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>

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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><tbody><tr><td>Eye tracker</td><td>Tobii Technology</td><td>Tobii X120 Eye Tracker</td><td>Stationary eye-tracking system</td></tr><tr><td>Eye tracker</td><td>Tobii Technology</td><td>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/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/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/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

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

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 ...

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

This method uses a rapid serial visual presentation (RSVP) paradigm to measure the cost of distraction when participants maintain multiple search goals. The protocol identifies two types of distraction within a single task - contingent attentional capture and set-specific capture - that represent different types of limitations of cognitive processing. Participants search for letters in two or more &#34;target&#34; ink colors (e.g., green and orange) within a continuous RSVP stream of heterogeneously colored letters, while ignoring two peripheral RSVPs of letters. Upon detecting a target, participants are to identify the letter. On some trials, target-colored distractors appear in the periphery just prior to the presentation of a target, causing a drop in target identification performance. Contingent attentional capture is observed by examining performance on trials in which the peripheral distractor is the same color as the target on that trial (e.g., both orange). Set-specific capture is represented by performance on trials in which the peripheral distractor is target-colored (e.g., orange), but not the same color as the target on that trial (e.g., green.) By varying the amount of time (i.e., the number of stimuli appearing) between the presentation of the distractor and the target, researchers can observe how participants recover from these distraction costs over time. As compared to static displays that are often used to measure contingent attentional capture, the dynamic display produces much larger effects, allowing the researcher to identify subtle effects of smaller manipulations. An unusual aspect of our design is that it employs a continuous display; &#34;filler&#34; stimuli connect one trial to the next seamlessly, and participants respond during this interval whenever they detect a target. The continuous display reduces chance performance to near-zero levels (rather than 50%) and provides researchers with a more sensitive measure of performance differences across trial types.

This method uses a dynamic visual display to index costs of distraction during visual search, including both &#34;contingent attentional capture&#34; and &#34;set-specific capture,&#34; which is a cost of distraction that occurs when the participants maintain multiple search goals simultaneously. This method has revealed basic mechanisms and limitations of visual attention.

This method uses a rapid serial visual presentation (RSVP) paradigm to measure the cost of distraction when participants maintain multiple search goals. ...

Loneliness Assuaged: Eye-Tracking an Audience Watching Barrage Videos

Researchers usually theorize media exposure based on assumptions of legacy media. However, a new interactive video viewing format, in this case barrage video where viewers&#8217; comments are overlaid over visual content, challenges past perspectives. This study proposes an activation and match satisfaction model to study viewing behaviors of lonely people and to challenge previous claims. It presents a protocol to examine the mechanism of how loners use barrage videos by combining eye tracking and self-report measures. Eye tracking documents the audience&#39;s conscious and subconscious watching behaviors in real time and allows for inference of the amount of allocated cognitive resources in response to rational and emotional content. The self-report gauges the amount of satisfaction obtained. Overall, results from the measures supported an activation and match satisfaction model regarding loners and their barrage video viewing behaviors. Implications are discussed.

The study proposes an activation-match model to study how loneliness is mitigated when a lonely audience watches barrage videos of rational and emotional appeals. The protocol uses eye tracking to document duration and fixation, accounting for the degree of satisfaction when emotional needs are appeased by content and barrage.

Researchers usually theorize media exposure based on assumptions of legacy media. However, a new interactive video viewing format, in this case barrage ...

