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Virtual Reality-Based Retrospective Think Aloud (VR-RTA): A Novel Method for Studying Offender Decision-Making

This article describes a novel multimodal method to examine decision- making: Virtual Reality-Based Retrospective Think Aloud (VR-RTA). This method taps directly into the offenders’ perspective and aims to enhance memory recall. We apply this method among a sample of ...

Published onJan 22, 2024
Virtual Reality-Based Retrospective Think Aloud (VR-RTA): A Novel Method for Studying Offender Decision-Making


This article describes a novel multimodal method to examine decision- making: Virtual Reality-Based Retrospective Think Aloud (VR-RTA). This method taps directly into the offenders’ perspective and aims to enhance memory recall. We apply this method among a sample of incarcerated burglars (N=200) who scouted neighborhoods in immersive virtual reality to explore possibilities for committing a burglary. Subsequently, they viewed back a screen recording of their scouting process and simultaneously ‘thought aloud’ about their assessment of the environment and the decision-making strategies they employed. Emerging themes were then further examined in an interview. Rich and detailed insights into participants' interpretation of the environment and their decision-making strategies were obtained. VR-RTA assisted in verbalizing automated cognitive processes and increased engagement by building rapport. That is, the method allows for capturing in the moment considerations underlying decision making during the crime commission process. We conclude that the VR-RTA method is a useful complement to other methods of data collection, such as interviews and survey approaches, and also enhance virtual reality-based research designs.


Those with prolific experience with offending are uniquely placed to provide insight into the development of that behavior (Topalli et al., 2020). In criminology, interviews have long been regarded as the go to method to achieve this (e.g., Maruna, 2001; Brookman, et al., 2011; Miller et al, 2013; Sampson and Laub, 2003; Wright and Decker, 1994, 1995). However, interviews with offenders are also subject to the inevitable degradation of memory recall. As time between the commission of a crime and the subsequent interview elapses, memory becomes blurred, details are lost, and timing warped, diminishing the accuracy and completeness of the information obtained (Nee, 2010; Van Gelder, 2023). To overcome these challenges, researchers would ideally be present at the moment of offending or right after the event, but ethical, practical and safety considerations generally render this option unfeasible (see: Van Gelder, 2023).

In this article, we describe a novel method, Virtual Reality-based Retrospective Think Aloud (VR-RTA), that integrates VR technology with so-called think-aloud protocols. This method remedies some the limitations plaguing interviews and other retrospective methods (e.g., surveys), and allows researchers to gain insight into offender decision-making. By way of illustration, we apply the VR-RTA method among a sample of incarcerated burglars who are invited to scout different neighborhoods for opportunities for burglary in VR. This process is screen-recorded. During the replay of the screen recording following the task, they are asked to ‘think aloud’ about their decision-making during the scouting process.

Below, we first elaborate on the cognitive underpinnings of think-aloud protocols and elaborate on the use of VR technology in crime research. This is followed by a presentation of the findings of our study. We conclude with a discussion of the potential of the VR-RTA method for crime research more broadly.

In research applying think-aloud protocols (TAPs), people are instructed to think out loud whilst completing a task, and to verbalize everything that goes through their minds while doing so (Ericsson and Simon 1993; Güss 2018)(Ericsson and Simon 1993; Güss 2018). When individuals are asked to report on their decision-making about an action out of context, they tend to rely on implicit theories about how the world works in general, which may not accurately reflect how their actual cognitions drive their behavior (Branch, 2000; Eccles and Arsal, 2017). By verbalizing one’s thoughts during task performance, this limitation is addressed and real-time insight into the reasoning process is gained (Leighton, 2017). Furthermore, people may provide inaccurate reports about cognitions when asked about them retrospectively because they have limited conscious access to these cognitions (see Nisbett and Wilson 1977, Wolcott et al., 2021). By analyzing thought processes that occur during or right after an activity, TAPs can reveal information about the decisions people make, how they interact with their environment, and what factors influence how they reach their goal (Wolcott et al., 2021). Finally, as highlighted by Nisbett and Wilson (1977), people often find it challenging to explain how and why they perceive or remember things, but they are generally well able to describe their own actions, as occurs during a TAP. In short, compared to traditional (task-based) interviews which pose challenges to perception and memory, TAPs allow for a more effortless and detailed (re)construction of actions, and provide a clear picture of the reasoning process in real time (Reinhart et al., 2022).

TAPs exist in two different forms; concurrent and retrospective. In concurrent think-aloud approaches, people think aloud whilst performing a task, and this is followed by a post-task interview (Meenaghan et al., 2018). The retrospective think-aloud (RTA), which is the focus of this study, involves people first completing a task and subsequently reflecting on it while a recording of their performance is played back to them (Ericsson and Simon, 1993; Fox et al., 2011; Hertzum et al., 2009). The advantage of retrospective over concurrent TAPs is that the former carry less cognitive load and do not interfere with task performance and therefore allow for the preservation of natural behavior (van den Haak et al, 2004).

