Student: Jim Müller - 10897976 Docent: Jasper Winkel
Does gaming alter your decision-making?
The influence of gaming-experience and gaming-preference on effort-based decision-making in virtual reality
Abstract
The effect of realism on effort-based decision-making (EBDM) is examined with 34
participants performing a decision task in virtual reality. Additional this paper also examines the effect of video game experience and video game preference on decision-making. All participants performed three different conditions of the (decision) task where the degree of realism was manipulated. The participants had to operate a bicycle pump to perform the requested task. The findings demonstrated that there was no significant effect of realism between the three conditions of the task and no effect of video game preference on reaction time. Contrary to our expectations we found a significant slower reaction time under participants with a substantial amount of game experience.
Introduction
People make decisions every day, it is impossible to think of a day where you made no decisions at all. Often you have to think about certain decisions in a particular way, e.g. should I go to the store to buy food, or should I spend less effort and just order food online. If you buy food in a store instead of online it feels much more realistic. That is because if you grab food in a store you can feel that you touch something, you can see it and probably even smell it. But if you buy something online, you cannot do either of those things and you only see a picture of the food. This seems to be less realistic, because of the lack of senses that are
being stimulated when buying online. Which is not only the case for online shopping
decisions but for every decision that could possibly be made online, from writing an e-mail to navigating through an online map. The possibility to make decisions at any given moment in any place is a convenient matter but what kind of effect does it have on the decision making of people? Will there be any advantages or disadvantages in making a decision in a different environment, and will people who spend more time in a pc-environment have any benefits at making a decision online?
In the decisive moment of the example mentioned above, you have to make an implicit trade-off between the amount of effort you want to spend, and the potential reward you can obtain for the given amount of effort. This phenomenon is called effort-based decision-making (EBDM) and has been researched many times before. It establishes different links with risk taking, impulsiveness and Dopamine (DA) (Kurniawan, Guitart-Masip, & Dolan, 2011). DA seems to play a significant part in the regulation of an effort-based decision. This is demonstrated by an example from a study of Bardgett, Depenbrock, Downs, Points & Green (2009) which shows that rats with a shortage of DA in the nucleus accumbens make less effortful decisions in a T-maze test. This proves that dopamine plays a role in enhancing an organism’s motivation toward effortful decisions. All organisms spend effort to obtain a desired reward but it seems that the preference to take an action decreases when the effort increases and/or the reward decreases. (Kurniawan, Guitart-Masip & Dolan, 2011)
As mentioned before EBDM is a widely studied phenomenon, only the effect of realism on EBDM is still a relatively unknown factor. Realism is defined as the opposite of abstract. It is the term to describe how real a particular experience seems to be and this definition is often used together with virtual environments. A sense of realism is defined as how accurately does the environment represent objects, events and people (McMahan, 2003).
As demonstrated with the example of buying food online and with the explosive growth of e-commerce, the number of decisions people have to make online in a so called virtual environment (VE) is increasing (Häubl & Trifts, 2000). Together with the increasing number of decisions made online and the still unknown influence of realism on EBDM this paper will seek an answer to the question: 'What is the effect of realism on effort-based decision making?'.
A method to measure EBDM and to manipulate the effect of realism is virtual reality (VR). In VR different components work together to create a sensory illusion that is able to produce a more or less realistic simulation of the reality. VR gives many advantages in the field of research by creating an interactive, multisensory virtual environment (Bohil, Alicea & Biocca, 2011). The main advantage is the possibility to manipulate multimodal stimulus inputs. This makes it possible to let the user feel more or less present in the represented environment and gives the potential to simulate and measure realistic psychological and behavioral responses. With VR, it is also possible to let the users move and have physical interaction with virtual objects and make combinations of stimuli that are hard to find in the natural world. Also VR allows naturalistic interactive behavior to take place in a controlled setting (Bohil, Alicea & Biocca, 2011).
