliseconds but depicted in seconds). A 2(Congruency: Congruent, Incongruent) &#215; 2 (Search Task: Premium, Budget) between-subjects ANOVA on TTFF on target showed a significant main effect of Congruency (F(1, 122) = 7.72, p = .006), where participants detected the target faster in the congruent condition (M = 0.94) than in the incongruent one (M&#160;= 1.45). Thus, regardless of search task, participants generally detected the target quicker when it was located on the vertical position that best serves as a cue of its value (e.g., premium products on the congruent top position instead of the incongruent bottom position). There was also a significant main effect of Search Task (F(1, 122) = 6.78, p = .010), where the budget search task led to faster target detection (M = 0.96) than the premium search task (M&#160;= 1.43). These two main effects were qualified by a significant two-way interaction (F(1, 122)&#160; = 78.57, p &#60; .001). Inspection of cell means revealed that, for the premium product, participants noted the target faster in the congruent (top) location (M = 0.37) than in the incongruent (bottom) location (M = 2.50). For the budget product, however, participants noted the target faster in the incongruent (top) location (M = 0.40) than in the congruent (bottom) location (M = 1.51); see Figure 2. Taken together, these results show that participants tend to move their gaze upwards independent of task; however, they turn their gaze downward faster in a budget task than in a premium task.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58846/58846fig2v2.jpg" /> 
Figure 2: TTFF on Target in seconds as a function of search task (premium, budget) and congruency (congruent, incongruent). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58846/58846fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Store Level Findings
The study included 66 participants with complete eye-tracking data. The dependent variable was the number of observations on the areas of interest (AOIs), with the AOIs defined for all relevant portions of the store (parts of the store that was not of interest for the analysis were not coded). The number of observations on an area is a frequently used measure in eye-tracking studies and serves as an indicator of interest28,29. A 2 (Task Specificity: Specific, Non-Specific) x 2 (Choice Task: First, Second) mixed ANOVA with the number of observations on AOIs as dependent variable, choice task as the repeated measure, and task specificity as the between-subjects factor. The results showed no significant main effect of the between-subjects factor (F(1, 64) = 1.71, p = .20). However, there was a significant main effect of choice task (F(1, 64) = 12.16, p &#60; .001) were the first choice was completed with fewer observations (M = 19.20) than the latter (M = 25.08). However, this main effect was qualified by a significant two-way interaction (F(1, 64) = 11.42, p = .001). Inspection of cell means revealed that participants in the specific choice group observed a fairly equal number of AOIs during their first (specific) choice task (M = 23.39) and their subsequent choice task (M = 23.58). In contrast, participants in the non-specific choice group observed a smaller number of AOIs during their first-choice task (M = 15.00) compared to the second choice task (M = 26.58); see Figure 3. These results show how the specificity of an initial shopping goal influences customers&#8217; visual search behavior during a choice task and how such a choice task affects the visual behavior during sub-sequent choices.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58846/58846fig3v2.jpg" /> 
Figure 3: Number of observations on AOIs as a function of task specificity (specific, non-specific) and choice task (first, second). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58846/58846fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model at JoVE.com

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

This article presents a new conceptualization of the in-store search process, the 3S Model, which captures customers&#8217; visual attention at three distinct levels of analysis: Stock, Shelf, and Store. We illustrate the usefulness of our conceptualization through three eye-tracking studies, one from each level of analysis in the 3S Model.

Several models of the in-store search process exist in the fields of retailing, marketing, and consumer-based research. The present article presents a new ...

spotlighting-customers-visual-attention-at-stock-shelf-store-levels

Nanyang Technological University

Research

JoVE Journal

Behavior

5.2K Views.  Aarhus University. This article presents a new conceptualization of the in-store search process, the 3S Model, which captures customers’ visual attention at three distinct levels of analysis: Stock, Shelf, and Store. We illustrate the usefulness of our conceptualization through three eye-tracking studies, one from each level of analysis in the 3S Model.

Video: Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

Assessing Forelimb Function after Unilateral Cervical SCI using Novel Tasks: Limb Step-alternation, Postural Instability and Pasta Handling

Cervical spinal cord injury (cSCI) can cause devastating neurological deficits, including impairment or loss of upper limb and hand function. A majority of the spinal cord injuries in humans occur at the cervical levels. Therefore, developing cervical injury models and developing relevant and sensitive behavioral tests is of great importance. Here we describe the use of a newly developed forelimb step-alternation test after cervical spinal cord injury in rats. In addition, we describe two behavioral tests that have not been used after spinal cord injury: a postural instability test (PIT), and a pasta-handling test. All three behavioral tests are highly sensitive to injury and are easy to use. Therefore, we feel that these behavioral tests can be instrumental in investigating therapeutic strategies after cSCI.