Although a common method for studying a range of different behaviors (e.g., Gregg et al., 2017; Malek et al., 2017; Kesler et al., 2016), the use of TAPs, both concurrent and retrospective, in crime research has thus far been limited. Some studies have used revisiting recent crime scenes to enhance interview engagement and increase ecological validity (Cromwell et al., 1992; Wright and Decker, 1994). However, without the added bonus of the re-enactment of the behavior under study, these methods still remain subject to the well-documented limitations of human memory (Nee et al., 2019). The advent of VR technology has provided novel possibilities to overcome these limitations.

Moving closer to the action: The use of VR in crime research

Immersive VR allows for the creation of realistic simulations of criminogenic environments which offenders can navigate and demonstrate decision-making under controlled circumstances resembling those of a behavioral experiment (Van Gelder, 2023; Van Gelder et al., 2014). Several VR studies on criminal decision-making have emerged in recent years, mostly focusing on burglars (e.g., Meenaghan et al. 2018; Nee et al., 2019; Van Sintemaartensdijk, 2020). In one study, (Meenaghan et al. 2018) pioneered the use of concurrent TAPs to study incarcerated burglars’ experiences while undertaking a simulated burglary on a laptop computer. This non-immersive study underscored the added value of TAPs and demonstrated how this method can help the elicitation of detailed information on burglar decision making.

Virtual Reality-based Retrospective Think Aloud (VR-RTA), which is developed and tested in the present study, is a technique that builds on this work using an immersive environment. Furthermore, it’s retrospective nature reduces cognitive load and minimizes reactivity compared to concurrent TAPs. That is, the verbal explanations offenders provide when viewing back the virtual re-enactment of their actions, allow for a discussion of motivations and other factors influencing burglar behavior in their real-life experiences, without disrupting the actual process.

Evidence from neuroscience further strengthens the case for using VR-RTA. By having people perform a certain behavior and to subsequently observe that behavior from a first-person perspective, it stimulates those parts of the brain that trigger action memory. That is, the way human memories are constructed is shaped by the perceptual and motor brain systems (Barsalou 2008). Re-enacting therefore facilitates the mental recall of past incidents (Meade et al., 2019). In our study, by scouting virtual residential neighborhoods and engaging in the active navigation of these neighborhoods, participants re-enact prior behavior, thereby facilitating the retrieval of memories related to their past burglaries in the real world. Additionally, previous research has demonstrated superior recollection when memories are retrieved from a first-person perspective (Bergouignan et al., 2014; Repetto et al., 2016; Glenberg and Hayes, 2016).

The Current Study

In this study, we invited a sample of incarcerated burglars to navigate two different virtual neighborhoods, with the task of scouting them for opportunities for a burglary. One of the virtual neighborhoods was an exact copy of an existing neighborhood in a city in the Netherlands, the other was designed to resemble a typical middle-class Dutch neighborhood. For each participant the scouting process in one of the two neighborhoods was screen recorded and subsequently played back to them. During the replay, they were instructed to think aloud about the scouting process and to elaborate on the decisions they made throughout this process. The objective was to stimulate disclosure of information and to gain more insight into their thinking while ‘on the job’. Themes that emerged were further examined in a subsequent semi-structured interview.

Additionally, the VR system was equipped with integrated eye tracking system which recorded what features of the environment the burglars were focusing on and for how long. This allowed for establishing the extent to which specific features, such as deterrent or attractive cues, were noticed and paid attention to. Burglary target selection cues have been extensively studied in the past (e.g., Bennett and Wright, 1984; Nee, 2015; Tunnell, 1992). We examine to what extent burglars in our study mentioned such cues in the VR-RTA and interview. Lastly, we tracked their spatial data to monitor how they moved through the neighborhood. We will explore the merits of the VR-RTA method by addressing two research questions: 1) To what extent do our results align with established findings from prior research, and 2) to what extent does the VR-RTA complement and extend the information extracted from the interview and VR-data (eye tracking and spatial data).



Participants were 200 incarcerated male burglars (age range 19-61, Mage = 33) with different degrees of burglary experience serving prison sentences for various offences (not only burglary) in the Netherlands. Participants could be included if they were 18 years or older, had committed at least 5 burglaries in their lifetime, did not have an epileptic history, or were currently using heavy medication (such as anti-psychotics). Involvement in burglary was checked through previous conviction data, prison staff, and self-reported offending history. Participants were recruited in four different male-only prisons. Recruitment happened through leafletting prison wings, and through referral by other inmates and prison staff. In exchange for their cooperation, participants were paid €5 in line with prison policy. Ethical approval was obtained from the Ethics Committee of the University of Leiden (ECPW-2022/372). All participants signed an informed consent form.