VR needs to be simulated on a display to let the user perceive the virtual world. This is often done on a high-end computer. To increase the immersion virtual glasses are often
connected to the computer to let the user perceive the world and experience the feeling he is present. An example of such device is the oculus rift dk2. These glasses contain a monitor which displays the perspective of the camera horizontally to the left and the right eye mimicking the natural disparity between the eyes. The brain fuses these images together to create a sensation of three-dimensional space (Bohil, Alicea & Biocca, 2011). The point of
view of the user is controlled by two virtual cameras, which changes the location of the images depending on the movements of the user's head. This creates a visually immersive experience for the person, which can be made even more immersive by the combination of auditory stimulation and by haptic feedback devices.
Before having a closer look on how realism is manipulated in VR, a couple of the common criticisms of virtual reality are discussed (Bohil, Alicea & Biocca, 2011).
The cost of advanced VR systems is high, only the glasses can cost about 800 euros without the computer equipment that can handle the operation. Nevertheless, the VR systems were more expensive years ago and this shows a rapid decrease in cost and might be more applicable in the future. The technological skills to operate and create a virtual world are pretty demanding, modeling, texturing and programming cost a lot of work. However, tools are released that will make it less demanding and more simplified to operate. The last concern for the users of VR is cyber sickness; there are reports that users experienced nausea,
headache, disorientation and other motion sickness symptoms after using VR. This is mostly because of the sensory information that did not match with the sense of motion people are experiencing. With better optimized VR systems, the amount of people that become cyber sick should decrease but may still persist under people with a vestibular balance disorder (dysfunction). It is important to note, that cyber sickness (virtual reality sickness) is different from motion sickness, since it can be caused by a visually-induced perception of self-motion instead of real-self motion such as driving in a car (LaViola, 2000). Cyber sickness is also not the same as simulator sickness, which is sickness caused by oculomotor disturbances in a non-virtual reality simulator (Stanne, Kennedy & Drexler, 1997).
presence. Immersion is the metaphorical term for the physical experience of being submerged in water. People feel immersive if they experience the sensation of being completely
surrounded by another reality (McMahan, 2003). In this study, we use the definition of immersion in another context, namely in a VR environment.
There are two types of immersion, perceptual immersion and psychological
immersion. The first type of immersion is accomplished by blocking as many of the senses that are obtained from the outside world and by only letting the user perceive the senses from the VR (McMahan, 2003). This can be done with the aid of goggles (oculus rift dk2),
headphones and/or gloves. The second type of immersion depends on the user mental absorption in the VE (McMahan, 2003). The level of immersion can be determined by the amount of active sensory and motor channels connected to the VE and by the duration of the sensory stimulation and the responsiveness of the motor inputs (Bohil, Alicea & Biocca, 2011). To create a sense of immersion there are three conditions that are vital. First, the user's expectations of the VE must match the environment norm (conventions) closely. Second, the user's actions must have a significant impact on the environment. And third, the
norm/convention must be consistent in the virtual reality (McMahan, 2003).
The sense of presence is the feeling of the user of 'being there' and it is the result of the perceptual and psychological immersion (McMahan, 2003). In most studies the sense of presence is measured by self-report, but researchers have looked for physiological indicators to measure the effect of presence. This is done by looking at bodily responses like heart rate and skin conductance (Bohil, Alicea & Biocca, 2011). In this study we anticipated that if more senses are stimulated in a VR setting, the more immersive the environment will be and the more the user has the feeling he is present in another reality. With these two factors, the sense of immersion and the sense of presence, it is possible to increase or decrease the effect of realism in a VE.
While the effect of realism is an important factor to study it seems that games or tools with video attributes, such as task difficulty, realism and interactivity, affect learning outcomes in a VE. This phenomenon is called gamification, which is a new technology development that incorporates video attributes, and elements of game play in nongame situations (Prince, 2013). PC-based videogames as an instructional tool are emerging and getting increasingly more popular in education, industry and the military. Furthermore, the research on
gamification is increasing and new studies show that by 2015 more than 50% of organizations managing innovation processes have gamify aspects incorporated (Hamari, Koivisto & Sarsa, 2014).