Real-Time Recording and Mapping of Human Interaction with 3D Virtual Objects

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data

We present a method for real-time recording of human interaction with three-dimensional (3D) virtual objects. The approach consists of associating rotation data of the manipulated object with behavioral measures, such as eye tracking, to make better inferences about the underlying cognitive processes.
The task consists of displaying two identical models of the same 3D object (a molecule), presented on a computer screen: a rotating, interactive object (iObj) and a static, target object (tObj). Participants must rotate iObj using the mouse until they consider its orientation to be identical to that of tObj. The computer tracks all interaction data in real time. The participant&#39;s gaze data are also recorded using an eye tracker. The measurement frequency is 10 Hz on the computer and 60 Hz on the eye tracker.
The orientation data of iObj with respect to tObj are recorded in rotation quaternions. The gaze data are synchronized to the orientation of iObj and referenced using this same system. This method enables us to obtain the following visualizations of the human interaction process with iObj and tObj: (1) angular disparity synchronized with other time-dependent data; (2) 3D rotation trajectory inside what we decided to call a &#34;ball of rotations&#34;; (3) 3D fixation heatmap. All steps of the protocol have used free software, such as GNU Octave and Jmol, and all scripts are available as supplementary material.
With this approach, we can conduct detailed quantitative studies of the task-solving process involving mental or physical rotations, rather than only the outcome reached. It is possible to measure precisely how important each part of the 3D models is for the participant in solving tasks, and thus relate the models to relevant variables such as the characteristics of the objects, cognitive abilities of the individuals, and the characteristics of human-machine interface.

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

We have developed a simple, customizable, and efficient method for recording quantitative processual data from interactive spatial tasks and mapping these rotation data with eye-tracking data.

We present a method for real-time recording of human interaction with three-dimensional (3D) virtual objects. The approach consists of associating ...

Neuroscience

Finding Your Blind Spot and Perceptual Filling-in

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
In the back of everyone's eye is a small piece of neural tissue called the retina. The retina has photosensitive cells that respond to stimulation by light. The responses of these cells are sent into the brain through the optic nerve, a bundle of neural fibers. In each retina there is a place somewhere in the periphery where the outputs from retinal cells collect and the bundled optic nerve exits to the brain. At that location, there is no photosensitivity-whatever light reflects from the world and lands in that position does not produce a signal in the brain. As a result, humans have a blind spot, a place in the visual field for which they don't process incoming stimuli. 
However, people are not aware that they have blind spots; there is not an empty hole in the visual images in front of the eyes. So what do people see in their blind spots? The brain actually fills-in missing input based on the surroundings. 
This video demonstrates how to find a person's blind spot, and how to investigate the mechanisms of perceptual filling-in.

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
In the back of everyone's eye is a small piece of neural tissue called the retina. ...

Education

JoVE Science Education

Psychology

Sensation and Perception

Visual Search for Features and Conjunctions

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

How do people find objects in cluttered visual scenes? Think, for example, of looking for keys on a messy desk, finding the ripest-looking fruit at the grocery store, locating your car when you can&#8217;t quite remember where you parked it, or finding an old friend at an airport exit gate. Clearly, an understanding of visual perception is going to play a role in any answers, and more specifically, an understanding of visual attention will be crucial.
Visual attention refers to the ability to focus in on just part of an image, mustering one&#8217;s processing resources selectively to determine whether the thing being looked for&#8212;the target, in the standard experimental jargon&#8212;is present. To study search and attention, experimental psychologists have developed a widely used paradigm known (unsurprisingly) as visual search.
Psychologists have also motivated a great deal of research by the intuition that any good theory of search is going to have, to explain why some things are easy to find and others are hard to find. So in the context of the visual search paradigm, perceptual psychologists have often focused on contrasting easy searches with more difficult ones. The most influential contrast is between what researchers call a feature search and a conjunction search.

Investigating Visual Attention by Feature and Conjunction Search

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

How do people find objects in cluttered visual scenes? Think, for example, of ...

Cognitive Psychology

Inattentional Blindness

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
We generally think that we see things pretty well if they are close by and right in front of us. But do we? We know that visual attention is a property of the human brain that controls what parts of the visual world we process, and how effectively. Limited attention means that we can't process everything at once, it turns out, even things that might be right in front of us. 
In the 1960s, the renowned cognitive psychologist Ulrich Neisser began to demonstrate experimentally that people can be blind to objects that are right in front of them, literally, if attention is otherwise distracted. In the 1980s and 1990s, Arien Mack and Irvin Rock followed up on Neisser's work, developing a simple paradigm for examining how, when, and why distracted attention can make people fail to see the whole object. Their experiments, and Neisser's, did not involve people with brain damage, disease, or anything of the sort, just regular people who failed to see objects that were right in front of them. This phenomenon has been called inattentional blindness. This video will demonstrate basic procedures for investigating inattentional blindness using the methods of Mack and Rock.1

Investigating Visual Awareness and Inattentional Blindness

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
We generally think that we see things pretty well if they are close by and right in ...