Data collection occurred in a dedicated room in the participating prison with only the researcher and the participant present. Participants received detailed information on confidentiality and were given the opportunity to ask questions prior to participating. After giving consent, each participant scouted two different virtual neighborhoods. For both neighborhoods they were instructed to scout it as if they intended to commit a burglary there. After scouting the second neighborhood, participants filled out a survey with questions regarding feelings of presence, deterrence, cybersickness, and other variables relevant to the larger Virtual Burglary Project (results reported elsewhere).1 Following this, the think aloud-task was conducted. The session ended with a semi-structured interview and a brief survey with questions about burglary expertise. Subject to participant consent (90%), verbalizations during the think-aloud process and interview were audio-recorded (and deleted after transcription). Following the interview and survey, participants were debriefed and support was offered for any issues raised. The entire session took 45-60 minutes.

VR-RTA instructions

The virtual scouting process in one of the two neighborhoods was screen-recorded based on random selection (1:1).2 The recording was played back to participants after finishing scouting both neighborhoods. Before starting the playback, participants received the following instructions from the experimenter:

“I would like to learn a bit more about how you evaluate a neighborhood. I am particularly interested in what things you find important when you make decisions related to burglary. We will use a method that is called the ‘think-aloud’ method. I will now show you the recording of your VR experience in the first [second] neighborhood. The idea is that you simply tell me whatever comes to mind when you watch the recording. Just say everything that comes to mind, irrespective of whether you think it is relevant or not. I may also ask you some questions during the replay of the recording.”

The experimenter aimed to minimize interference during the process. Only in cases where participants did not verbalize any thoughts, prompts were used to stimulate disclosure. Examples of prompts included questions about the participant's chosen direction, actions taken at specific locations, prolonged periods of non-movement, and reasons for changing direction.


Virtual Environment

The virtual neighborhoods A and B (see Figures 1 and 2) were developed using the Unity Pro engine (version 2017.3.1f1) and viewed through the VIVE Pro Eye head-mounted display, which is equipped with built-in eye tracking technology. Participants wore headphones delivering immersive audio and navigated the virtual environment using a game controller.

Figure 1. Top view, aerial view, and first person view of Neighborhood A in the virtual environment.

Figure 2. Top view, aerial view, and first person view of Neighborhood B in the virtual environment.

Eye tracking

In both neighborhoods, a set of items (henceforth ‘Easter-Eggs’), that may act as burglary attractors (e.g., open window, ladder, packaging of expensive items) or deterrents (e.g., beware of dog sign, alarm box) were distributed across the neighborhoods (see Figure 3). We used eye tracking to assess whether these Easter-Eggs had been noticed by participants. Eye tracking algorithms use specific criteria to determine where a person is looking to help understand what draws a person's attention and what information they are actively processing in their mind.

Figure 3. Easter-Eggs in neighborhood A. Beware of the Dog’-sign, ladder below front window and the placement of the Easter-Eggs.

Spatial patterns

X and y coordinates of the participants in the neighborhoods were tracked per second. These data yielded time spent (in seconds) in the virtual neighborhood, walking patterns, as well as the distance travelled (in meters). Spatial data allows for generating heat maps that reflect how participants move through a neighborhood and provide insight into scouting strategies.

Semi-structured interview

Three items in the semi-structured interview that followed the VR-RTA asked about participants’ approaches to committing burglaries in real-life in the period(s) when they were active as burglars prior to incarceration. The interview questions were: “How do you decide on where you burgle?”, “What things about a house or in a neighborhood attract you?”, “What things about a house or in a neighborhood deter you?”.

Coding strategy of the RTA-data

RTA-data were analyzed following a thematic approach (Braun and Clarke, 2006), using MAXQDA 2022 software (VERBI Software, 2021). Coding was performed by two independent coders. Intercoder agreement was 95%, which can be considered substantial (Hallgren, 2012). Disagreements between coders were resolved through consensus.

During the analysis, or coding process, we classify burglary cues into codes. Cues reflect aspects that burglars talked about during their RTA task. The analysis consisted of six phases (see Figure 4). Phase 1 involved transcription and repeated reading of the RTA-data. Phase 2 involved the generation of initial codes (features of the data that seemed of interest). Phase 3 entailed reviewing these codes and grouping them into ‘themes’. Later phases consisted of reviewing (phase 4), and defining the codes (phase 5), into output, that is defining our burglary cues (phase 6).

The first goal was to examine the extent to which burglary cues that have emerged in prior research on burglary (Nee, 2015; Peeters et al., 2013; see Table 1 for an overview) also emerged in our data. Second, the RTA-data has been coded to find emerging themes of burglars talking about the eye tracking data (Easter-Eggs), such as talking about the ‘ladder outside window’ or the ‘alarm box on the wall’ (see Figure 3). Third, the RTA-data has been coded to illustrate the added value of the retrospective thinking exercise to complement interviews, such as talking about their decision to burgle and what they found attractive about houses.