It is demonstrated that students have a higher intrinsic motivation and self-efficacy after implanting a variation of gamification techniques in education (Banfield & Wilkerson, 2014). This might underline the importance to understand the effect of realism while making effort-based decision. When education is designed with gaming elements there could be a change in experience of realism, which could have an effect on the decision making of people.
More research shows that prior videogame experience, measured by frequency of videogame use, is predictive of the user's future performance in video-game based environments. Prior videogame experience also significantly predicts motivationally and affective learning outcomes, such as satisfaction, ease of the task and time spent engaging in a game (Orvis, Orvis, Belanich & Mullin, 2007). People with a lot of prior videogame experience show that they perform better than people with less videogame experience regardless of the task
difficulty. This could be explained by the model of domain learning, which demonstrates that users with a greater level of domain knowledge (I.E prior video game experience) prior to the
instruction tend to apply more advanced strategies and a greater variety of strategies (Murphy & Alexander, 2002).
Individual’s prior videogame experience and knowledge has not only been proven to predict future performance in videogame-based environments (Alvarez, Salas & Garofano, 2004; Frey, Hartig, Ketzel, Zinkernagel & Moosbrugger, 2007), but also predicts the importance in other PC-based instruction environments (Brinkerhoff, Koroghlanian, 2005; Shih, Munoz & Sanchez, 2006). These last findings may underline the importance of prior videogame experience in the increasingly PC-based and videogame-based environments such as gamification were people have to make decisions. It also suggests that there might be a difference in learning and motivation performance on task between individual with a low or high amount of prior experience (Clarke, Ayres & Sweller, 2005). While still being discussed heavily, researchers demonstrate that knowledge and skills that are obtained during playing a video game could transfers to real-world tasks (Orvis, Horn & Belanich, 2008).
In addition, a number of hours spent gaming each week is associated with impulsivity, but might vary between different genres of video games. An example shows that two popular game genres, namely first person shooter (FPS) and strategy games had a close relationship with impulsivity. People playing FPS games had a positive correlation with impulsivity while people playing strategy games had a negative correlation with impulsivity (Bailey, West & Kuffel 2013). Impulsive behavior could be labeled as urgency, lack of premeditation, lack of perseverance and sensation seeking (Whiteside & Lynam, 2001). The number of hours spent gaming per week was associated with increased impulsivity. The more impulsive a person was, the more errors they made and the shorter their reaction times was compared to people that were less impulsive (Edman, Schalling & Levander, 1983).
Based on these previous studies this research will focus on the effect of realism while performing an effort-based decision task in virtual reality. It will also focus on the effect of the prior videogame experience, and videogame preference on decision-making.
The main hypothesis of this paper is: The more realistic a component of a task is, the more significant it is in decision-making. This study examined 51 participants performing an EBDM task in virtual reality. The participants participated in three different conditions were either the effort or the reward has increased or decreased in the degree of realism. On every trial the participants had to make a choice between a high effort/high reward track (HE/HR), or a low effort/low reward (LE/LR) track. Each completed condition gave the participant a point of indifference (POI), which shows the preference of the amount of effort a participant wants to spend for a certain reward. The anticipation is that when the condition is more realistic it is more significant in decision-making, therefore in the conditions were the effort or reward is made more realistic there should be a lower POI than in the baseline condition.
This paper will also focus on prior video game experience, and the influence of different genres of video game. This can be explored and measured by the reaction time at each decision per trial in a task, and through two open-end questions about the amount of hours videogames played weekly, and which genre the participants prefer. People with prior video game experience should have an advantage because of an increase in impulsivity, knowledge and motivation in a pc-based environment compared to people with little or no video game experience.