Spatial Cueing

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
Attention refers to the limited human ability to select some information for processing at the expense of other stimuli in the environment. Attention operates in all sensory modalities: vision, hearing, touch, even taste and smell. It is most often studied in the visual domain though. A common way to study visual attention is with a spatial cueing paradigm. This paradigm allows researchers to measure the consequences of focusing visual attention in some locations and not others. This paradigm was developed by psychologist Michael Posner in the late 70s and early 80s in a series of papers in which he likened attention to a spotlight, selectively illuminating some portion of a scene.1,2 This video demonstrates standard procedures for a spatial cueing experiment to investigate visual attention.

Spatial Cueing Task to Investigate Covert Visual Attention

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
Attention refers to the limited human ability to select some information for ...

Crowding

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
Human vision depends on light-sensitive neurons that are arranged in the back of the eye on a tissue called the retina. The neurons, called the rods and cones because of their shapes, are not uniformly distributed on the retina. Instead, there is a region in the center of the retina called the macula where cones are densely packed, and especially so in a central sub-region of the macula called the fovea. Outside the fovea there are virtually no cones, and rod density decreases considerably with greater distance from the fovea. Figure 1 schematizes this arrangement. This kind of arrangement is also replicated in the visual cortex: Many more cells represent stimulation at the fovea compared to the periphery.
<img alt="Figure 1" src="/files/ftp_upload/10280/10280fig1.jpg" /> 
Figure 1. Schematic depiction of the human eye and the distribution of light-sensitive receptor cells on the retina. The pupil is the opening in the front of the eye that allows light to enter. Light is then focused onto the retina, a neural tissue in the back of the eye that is made of rods and cones, light-sensitive cells. At the center of the retina is the macula, and in the center of the macula is the fovea. The graph schematizes the density of rod and cone receptors on the retina as a function of their position. Cones, which are responsible for color vision, are found almost exclusively in the fovea. Rods, which support seeing in low-light conditions, are similarly clustered more heavily near the fovea, with quickly falling densities outside the macula.
The result: We see very well in the part of space that our eyes are pointing at directly, the part of space stimulating the fovea; but we actually don&#39;t see very well in the periphery. We don&#39;t really notice it though, because our eyes move around constantly, building a representation of space from many individual fixations.
One way to study the properties of peripheral vision is with a phenomenon known as crowding.1 Crowding refers to an inability to recognize objects in clutter, and we experience crowding especially strongly when objects are shown in the periphery. Figure 2a is an example in which you should be able to experience crowding: Look at the cross in the center, and see if you can report the letter that is in the middle of the pack on the right. It is probably pretty hard. Now in Figure 2b try to report the letter on the left. Much easier! In this figure the letter is not crowded and there is no clutter around it, so it is easier to recognize.
<img alt="Figure 2a" src="/files/ftp_upload/10280/10280fig2a.jpg" />
Figure 2a. Crowded stimulus. Fixate the cross in the center, and see if you can recognize the letter in the middle of the pack on the left. It should be difficult, because the letters are in the periphery, and the central letter is crowded by the letters around it.
<img alt="Figure 2b" src="/files/ftp_upload/10280/10280fig2b.jpg" />
Figure 2b. Uncrowded stimulus. This stimulus is identical to Figure 2a, except that the letter G is uncrowded-no other letters surround it. Even while fixating the cross, the letter should be easy to recognize, even though it is just as much in the periphery as the G in Figure 2a.
This video will demonstrate how to design and implement a crowding experiment with letters as stimuli.

Investigating Crowding in Peripheral Vision

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
Human vision depends on light-sensitive neurons that are arranged in the back of ...