Figure 4. Thematic analysis process (adapted from Michel-Villarreal et al., 2021).


The results are organized into two parts. In the first part, we examine to what extent our results regarding burglary cues align with previous research. In the second part, we explore to what extent the VR-RTA method complement and extend the information from the interview and VR data (eye tracking and spatial data).

Research question 1: Burglary cues

Prior research has identified four different categories of cues related to burglar target selection: Layout, Security, Occupancy and Affluence (for reviews, see Nee, 2015; Peeters et al., 2013). Layout cues regard features such as degree of cover, presence of escape routes and surveillability. Security cues include features related to security measures and target hardening, such as the presence of alarms and cameras, and quality of door and window locks. Occupancy cues are features signaling the presence of people in the immediate environment, such as the presence of a car, or lights on inside a house. Affluence cues, finally, include the availability of expensive items, the size of a house, and decor. We examined whether these cue categories emerged during the retrospective thinking task, and coded how frequently participants mentioned them. Table 1 provides an overview of the four different cue categories and subcategories, along with examples of specific cues.


In previous studies (e.g., Langton and Steenbeek, 2017; Nee and Meenaghan, 2006), layout cues were found to significantly influence burglars' decision-making. Of all four categories, layout cues were mentioned most often by the burglars in our sample, particularly cues relating to the subcategory surveillability (Table 1). Burglars particularly mentioned sightlines and the ease of being spotted (e.g., by neighbors or passers-by). In addition, and related to surveillability, degree of cover and escape routes were also frequently mentioned. Both cover and escape routes were often mentioned first upon entering the neighborhood. Quotes from our sample are provided in Table 2.


Predominant cues mentioned by the burglars in our sample related to the quality of doors and window locks (e.g., estimated time required to break open locks, target hardening). These findings correspond with results reported in the research literature on security cues (e.g., Newton et al., 2008). Furthermore, burglars paid particular attention to social control, i.e., the presence of people in the neighborhood who may act as guardians. Burglars talked about social control systems that might be active, such as neighborhood watch, vigilant parents, and communication between neighbors. Other security measures, such as cameras, alarms, or the possible presence of a dog were also mentioned, although less often.


The (possible) presence of residents was also mentioned frequently, aligning with ample prior evidence indicating that burglars prefer unoccupied houses over occupied ones (Coupe and Blake, 2006; Hearndon and Magill, 2004; Wright and Decker, 1994). Frequently mentioned cues to establish occupancy were lights being on in a house or cars being parked in front of it. Since participants were told prior to entering the VR that it was 5:30 PM in the virtual neighborhood, they indicated that this was a time that many residents would be home or about to come home, and therefore that this time was not optimal for committing burglary. Additionally, burglars mentioned using tricks or technology to monitor residents' routines, for example by sticking small items (e.g., toothpicks) in the door, or using hidden cameras to register residents’ routines, or ringing the doorbell with a pretext.


Of the four categories, affluence cues were mentioned least often. In this category, burglars primarily focused on valuable items that were visible from outside a house to determine wealth. Upkeep was also frequently mentioned within this category. House size and the value of parked cars were also mentioned, but less frequently. These findings align with previous research by Peeters et al. (2013), who reported mixed results on how affluence influences risk-taking and target selection.

Research question 2: VR-RTA as a method to complement and extend information from the interview and VR data

We have thus far focused on validating the VR-RTA method by relating our findings to findings from the broader documented burglary cue literature. We now turn to demonstrating how VR-RTA can complement eye tracking data and spatial patterns. Furthermore, we compare VR-RTA data with responses to interview questions to explore how the former can be used as a tool for eliciting information.

Combining eye tracking with RTA

Eye tracking provides information about what aspects of a situation or environment people pay attention to. In this study, it was used to identify which Easter-Eggs, that is items that may act as burglary attractors or deterrents (see Figure 3), which were dispersed throughout neighborhood A3, were noticed by the participants. In this sense, eye tracking technology allows the researcher to see through the eyes of burglars in an almost literal way. Here we combine the eye tracking data with the VR-RTA data to deepen our understanding of the reasons behind gaze patterns, i.e., capturing more of the 'why' underlying the eye tracking data.

The results of our analysis indicate that participants who registered more Easter-Eggs (as registered with the eye-tracking data), also perceived a lower degree of guardianship in the neighborhood. These findings suggest that the Easter Eggs drew the attention of the participants and make neighborhoods more attractive for burglary. However, these results by itself do not speak to the decision-making processes. The RTA-task, in contrast, can shed light on the interpretation of the Easter Eggs and how they affect decisions. More than half of the participants spontaneously mentioned the Easter-Eggs during their RTA-task (see Table 6) in regards of using them as a tool to commit burglaries, such as using the containers to climb or the ladder to break into the window. Another interesting finding is that many of the burglars talked about the Easter-Eggs in a matter underlying social control (see Table 6). The Easter-Eggs provide opportunities for them to estimate the residency in a neighborhood. In this way they can determine what kind of people live there, how they go about their belongings and to indicate the level of social control.