The second hypothesis is: People with prior video game experience are more
impulsive while making decisions in a pc-based environment compared to people with little or no video game experience. This advantage might be seen in the reaction time of
participants with prior game experience they have a lower reaction time per trial on the
will be compared between participants with a low amount of prior video game experience and participants with high amount of prior video game experience.
The last hypothesis of this paper is: People with fast-paced video game preference are more impulsive than people with a slow-paced video game preference. The difference in genre of videogames, and impulsivity should be noticeable by an average lower reaction time with people playing fast action paced game (E.G action, FPS) compared to the reaction time of people playing slow action games (E.G Strategy, puzzle).
Method
Participants
In total 51 people participated in the experiment. Of those, 16 were excluded from analysis due to ceiling effects in measurements. One participant did not partake in all three of the conditions due to nausea, and was thus excluded. Of the remaining 34 participants, 44.12 % were female, 85.30 % were students and the average age was 22.41, whereby the standard deviation of age was 0.57. Participants were not paid for their attendance, but could win payment of the mean amount of coins over conditions, with each virtual coin representing 10 eurocents. One participant was awarded this money.
Task
The task was run in unreal engine 4 on a computer executing a windows operating system, which contained an NVidia video card 9800 gtx that was strong enough to be able to display the graphical power of the virtual reality task.
Participants completed the baseline, realistic reward and realistic effort conditions in a counterbalanced order. Each condition consisted of 13 trials, all implemented in virtual reality. Participants were instructed to power a mine cart over a track by making pumping motions with a bicycle pump. At the beginning of each trial, participants were given a choice between a HE/HR route or a LE/LR route. Color-coding in presentation of the tracks
informed participants of the amount of effort a route would require; green sections of the track required no pumping input, orange sections of the track required medium effort and red sections of the track required high effort pumping.
In the baseline condition [fig. 1], the different route options and coin rewards were displayed on two different computer screens, one on the left side of the virtual room and one on the right. The coins were displayed abstractly on these screens as stacked orange bars.
After choosing a route with a mouse click, a third display in the middle of the room showed a power bar and progress within the chosen track. Participants were only able to track their progress on this screen: no visible cart was moving.
In the realistic reward condition [fig. 2], participants were in the same virtual
environment. Choices were again represented on two different screens, but now rewards were realistically represented as stacks of golden coins on the left and right side of a desk in front of the participant and no longer on the computer screens. After choosing, the coins of the preferred route would fly into a chest in front of the participant. Then participants drove the cart the same way as in the baseline condition, and saw their progress in the middle screen. In the realistic effort condition [fig. 3], participants found themselves in a mine cart inside a room with two screens displaying the different route options and abstract coins as orange bars. After selecting a route, a large door would open and they drove themselves into an outside natural environment [fig. 4], using the same bicycle pump that they now saw integrated into their cart. Some sections of the outside tracks were overgrown with either grass (medium effort) or shrubs (large effort).
Before the start of the experiment, participants completed at least one test trial in every condition, to make sure they understood the amount of effort the different colors represented and to make sure how to operate the cart and where to look for effort, reward and progress information.
Reward modifier
For each HE choice the difference between track rewards reduced and for each LE choice the difference between track rewards grew by using the reward modifier. For each trial the reward for both tracks was calculated by subtracting the total effort values from both tracks (zero for green, two for orange and four for red). These values were multiplied with the reward
modifier. The outcome was then added to ten for the high effort track and subtracted from ten for the low effort track.
The value of the reward modifier ranged between 0, which means no difference between rewards, and 1.25, which was the maximum possible reward (20 coins) divided by the maximum possible difference between tracks (16). Each trials started with a reward
modifier value of 0.625, which was the maximum reward modifier value divided by two. For each HE choice a value was subtracted from the reward modifier value and for each LE choice a value was added to the reward modifier. The added or subtracted values increased for each consecutive choice of the same effort type. This value was 0.02 for the first, 0.05 for the second, 0.1 for the third and 0.2 for the fourth or a higher consecutive choice of the same type of effort. With each switch in effort type this value dropped back to 0.02. The minimum value of the reward modifier has been set to 0 to avoid that the LE choice pertains a higher reward than the HE choice. The maximum value of the reward modifier has been set to 1.25 to avoid scores that are higher than 20 coins.
