Effort-based decision making while influenced by the realism of effort and reward representation : an account of individual differences in risk propensity

(1)

Bachelor Project

Effort-Based Decision Making

while Influenced by the Realism of Effort and Reward Representation:

An Account of Individual Differences in Risk Propensity

Daniël van den Broek

Student number: 5694299 University of Amsterdam

(2)

The Role of Risk Propensity in Effort-Based Decision Making

(A Study within Virtual Reality)

Daniël van den Broek University of Amsterdam

As technology advances, psychological research in effort-based decision making can benefit from these methodological improvements by implementing virtual reality to study the effects of differences in realism for effort and reward representation, while taking individual differences in risk propensity into account. The present study developed a computer-driven task in a virtual environment to examine effort-based decision making, while using the UPPS Impulsive Behavior Scale to asses risk propensity. 51 Subjects participated (19 women, 32 men). Three hypotheses were formed: (a) realism of effort, realism of reward and a risk seeking nature have a positive effect on presence, (b) realism of reward has a positive effect on exerted effort, while realism of effort will have a negative effect on exerted effort, (c) risk propensity has a positive effect on exerted effort. No conclusions could be made following the results, except for an effect on presence. Future research may focus on the benefits of implementing virtual reality in psychological studies on personality and risk propensity.

(3)

Introduction

Risk propensity (risk taking, sensation seeking, or impulsive tendency) is a common characteristic of human behavior as testimonies of high risk activities like compulsive gambling, smoking, aggression and drug or alcohol use are well documented throughout the field of

psychology, sharing a common background as sensation seekers (Lauriola, Panno, Levin & Lejuez, 2014). Impulsivity, along with sensation seeking, is considered as a driving force of overall risk taking (Zuckerman & Kuhlman, 2000). Both concepts correlate so reliably with people's

involvement in risky activities that they are acknowledged among personality psychologists as measurements of risk taking itself (Lauriola, Panno, Levin & Lejuez, 2014). Personality, thus, is generally regarded as the strongest influencer of risk behavior (Nicholson et al., 2006). The Five Factor Model, according to McCrae & Costa (1990, quoted by Whiteside & Lynam, 2001), incorporates sensation seeking in the domain of extraversion, with Eysenck and Eysenck (1977) mentioning risk taking specifically as being strongly correlated with extraversion. In addition, people with this tendency are often described as persons in need of stimulation, with high activity levels, brimming with energy, and with a tendency to exert more effort than introverts when it comes to task performance (Beauducel, Brocke & Leue, 2006). Finally, as one would expect, risk propensity seems to be inversely related to age, with teenagers more prone to exert risky behavior than the elderly (Nicholson, Soane, Fenton, O'Creevy & Willman, 2006). The latter also stress the dualistic nature of risk: both general and domain-specific risk propensities are possible. People inconsistent in their approach towards risk in different domains are regarded as lacking a strong propensity to either take or avoid risks, with them taking risks in some situations, but not others. This could vary depending on the demands of situations, or could be consistent, taking certain risk with regards to corporal decision-making, but not in private domestic areas.

Whereas the clinical field of psychology has previously linked personality traits to decision making and risk taking extensively, past research on risk taking has not only been guided by trait

(4)

psychology, with Kuhnen & Knutson (2005) finding a neural basis for risk propensity in the ventral striatum; the nucleus accumbens (NAcc) showing activity while making risky choices, a finding replicated in effort-based decision making (EBDM) experiments (Doya, 2008). Kuhnen and Knutson (2005) mention that the anticipation of rewards activates the NAcc through dopamine, which may cause individuals to switch from risk averse to risk seeking behavior. As an element of the basal ganglia, the NAcc is a part of the same dopaminergic neural network that is involved in EBDM. In this reward system, the ventral part of the anterior cingulate cortex (ACC) is connected with the NAcc and is involved with assessing the salience of motivational information. The ACC is particularly involved when effort is needed to carry out a task in problem-solving situations,

commonly addressed in animal studies examining EBDM. Furthermore, Doya (2008) found that the expectation of a high reward motivates subjects to choose an action despite a large cost, for which dopamine in the ACC is responsible. And finally, Walton, Kennerley, Bannerman, Phillips and Rushworth (2006) found that top-down signals from the ACC to the NAcc are vital in overcoming effort-based response costs. In sum this could mean that risk seekers will experience an even greater inclination towards effort expenditure than risk averse subjects: under the influence of dopamine the prospect of a high reward will motivate the average person to exert effort, but in the case of a risk seeking individual it will do even more so. Apart from such guidance by neurosciences, it is also due to economic heavyweights studying financial decisions under risk that risk seeking behavior is now more widely understood.

The concept of risk propensity has been examined in papers through the lens of expected utility theories, of which prospect theory posed by Kahneman and Tversky (1979) has been highly influential. Opposing the common opinion, prospect theory contradicts the assumption of rationality within economic agents, underlining the impulsive nature of decision making, as Kahneman and Tversky (1992) remark that: “people can spend a lifetime in competitive environment without being able to avoid framing effects or apply linear decision weights” (p. 317). The theory distinguishes

(5)

two phases in the choice process: framing and valuation. In the framing phase the decision maker constructs a representation of the acts, contingencies and outcomes that are relevant to the decision. In the valuation phase the decision maker assesses the value of each prospect and chooses

accordingly. Choice options are prospects framed in terms of gains and losses, and as such costs in terms of effort can thus be garrisoned under losses, while the representation of reward contributes to the valuation of gains, thereby influencing the risk seeker's decision making process as to maximize gains and minimize losses. While weighing effort costs against its produced benefits, exerted effort will then decrease as effort costs increase due to the proposed framing and valuation phase. The importance of prospect representation in decision making is the reason that this study chose to implement virtual reality (VR) to manipulate the realism of both effort and reward in studying individual differences in risk propensity during EBDM.