The quotes in Table 6 illustrate the level of detail the RTA-task provides and gives us a glimpse of the mechanisms that drive the decision-making process of offenders. Furthermore, it demonstrates how we can discuss their behavior in detail without solely having to rely on subjective behavioral VR data, capturing the ‘why’ behind eye-tracking data and the reason why participants pay attention to environmental cues.

Combining spatial data with RTA

The use of spatial data for determining walking patterns added to our understanding of how burglars move through a neighborhood. Previous studies using spatial data in burglary research have shown burglary experience to affect spatial behavior of burglars (i.e., searching a larger part of the neighborhood and spending more time in affluent areas of a house) in comparison with non-burglars (e.g., Nee et al., 2019). Similarly, a study of Gerstner and Van Sintemaartensdijk (2023) used spatial data to define sensitive areas for break-ins and found that burglars with higher self-control entered these areas less frequently than burglars with lower self-control. Here we use the RTA method to gain more insight into the reasons driving spatial behavior patterns. Figure 5 provides a heatmap showing areas that were most (and least) frequently visited, and identifies places where participants paused and/or may have changed course.

Commentaries provided during the RTA allowed us to inquire about motivations and cognitions driving these observed behaviors. Due to these commentaries (see Table 7) we can get in depth information about the ‘why’ behind their behavior, and their reasoning behind their movement, which otherwise would have been undiscovered.

Combining VR-RTA with interview questions

The previous sections suggest that the VR-RTA approach helps elicit information. In this section we explore whether VR-RTA also enhance peoples’ motivation to disclose information. During the video replay, participants often went beyond sharing details about their scouting process. The enhanced level of detail in participant responses during the RTA-task during a specific situation is something one simply cannot achieve with using an interview. In principle, similar to the analysis of CCTV footage, the VR screen recording can be infinitely replayed, paused, and slowed down and provide a frame-by-frame account of the events that unfold (Philpot et al. 2019). As such it allows for dissecting a decision-making sequence and ask questions to clarify in real-time. However, unlike analyzing CCTV footage, it also allows for linking observations to other relevant factors pertaining to individuals (e.g., psychological states, motivations, dispositions, background characteristics; Van Gelder, 2023).

In general, in the semi-structured interview in this study, participants tended to give short, condensed answers when asked directly about what attracts them in a neighborhood. During the RTA, in contrast, information provided tended to be more detailed, elucidating different steps in the decision-making process without necessarily requiring prompts. The VR-RTA findings also highlight the importance of environmental factors affecting decision that are difficult to capture in traditional interviews that rely on memory.

By way of example, consider some of the differences between responses to the interview question “what attracts you in a neighborhood?” and the information elicited by the RTA-task. In the interview participant 50 would respond: “I only look at cameras”, but the RTA from the same participant uncovered various details: “There are several things I pay attention to. Windows that are open, gardens. It is difficult here because I can’t look over the fences. I would do that to check if the residents were home. This neighborhood is difficult because everyone can monitor each other, there is sight from all the houses. You have to stay in the dark and avoid the lights. A lot to pay attention too”. Participant 72 responded in the interview: “Tips or holiday time”. In the RTA the same participant responded. “This dark alley is ideal for scouting—easy hiding, limited police access, multiple escape routes. Dogs and lights are a no-go. Good targets here.". The interview answer of Participant 94 was equally short: “Wealth”. The RTA from the same participant revealed valuable insights however: “This neighborhood offers abundant hiding places, making it attractive for cover. Limited social activity means fewer people outside, with families as you can see from the toys lying around. Easy balcony access for break-ins, a small, long alley provides good cover. The quiet, boring atmosphere stands out; I prefer busy neighborhoods like Amsterdam for blending in”.

In a similar vein, interview responses regarding deterrent factors in neighborhoods also tended to be brief while during their RTA-task, the participants emphasized the importance or several facets determining surveillance, social control or deterrence. By way of illustration these are the interview questions in response to what deters them in a neighborhood compared to the information from the RTA-task:

Interview answer: “Nothing deters me” (Participant 53), the RTA from the same participant revealed nuanced insights: “In a community where people watch and monitor each other, good neighbors are vigilant about suspicious activities. You have to be careful for the residents”. Interview answer: “Nothing deters me, couldn’t care less.” (Participant 70), the RTA from the same participant revealed different insights: “Vigilance and surveillance are problematic in this neighborhood. Close-knit neighbors oversee everything, making breaking in too risky. No, I wouldn’t break in to this neighborhood, having too many eyes is dangerous”. The examples presented above demonstrate the importance of community dynamics in their decision-making process, which is not fully captured in traditional interviews. Interview answer: “Nothing deters me, I can avoid everything” (Participant 129), the RTA from the same participant provided key insights. “Emphasizes the importance of ease—avoiding visibility is crucial. This neighborhood's lack of attractiveness stems from houses being too close, enabling residents to monitor each other closely”. Interview answer: “Nothing deters me, if it is worth it there is no fear” (Participant 182), the RTA from the same participant provided insightful details: “Child-friendly neighborhoods pose risks. Vigilant parents, more people at home, and neighbors actively watching out make it too risky.