Input Device
For this experiment we created a custom-made input device. The goal of this input device was to make participants feel that they were giving considerable effort during the task and to enhance one’s immersion by mimicking the VE. In the VE, participants were driving a mine cart, similar to a human powered handcar. To mimic the motion made operating a virtual mine cart we chose a bicycle pump as the base of our input device. Operating a bicycle pump (‘pumping’) resembled the motion one would make operating a handcar. Furthermore, the (air) resistance felt while operating the bicycle pump, made its usage an effortful activity. In order to make this bicycle pump an appropriate input device for a computer, we attached a strip of aluminum to the handle of the pump. This strip of aluminum covered the entire length of the pump. Since this strip was only attached to the handle, it goes up and down, along with the pumping motion. Over the aluminum strip, a computer mouse (Logitech G300) was fixed. Consequently, when moving the bicycle pump-handle up and down, the aluminum strips moved similarly along the fixed computer mouse. In this manner, the computer mouse could register the motions of the pump.
To keep the pump in its place the underside of the pump was attached to a MDF board with a chair placed on top. In this manner the weight of the participant kept the board fixed in its place. Therefore, potential differences in tilting of the pump and participant-to-pump distance between participants are minimized.
POI
As a measure of the relationship between reward and perceived effort, the point of
indifference (POI) for each subject was determined per condition. Assigning POI values has demonstrated to be a reliable method for measuring individual differences in subjective effort (Westbrook, Kester, & Braver, 2013). The POI is reached when the subject no longer
expresses a preference for the options. At this point the subject will choose the HE/HR just as much as the LE/LR.
The POI can take on a value between 0 and 1.25. To determine the POI value for each condition, the average of the values of the reward per unit effort modifier of the last four trials was calculated. When the POI was low, the subject needed less reward to choose the HE/HR option. A higher POI meant that the subject needed a higher reward to choose the HE/HR option. When the POI was equal to zero the subject chooses the HE/HR option, without consideration of the effort required.
The differences in POI values represent the differences in perceived effort. The representation of the effort and reward could account for these differences. When reward representation is constant, differences in the POI values are due to perceived effort. When effort representation is held constant, differences in the POI values can be attributed to perceived reward.
Gaming
As a measure of impulsiveness, the reaction time for each subject was determined per condition. Lower reaction time is associated with a higher impulsiveness behavior, and a higher reaction time is associated with a less impulsiveness behavior.
As a measure of prior game experience one open-ended item modified from Orivis et al. (2006) was used. The item was as following: ‘in a typical week, how many hours do you play videogames?’
As a measure of preference video genres, an extra open-ended item was added. The item was as following: ‘When playing video games, what kind of games do you prefer? (e.g. strategy, action (FPS), simulation or sport (racing)). These three measurements could
establish a link between prior game experience and impulsiveness, and might establish a relationship between impulsiveness and gaming genre.
Procedure
The experiment took place within three weeks between Monday and Friday. Each day there was a possibility to perform the experiment with 6 participants. Every experiment had an average duration of two hours. Participants signed an informed consent, which guaranteed their anonymity and informed about the chance of cyber sickness while performing the task. They also received an information folder about the goal, instruction, procedure and a
screening list. There was a minimum of two experimenters present at all times to welcome the participants. On arrival of two participants at the same time, one pilot leader guided a
participant to the cubicle to let him/her answer a variation of questionnaires on a computer including the two open-ended questions about prior game experience and game genre preference. The second participant went directly into to lab and took place behind the computer to perform a virtual decision task three times in a row seen through the oculus rift
DK2. The pilot leader explained that the decisions should be purely made on personal
preference, and that he should keep the power bar in green while doing a pumping motion on the bicycle pump. If the participant understood the information the pilot leader should let him practice one trial in all three conditions. The questionnaires took in total about 45 minutes to complete, and the virtual decision task about 15 minutes per condition. The experiment took about 1.5 hour per participant.