Three new behavioral tests (forelimb step-alternation, postural instability test, pasta handling test) for evaluating forelimb function after cervical spinal cord injury in rodents are described.

Cervical spinal cord injury (cSCI) can cause devastating neurological deficits, including impairment or loss of upper limb and hand function. A majority ...

The 5-Choice Serial Reaction Time Task: A Task of Attention and Impulse Control for Rodents

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. Test day challenges, modifications to the standard task, can be used to systematically tax the neural systems controlling either attention or impulse control. Importantly, these challenges have consistent effects on behavior across laboratories in intact animals and can reveal either enhancements or deficits in cognitive function that are not apparent when rats are only tested on the standard task. The variety of behavioral measures that are collected can be used to determine if other factors (i.e., sedation, motivation deficits, locomotor impairments) are contributing to changes in performance. The versatility of the 5CSRTT is further enhanced because it is amenable to combination with pharmacological, molecular, and genetic techniques.

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. Test day challenges, which are modifications of the standard task, increase flexibility of the task and can be combined with other manipulations to more fully characterize behavior.

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. ...

Methods to Test Visual Attention Online

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative data samples than can typically be collected on college campuses. However, before such methods can be widely adopted, a number of technological challenges must be overcome &#8211; in particular in experiments where tight control over stimulus properties is necessary. Here we present methods for collecting performance data on two tests of visual attention. Both tests require control over the visual angle of the stimuli (which in turn requires knowledge of the viewing distance, monitor size, screen resolution, etc.) and the timing of the stimuli (as the tests involve either briefly flashed stimuli or stimuli that move at specific rates). Data collected on these tests from over 1,700 online participants were consistent with data collected in laboratory-based versions of the exact same tests. These results suggest that with proper care, timing/stimulus size dependent tasks can be deployed in web-based settings.

To replicate laboratory settings, online data collection methods for visual tasks require tight control over stimulus presentation. We outline methods for the use of a web application to collect performance data on two tests of visual attention.

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative ...

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

This protocol describes how to conduct temporal-order experiments to measure visual processing speed and the attentional resource distribution. The proposed method is based on a new and synergistic combination of three components: the temporal-order judgments (TOJ) paradigm, Bundesen&#39;s Theory of Visual Attention (TVA), and a hierarchical Bayesian estimation framework. The method provides readily interpretable parameters, which are supported by the theoretical and neurophysiological underpinnings of TVA. Using TOJs, TVA-based estimates can be obtained for a broad range of stimuli, whereas traditional paradigms used with TVA are mainly limited to letters and digits. Finally, the meaningful parameters of the proposed model allow for the establishment of a hierarchical Bayesian model. Such a statistical model allows assessing results in one coherent analysis both on the subject and the group level.
To demonstrate the feasibility and versatility of this new approach, three experiments are reported with attention manipulations in synthetic pop-out displays, natural images, and a cued letter-report paradigm.

Temporal-order judgments can be used to estimate processing speed parameters and attentional weights and thereby to infer the mechanisms of attentional processing. This methodology can be applied to a wide range of visual stimuli and works with many attention manipulations.

This protocol describes how to conduct temporal-order experiments to measure visual processing speed and the attentional resource distribution. The ...

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.

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 ...

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention

Two dominant theories on lateralized processing of emotional information exist in the literature. One theory posits that unpleasant emotions are processed by right frontal regions, while pleasant emotions are processed by left frontal regions. The other theory posits that the right hemisphere is more specialized for the processing of emotional information overall, particularly in posterior regions.
Assessing the different roles of the cerebral hemispheres in processing emotional information can be difficult without the use of neuroimaging methodologies, which are not accessible or affordable to all scientists. Divided visual field presentation of stimuli can allow for the investigation of lateralized processing of information without the use of neuroimaging technology. 
This study compared central versus divided visual field presentations of emotional images to assess differences in motivated attention between the two hemispheres. The late positive potential (LPP) was recorded using electroencephalography (EEG) and event-related potentials (ERPs) methodologies to assess motivated attention. Future work will pair this paradigm with a more active behavioral task to explore the behavioral impacts on the attentional differences found.