Taken together, the examples above demonstrate how VR-RTA in studying criminal behavior can provide valuable insights into the decision-making process of offenders. During the interviews participants can be prompted to disclose more information, however, using VR-RTA people seemed inclined to provide more information without being prompted. Also, aside from providing more information, they provide more relevant details in their decision-making process. Highlighting again the importance information of the VR-RTA method as an information eliciting tool. By allowing burglars to reflect back on their actions through the RTA method, we tap into their cognitive process, making it easier to talk about their process and resulting in rich and detailed information.


In this article, we introduced a novel method, VR-RTA, that integrates a retrospective think aloud with virtual reality methodology to gain insight into offender decision-making processes. We illustrated this method among a sample of incarcerated burglars, who reflected on their actions from a first-person perspective after they had scouted a virtual neighborhood for opportunities to commit a burglary. By allowing burglars to reflect on their actions whilst observing their own experience, we made it easier for them to elaborate on their choices and to verbalize cognitive processes while on the job. That is, the method allows for capturing in the moment considerations underlying decision making during the crime commission process. The results demonstrate how the application of VR-RTA to the study of criminal behavior can provide relevant insights into the decision-making processes of offenders.

The VR-RTA method bypasses several challenges facing conventional approaches to studying criminal decision-making. First, by letting participants think aloud, it assists in the verbalization of cognitive decision-making processes that have become automated, and hence not subject to deliberate retrieval through interviews or surveys. Second, as the recording is replayed immediately after the action, the time lag between the (virtual) crime and the subsequent data collection is shortened from what is commonly a period of months or even years to a mere few minutes, thus overcoming the problems related to retrospection and memory degradation. Third, our approach also adds to virtual reality research and observational research as it allows for discussing specific behavior of participants with them, rather than having to rely solely on the behavioral or observational data. Finally, the VR-RTA turned out to be an effective way of breaking the ice and building rapport between the experimenters and the participants.

Our first research question regarded the extent to which our results aligned with established findings from prior research into the cues that guide the decision making of burglars. The four different categories of burglary cues, layout, security, occupancy and affluence that emerged from earlier research, also emerged in our study. In line with prior work, layout cues were most frequently mentioned with an emphasis on surveillance. Other frequently mentioned subcategories, such as social control, target hardening, and presence of residents, also aligned with prior work (Nee et al. 2019; Peeters et al., 2013). We interpret these findings as support for the validity of the VR-RTA method. Answering our second question, the VR-RTA method was shown to extend the interview and VR-data in meaningful ways. First, the RTA-task contributed to understanding the ‘why’ behind gaze direction and spatial data. Second, the VR-RTA method was shown able to elicit detailed information which otherwise may have been left undisclosed.

Limitations and Future Directions

We consider this study to be only the first step in the development of a novel and exiting approach to collecting data on criminal behavior and believe that we have only scratched the surface in terms of exploring the possibilities of combing VR technology with think aloud protocols. Inevitably, this study was also prone to several limitations that merit discussion. First, the absence of a control group limits our ability to quantify the extent to which the method enhanced participants' motivation to disclose information. Future studies should include a control group to be able to assess the incremental value of the VR-RTA method for information elicitation. Secondly, it is important to acknowledge that doing a ‘virtual burglary’ is not to be equated with an actual burglary. Despite the realism of our neighborhoods and the high resemblance with real life neighborhoods, what participants do in a VE does not necessarily correspond with how they act in real life. Despite prior research showing that burglars in virtual environments operate in similar ways as in real life, generalizability of (criminal) behavior in VR and behavior in the real world is not a given. Such factors need to be explored prior to being able to speak confidently that results demonstrating in a specific virtual setting apply equally to real world settings. Third, participants did not actually walk through the VE, but used a game controller to navigate the virtual neighborhood instead. Future studies could consider using treadmills so that actually engage in walking to render the experience even more realistic and immersive.


The VR-RTA method serves as a valuable tool to complement existing data collection approaches, contributes to understanding the cognitive process underlying criminal decision making, and allows researchers to access motivations for specific behaviors that otherwise would have been left undisclosed. In this way, this method provides a first-hand offender perspective and allows for examining crime in action. The present study only serves as a first illustration of how of think-aloud protocols can be integrated with VR in research designs. It is our hope that VR-RTA will be implemented across the criminological domain and become an important instrument in the researchers’ toolkit.