Results
From the total 51 participants that were examined in this study, 16 were excluded due to ceiling effects and 1 due to the effect of nausea. The data of these participants were not included in the main analysis.
The remaining data has been used in the analysis. For every condition the average POI score and the standard deviation has been calculated (Table 1).
Table 1
Mean Point of Indifference score and Standard deviation for the baseline, realistic reward and realistic effort condition.
Note. The POI value range is between 0 and 1.25
A one-way ANOVA repeated measure has been used to analyze the mean difference of the POI score between the within-subject variable conditions (baseline, realistic effort and
realistic reward). Mauchly’s Test of Sphericity indicated that the assumption of sphericity had been violated, x^2(2) = 7.091, p =.029.
A repeated measure ANOVA with a Huyn-Feldt correction determined that there was no significantly difference between the conditions, p > .25. The effect of realism on a task component had no significant effect on the POI between the conditions. We anticipated that the data showed a significant drop of POI value in the realistic reward and realistic effort condition this expectancy was not met.
Condition POI Standard deviation Baseline 0.48 0.31
Realistic Reward 0.45 0.31 Realistic Effort 0.54 0.37
Although the excluded data might not be relevant for the main analysis this data could be still relevant for the effect of prior game experience, videogame genre preference, and reaction time. Therefore, of the 51 participants only the data of 4 participant’ reaction time were excluded, 2 due to missing data points and 2 due to the reaction time being in one or more conditions higher than 2.5 times the standard deviation. The data of the remaining 47 participants were included in the second analysis (Table 2).
Table 2
Mean Reaction Time and Standard deviation for the baseline, realistic reward, realistic effort condition, and all three conditions combined in seconds.
s
Participants were divided into two groups based on their answer on the question: ‘In a typical week, how many hours do you play videogames?’. Participants that answered to play more than 0.5 hours weekly were classified in the group with game experience (N = 23).
Participants that answered to play less than 0.5 hours weekly were classified in the group with no game experience (N = 24). The group with game experience had an average of 6.94 hours weekly played with a standard deviation of 5.71. While the group with no game experience had an average of 0 hours weekly played.
For every group the average score and standard deviation were calculated in table 3, and displayed in figure 5.
Condition Reaction Time Standard deviation Baseline 6.08 3.52 Realistic Reward 6.12 3.36
Realistic Effort 5.63 3.31 Baseline, Realistic- 5.94 3.20 Reward & Effort
Table 3
Mean Reaction Time and Standard deviation for the group with Game Experience and the group with no Game Experience in seconds.
An independent sample t-test has been used to analyze the mean difference on the between-subject variable group (game experience vs. non game experience). The assumptions of normality has been violated, the shapiro-wilk test showed a significant value for the group with no game experience, p = < .043. This means that the data of one group significantly deviated from a normal distribution. Therefore, the Mann-Whitney U test has been used since this is a non-parametric test that does not require the assumption of normality. From this test and data, can be concluded that reaction time in the GameExperience group was statistically significantly higher than the NoGameExperience group (U = 133, p =.002). This result did not meet the expectations of the hypothesis. The hypothesis anticipated a significantly lower reaction time for the group with GameExperience instead of the group of
NoGameExperience.
Group Reaction Time Standard deviation Game Experience 7.46 3.72 No Game Experience 4.49 1.66
Fig. 5. Mean Reaction Time for the group with Game Experience and the group with no Game Experience in seconds.
Game Experience = > 0.5 hours weekly played No Game Experience = < 0.5 hours weekly played
To further explore the relationship between Reaction Time and Hours Weekly Played a Spearman’s rank-order correlation was run. There was a moderate, positive correlation
between Reaction Time and Hours Weekly Played, which was statistically significant, Rs(8) = .472, p = .001. (Figure 6)
Fig. 6 - Relationship between Hours Weekly Played and Reaction Time in Seconds.