This study compared central versus divided visual field presentations of emotional images to assess differences in motivated attention between the two hemispheres. The late positive potential (LPP) was recorded using electroencephalography (EEG) and event-related potentials (ERPs) methodologies to assess motivated attention.

Two dominant theories on lateralized processing of emotional information exist in the literature. One theory posits that unpleasant emotions are processed ...

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded via an eye tracker as the participant produces or comprehends a spoken language describing the concurrent visual world. This paradigm has high versatility, as it can be used in a wide range of populations, including those who cannot read and/or who cannot overtly give their behavioral responses, such as preliterate children, elderly adults, and patients. More importantly, the paradigm is extremely sensitive to fine grained manipulations of the speech signal, and it can be used to study the online processing of most topics in language comprehension at multiple levels, such as the fine grained acoustic phonetic features, the properties of words, and the linguistic structures. The protocol described in this article illustrates how a typical visual world eye tracking study is conducted, with an example showing how the online processing of some semantically complex statements can be explored with the visual world paradigm.

The visual world paradigm monitors participants' eye movements in the visual workspace as they are listening to or speaking a spoken language. This paradigm can be used to investigate the online processing of a wide range of psycholinguistic questions, including semantically complex statements, such as disjunctive statements.

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded ...

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Sentence comprehension relies on the ability to rapidly integrate different types of linguistic and non-linguistic information. However, there is currently a paucity of research exploring how preschool children with autism understand sentences using different types of cues. The mechanisms underlying sentence comprehension remains largely unclear. The present study presents a protocol to examine the sentence comprehension abilities of preschool children with autism. More specifically, a visual world paradigm of eye-tracking is used to explore the moment-to-moment sentence comprehension in the children. The paradigm has multiple advantages. First, it is sensitive to the time course of sentence comprehension and thus can provide rich information about how sentence comprehension unfolds over time. Second, it requires minimal task and communication demands, so it is ideal for testing children with autism. To further minimize the computational burden of children, the present study measures eye movements that arise as automatic responses to linguistic input rather than measuring eye movements that accompany conscious responses to spoken instructions.

We present a protocol to examine the use of morphological cues during real-time sentence comprehension by children with autism.

Sentence comprehension relies on the ability to rapidly integrate different types of linguistic and non-linguistic information. However, there is ...

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact with people around them. Head-mounted eye tracking offers a unique opportunity to capture children's dynamic egocentric views and how they allocate visual attention within those views. This protocol provides guiding principles and practical recommendations for researchers using head-mounted eye trackers in both laboratory and more naturalistic settings. Head-mounted eye tracking complements other experimental methods by enhancing opportunities for data collection in more ecologically valid contexts through increased portability and freedom of head and body movements compared to screen-based eye tracking. This protocol can also be integrated with other technologies, such as motion tracking and heart-rate monitoring, to provide a high-density multimodal dataset for examining natural behavior, learning, and development than previously possible. This paper illustrates the types of data generated from head-mounted eye tracking in a study designed to investigate visual attention in one natural context for toddlers: free-flowing toy play with a parent. Successful use of this protocol will allow researchers to collect data that can be used to answer questions not only about visual attention, but also about a broad range of other perceptual, cognitive, and social skills and their development.

Young children do not passively observe the world, but rather actively explore and engage with their environment. This protocol provides guiding principles and practical recommendations for using head-mounted eye trackers to record infants' and toddlers' dynamic visual environments and visual attention in the context of natural behavior.

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact ...

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

Verbal operant analyses are the extension of functional analysis technology to the field of verbal behavior, of particular relevance to autism spectrum disorders and associated developmental disabilities. Similarly, a verbal operant analysis carefully controls specific environmental factors that influence language, and measures strength of responding across four verbal operant classes: tact, mand, echoic, and sequelic. The frequency of each independent verbal operant is then measured against one another using the stimulus control ratio equation (SCoRE) to summarize the relative strength of the speaker&#8217;s repertoire. The verbal behavior SCoRE yields a statistic that can be compared against itself (e.g., for the purposes of pre/post testing) or against others (e.g., for the purposes of randomized controlled trials). The results of this evaluation provide an individualized hierarchy for diverging stimulus control across the verbal operants, from which a treatment plan for errorless language learning may be prescribed.

This article describes the procedures for conducting a verbal operant analysis (VOA), calculating the stimulus control ratio equation (SCoRE), and developing individualized verbal behavior treatment plans, specifically significant to address the deficits characteristic of autism.

Verbal operant analyses are the extension of functional analysis technology to the field of verbal behavior, of particular relevance to autism spectrum ...