Barsalou, L. W. (2008). Grounded Cognition. Annual Review of Psychology, 59(1), 617–645.

Bennett, T., and Wright, R. (1984a) Burglars on Burglary: Prevention and the offender. Aldershot: Gower.

Bergouignan, L., Nyberg, L., and Ehrsson, H. H. (2014). Out-of-body–induced hippocampal amnesia. Proceedings of the National Academy of Sciences, 111(12), 4421–4426.

Branch, J. L. (2000). Investigating the Information-Seeking Processes of Adolescents: The Value of Using Think Alouds and Think Afters. Library and Information Science Research, 22(4), 371–392.

Braun, V. and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology. 3. 77-101. 10.1191/1478088706qp063oa.

Brookman, F., Copes, H. and Hochstetler, A. (2011). Street Codes as Formula Stories: How Inmates Recount Violence. Journal of Contemporary Ethnography - J CONTEMP ETHNOGR. 40. 397-424. 10.1177/0891241611408307.

Cromwell, P. F., Marks, A., Olson, J. N., and Avary, D. W. (1991). Group Effects on Decision-Making by Burglars. Psychological Reports, 69(2), 579–588.

Eccles, D. W., and Arsal, G. (2017). The think aloud method: What is it and how do I use it? Qualitative Research in Sport, Exercise and Health, 9(4), 514–531.

Ericsson, K. A., and Simon, H. A. (1993). Protocol Analysis: Verbal Reports as Data. The MIT Press.

Fox, M. C., Ericsson, K. A., and Best, R. (2011). Do procedures for verbal reporting of thinking have to be reactive? A meta-analysis and recommendations for best reporting methods. Psychological bulletin, 137(2), 316–344.

Gerstner, D., van Sintemaartensdijk, I. (2023). Erforschung von Kriminalität mit Experimenten in der virtuellen Realität – Darstellung eines neuen methodischen Ansatzes. In: Rüdiger, TG., Bayerl, P.S. (eds) Handbuch Cyberkriminologie. Springer VS, Wiesbaden.

Glenberg, A.M. (1997). What is memory for? Behavioral and Brain Sciences, 20, 1-55.

Glenberg, A. M., and Hayes, J. (2016). Contribution of Embodiment to Solving the Riddle of Infantile Amnesia. Frontiers in Psychology, 7.

Gregg, A., Reid, R, Garbrick, A., Williams, V and Aldemir, T. (2017). Canvas UX Think Aloud Observations Report. University Park, PA: The Pennsylvania State University.

Güss, C. D. (2018). What is going through your mind? Thinking aloud as a method in cross-cultural psychology. Frontiers in Psychology, 9, Article 1292.

Hallgren, K. A. 2012. “Computing Inter-rater Reliability for Observational Data: An Overview and Tutorial.” Tutorials in Quantitative Methods for Psychology 8 (1): 23. doi:10.20982/tqmp.08.1.p023.

Hertzum, Morten and Holmegaard, Kristin and c, H.. (2009). Scrutinizing usability evaluation:Does thinking aloud affect behaviour and mental workload?. Behaviour and Information Technology. 28. 10.1080/01449290701773842.

Kesler, T., Tinio, P. P. L., and Nolan, B. T. (2016). What’s our position? A critical media literacy study of popular culture websites with eighth-grade special education students. Read. Writ. Q. 32, 1–26. doi: 10.1080/10573569. 2013.857976\

Langton, S.H., and Steenbeek, W. (2017). Residential burglary target selection: An analysis at the property-level using Google Street View, Applied Geography, Volume 86, Pages 292-299,

Leighton, J. P. (2017). Using Think-Aloud Interviews and Cognitive Labs in Educational Research, New York: Oxford University Press.

Malek, H. B., Berna, F., and D’Argembeau, A. (2017). Reconstructing the times of past and future personal events. Memory 25, 1402–1411. doi: 10.1080/09658211.2017.1310251

Maruna, S. (2001). Reading from a condemnation script. In S. Maruna, Making good: How ex-convicts reform and rebuild their lives (pp. 73–84). American Psychological Association.,

Meenaghan, A., Nee, C., Van Gelder, J.-L., Otte, M., and Vernham, Z. (2018). Getting Closer to the Action: Using the Virtual Enactment Method to Understand Burglary. Deviant Behavior, 39(4), 437–460.

Michel-Villarreal R, Vilalta-Perdomo EL, Canavari M, Hingley M. Resilience and Digitalization in Short Food Supply Chains: A Case Study Approach. Sustainability.2021; 13(11):5913.

Miller, J. F., Neufang, M., Solway, A., Brandt, A., Trippel, M., Mader, I., Hefft, S., Merkow, M., Polyn, S. M., Jacobs, J., Kahana, M. J., and Schulze-Bonhage, A. (2013). Neural Activity in Human Hippocampal Formation Reveals the Spatial Context of Retrieved Memories. Science, 342(6162), 1111–1114.