To analyze the relationship between reaction time and videogame genre preference the participants were divided into 4 groups based on the answer of the open-ended question: ‘When playing video games, what kind of games do you prefer?’ Participants that answered nothing or ‘no preference’ were classified into the group, no game preference (N = 12).
Participants that answered action or another fast paced genre were classified into the group, fast-paced game preference, (N = 10). Participants that answered strategy, or another slow paced genre were classified into the group, slow-paced game preference (N =6). The rest of the participants gave mixed answer with both fast and slow paced games, they were
classified into the group, mixed-game preference, (N = 19). The average reaction time and standard deviation has been calculated for every group (Table 4).
Table 4
Mean Reaction Time and Standard deviation for the groups, no game preference, fast-paced game preference, slow-paced game preference and mixed-game preference in seconds.
A one-way independent variation analyses (ANOVA) has been used on the average reaction time with between-subject variable (no game preference, fast-paced game preference, slow-paced game preference, mixed-game preference). There was no significant effect found of game preference on reaction time and it can be concluded that there is no significant difference between the various groups. This result did not meet the expectations, it was anticipated that there would be a significant lower reaction time for the group with fast-paced game preference compared to the group with slow-paced game preference.
Group Reaction Time Standard deviation No game preference 5.28 2.78 Fast-paced game preference 6.03 2.78
Slow-paced game preference 5.58 3.24 Mixed-game preference 6.43 3.75
Further, to explore if reaction time does relate to impulsiveness the result from the NEO personality inventory that asses the big five personality traits one of them being neurotiscm which includes a category of impulsiveness (Costa & MacCrae, 1992) has been examined. A Pearson correlation between impulsiveness and reaction time has been run. There was no significant correlation found between the score on the NEO personality inventory and reaction time.
Discussion
In this paper the effect of realism on effort based decision-making is examined. The main hypothesis of this paper is - the more realistic a component of a task is, the more significant it is in decision-making. Therefore, we anticipated that if the condition felt more realistic it would have a lower POI than the less realistic condition, the baseline.
However, the results demonstrate that participants did not vary significantly in motivational preference between the baseline, realistic reward and realistic effort conditions. Therefore, the first hypothesis is refuted and it can be concluded that people making a decision in a realistic setting, e.g. in a store, does not vary from the decision making in a less realistic setting, e.g. electronic commerce. There is no effect between how realistic a setting is and how much impact the setting has on the decision making of the person.
To explore the effect of prior video game experience on decision-making the next part of this paper is the question if people with high prior video game experience have an
advantage while making decisions in a pc-based environment compared to people with little or no video game experience. The results demonstrate that people with prior video game experience, which was measured in hours weekly videogames played, do not have a faster reaction time than people with lower video game experience. In fact, people with more experience have a significant slower reaction time than people with less gaming experience. This outcome contradicts the second hypothesis, that people with more prior video game experience are more impulsive while making decisions than people with little or no video game experience.
The last question examines if people with different video game preferences have a difference in impulsivity measured by reaction time. The third hypothesis is that people with fast-paced video game preference are more impulsive than people with a slow-paced video game preference. The result demonstrates that there is no significant difference between the
groups with different gaming preferences and therefore the last hypothesis is also refuted Therefore it is possible to conclude that it does not matter what preference the participants actually have, their reaction time is not being altered by game preference and thence there is no difference found in impulsivity between the participants. No evidence is found in this paper that people who played strategy or slow-paced games have an overall slower reaction time while making decisions in pc-based environments. This outcome applies to people with fast-game preference who do not have an overall faster reaction time while making decisions in a pc-based environment.