Nee, C. (2010). Research on residential burglary: ways of improving validity and participants’ recall when gathering data. In W. Bernasco (Ed.). Offenders on Offending: learning about crime from criminals. Devon: Willan Press.

Nee, C. (2015). Understanding expertise in burglars: From pre-conscious scanning to action and beyond. Aggression and Violent Behavior, 20, 53–61.

Nee, C., White, M., Woolford, K., Pascu, T., Barker, L., and Wainwright, L. (2015). New methods for examining expertise in burglars in natural and simulated environments: Preliminary findings. Psychology, Crime and Law, 21(5), 507–513.

Nee, C., van Gelder, J.L., Otte, M., Vernham. Z. and Meenaghan, A. (2019). Learning on the job: Studying expertise in residential burglars using virtual environments. Criminology, 57(3), 481-511.

Newton, A., Rogerson, M. & Hirschfield, A. (2008). Relating target hardening to burglary risk: Experiences from Liverpool. Papers from the British Criminology Conference: An Online Journal Published by the British Society of Criminology. 8. 153-174.

Nisbett, R.E. and Wilson, T.C., 1977. Telling more than we can know: verbal reports on mental processes. Psychological review, 84, 231–259. doi:10.1037/0033-295X.84.3.231.

Peeters, M.P. (2013). Assessing the vulnerability of targets for burglary. Creating a multi-level observational instrument In P. Ponsaers, A. Crawford, De Maillard, J. Shapland and A. Verhage (Eds.), Crime, Violence, Justice and Social Order (pp. 171-  206). Antwerpen: Maklu.

Philpot, R., Liebst, L. S., Møller, K. K., Lindegaard, M. R., & Levine, M. (2019). Capturing violence in the night-time economy: A review of established and emerging methodologies. Aggression and violent behavior, 46, 56-65.

Reinhart, A., Evans, C.,  Luby, A., Orellana, J., Meyer, M., Wieczorek, J., Elliott, P., Burckhardt, P., & Rebecca Nugent (2022) Think-Aloud Interviews: A Tool for Exploring Student Statistical Reasoning, Journal of Statistics and Data Science Education, 30:2, 100-113, DOI: 10.1080/26939169.2022.2063209

Repetto, C., Serino, S., Macedonia, M., and Riva, G. (2016). Virtual Reality as an Embodied Tool to Enhance Episodic Memory in Elderly. Frontiers in Psychology, 7.

Sampson, R. J., and Laub, J. H. (1993). Crime in the Making: Pathways and Turning Points Through Life. Crime and Delinquency, 39(3), 396–396.

Topalli, V., Dickinson, T., and Jacques, S. (2020). Learning from criminals: Active offender research for criminology. Annual Review of Criminology3(1), 189-215.

van den Haak, M. J., De Jong, M. D. T., and Schellens, P. J. (2004). Retrospective vs. concurrent think-aloud protocols: Testing the usability of an online library catalogue. Behaviour and Information Technology, 22(5), 339– 351.

Van Gelder, J. L., Otte, M., and Luciano, E. C. (2014). Using virtual reality in criminological research. Crime Science, 3, 1-12.

Van Gelder, Jean-Louis. (2023). Virtual Reality for Criminologists: A Road Map. Crime and Justice. 000-000. 10.1086/726691.

Van Gelder, J.-L., Nee, C., Otte, M., Demetriou, A., van Sintemaartensdijk, I., and van Prooijen, J.-W. (2017). Virtual Burglary: Exploring the Potential of Virtual Reality to Study Burglary in Action. Journal of Research in Crime and Delinquency, 54(1), 29–62.

VERBI Software. (2021). MAXQDA 2022 [computer software]. Berlin, Germany: VERBI Software. Available from

Van Sintemaartensdijk, I., van Prooijen, J.W., Nee, C., Otte, M., van Lange,P., Personality and burglary: A virtual reality study, Personality and Individual Differences,Volume 196, 2022, 111712,

Van Sintemaartensdijk, I., van Gelder, J.-L., van Prooijen, J.-W., Nee, C., Otte, M., and van Lange, P. (2021). Mere presence of informal guardians deters burglars: A virtual reality study. Journal of Experimental Criminology, 17(4), 657–676.

Wolcott, M. D., & Lobczowski, N. G. (2021). Using cognitive interviews and think-aloud protocols to understand thought processes. Currents in pharmacy teaching & learning13(2), 181–188.

Wright, R.T. and Decker, S. (1994). “Burglars On The Job: Streetlife And
Residential Break-Ins. Boston: Northeastern University Press.

Wright, R., Logie, R. H., and Decker, S. H. (1995). Criminal expertise and offender decision making: an experimental study of the target selection process in residential burglary.
Journal of Research in Crime and Delinquency, 32(1), 39–53. .

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