The unexpected result of this study is that people with less video game experience actually had a lower reaction time than people with more video game experience contrary to the expectation. This outcome might be explained by two factors. First, most of the participants, who participate in the experiment, experience the virtual reality for the first time. This could have an impact on the reaction time, because people who have more experience playing video games are probably more enthusiastic and motivated to try the new technology. Second, the design of the virtual reality task is highly similar to a video game, which might motivate the people who regularly play video games and are receptive for such an environment. These factors might increase the reaction time of the people with prior game experience, because they are more immersed in the task and looked around longer before making the decisions. To confirm this speculation more research should be done, and questions should be asked to participants if they had ever any experience with virtual reality, and how immersive and present they felt in the virtual reality task. This could explain the high reaction time of people with more prior game experience.
high amount of prior game experience could have experienced the virtual reality for the first time. Therefore, it should make no difference in reaction time. On the other hand people with a lot of video game experience have a greater chance to already have experience with virtual reality, since they are more interested in video game and the related technology.
Also because of their interest in the related technology is greater they might just be more distracted while performing the task in VR, and therefore they simply take more time to make a decision.
However, if this is true the difference that is demonstrated should be even greater if you take VR experience into account, since one group already had the experience and is less prone to the first time VR experience. Whatever might be true a follow-up study is needed to be able to answer this correctly and take at least VR experience into account.
Further, since the results that has been found between the groups of gaming experience are very significant p = .02, this could imply that there are more than these factors mentioned above that could account for these unexpected results.
The next item what might be a restriction in the conclusion is that reaction time is not a real measurement for impulsivity. The articles that mentioned the consistency only found
correlation between impulsivity, prior game experience and game preference and a correlation between impulsivity and reaction time. Because correlation is not the same as causation and to be absolute sure about the finding that people with more game experience are less
impulsive and that reaction time is not manipulated by another factor is to compare the reaction time with a NEO personality inventory or a NEO five-factor inventory. In the results section we explored the relationship between reaction time and impulsiveness and did not found a significant correlation. Therefore it can be concluded that the increase or decrease of the reaction time from the participants does not relate to the impulsiveness of a participant.
There are a couple of improvements which can be made while repeating this experiment. The first is to change the open-end question, like ‘when playing video games, what kind of games do you prefer?’ into a closed question with a couple of different video game genre options to choose from.
Therefore, it prevents that people are giving more than one option as answer, and that makes it easier to compare the different video game preference between fast games and slow games. In this paper four groups were created, because participants gave a blank answer, and others gave multiple preferences as an answer instead of only a fast-paced game or a slow-paced game preference. This closed question with a couple of fixed options will not only make it easier to compare and answer the hypothesis, but will also decrease the amount of data excluded from the analysis.
Furthermore, there will be improvements needed during the testing and designing phase of the experiment. Since a lot of the data have been excluded due to ceiling effects, the task should be altered to prevent this. One option is to make the trials and conditions more difficult, because most participants will always choose for the highest reward regardless the amount of effort it cost. The task was too easy for many participants which results in no difference found in the data within the participants. An option is to test the internal
consistency reliability at the pre-test and alter when needed before the test. Another option is to add more than two decisions with an increasing difficulty to decrease the chance that all participants make the same decision.
During the testing-phase some participants controlling the bicycle pump experienced a change in pumping difficulty, this happened due to accidentally changing the mouse setting. Therefore, the possibility exists that not all the participants had the same experience, and difficulty while making decisions in virtual reality. In a follow-up study this setting of the
mouse should be locked and not be able to change by the accidently touching of a participant. The effect of realism on a task component might be never significant, but it can be further explored in a follow-up study with more participants than 34 and no exclusion due to ceilings effects. To conclude, this study does not find what the effect of realism is on effort based decision-making and cannot make a distinction between the decision making in an environment where the effort or reward felt more realistic. This paper emphasizes the result that in a rapidly developing world where every day more people are ‘living’ online and therefore more decisions are made online there is no significant impact in which environment a decision is made. The choice is yours: online or offline.
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