VR allows for new possibilities in representing prospects when researching EBDM in experimental tests. Past limitations in a realistic approach towards effort and reward representation may have been diminishing the effects of effort or reward while researching EBDM, as the

operationalization failed to establish the constructs properly. As VR allows for the creation of naturalistic environments that at the same time can be controlled optimally it offers a platform that balances ecological validity, and with it generalizability, with experimental control (Bohil, Alicea & Biocca, 2011), a feat long deemed impossible. By being able to manipulate multimodal stimulus inputs a sensorimotor illusion is being created, tricking the brain into responses as if it were the natural world and making the subject believe to be 'present' in the virtual environment while providing an egocentric reference frame, or first-person perspective. Presence is one of the key elements establishing a seemingly realistic experience for the user and refers to his subjective psychological response to the VR system and interacting with it: a subjective feeling of 'being there'. In turn, this feeling of presence can be heightened by increasing the level of sensory stimuli, known as increasing the level of immersion, creating a contextually rich scenario for the user to

(6)

move through and interact with (Bowman & McMahan, 2007). As it is thought that people with different dispositions in personality show different immersive tendencies, research by Weibel, Wissmath and Mast (2010) examined the relation between immersion and the Big Five personality domains and showed that the level of immersion correlated positively with an extravert disposition. They also think it likely that extraverts experience more presence when exposed to positive and pleasurable stimuli. As risk propensity is strongly connected to extraversion, it is probable that these risk seekers are prone to experience a strong presence in VR, allowing for a realistic and heartfelt effort and reward representation.

The paradigm of EBDM concerns itself with the way people make action choices based on an integration of action and goal values. Therefore, the cost of an action, such as effort, weighs in on the framing and valuation of prospects and contributes to the decision to act (Kurniawan, Guitart-Masip & Dolan, 2011). While subjects try to obtain a desired reward, the amount of effort that is required gets assessed such that the preference for an action decreases as effort cost

increases. As such, the process of decision making itself can be explained through four steps. First, one recognizes the current situation, or state. Then, evaluation of action options, in terms of how much reward or punishment each potential choice would bring, takes place. Third, one selects an action according to one's needs. And lastly, there is the possibility to reevaluate the action based on the outcome, influencing future effortful decision making. Just like outcome history has a

significant impact on future behavior towards risk propensity (Nicholson et al., 2006), reevaluation makes sure that past effort endeavors will not be in vain.

Even though research in EBDM may have been limited in the realistic approach towards effort and reward representation without the use of VR, past EBDM studies have been examining the reward system thoroughly by looking at the relationship between effort and reward and their effect on the decision making process through fMRI and lesion studies. With the presence of an innate mechanism that weighs effort costs against benefits, prompting to act, it is human and animal

(7)

nature alike to try and maximize gain while minimizing losses. In their research, Walton et al. (2006) found that animals applied additional work in order to receive a larger reward (high effort/high reward) when the other option was receiving a lower reward for expending less effort (low effort/low reward), again influenced by the ACC through dopamine. The importance of dopamine is stressed by Kurniawan et al. (2011) as well, regarding it as a driving force for motivated behaviors towards desired goals, simply called 'wanting' by them. Dopamine, overall, seems a powerful agent when it comes to overcoming action costs and spurring risk seekers into action alike, a notion Treadway et al. (2012) recognize as they found that individual differences in extraversion correlated with dopaminergic function in the ventral striatum. Extraverts showed the willingness to expend greater effort for larger rewards compared to introverts, especially when the probability of receiving that reward was low (the risk seeking element). This interconnected neural network, that greatly underlies risk propensity, seems to constitute a human brain system for the evaluation of costs and benefits in order to make effort-based decisions and propels risk seeking individuals to exert high effort in order to maximize their gains.

In general, psychological research indicates a strong tie between individual differences and EBDM, with traits like risk propensity influencing the way people perpetually weigh costs and benefits as to obtain rewarding outcomes through their choices. VR, then, offers researchers the possibility to study EBDM in a controlled setting while offering ecological validity at the same time and allows for a realistic effort and reward representation as risk seekers are prone to experience presence. Because risk seeking individuals are thought to exert more effort in trying to maximize their gains through dopaminergic pathways in the brain's reward system the present study examines the way people conduct EBDM, influenced by the realism with which effort and reward are

represented, while taking individual differences in risk propensity into account. First, it is hypothesized that realism of effort, realism of reward and a risk seeking nature have a positive effect on presence. Second, realism of reward will have a positive effect on exerted effort, while in

(8)

contrast realism of effort will have a negative effect on exerted effort. And last, risk propensity will have a positive effect on exerted effort: with a larger effect due to realistic reward representation and a smaller effect due to realistic effort representation, when compared to the baseline condition.

Method Participants

A total of 51 Dutch people (19 women and 32 men) took part in this experiment, completing a computer-driven task and questionnaires as part of bachelor thesis-supporting research. Data from 17 participants were excluded: 15 participants were removed from analysis due to ceiling effects in measurements, one did not partake in all three conditions because of nausea and one participant's test-run was not measured all the way through. Finally, 34 participants were included in the analyses (15 women and 19 men) with a mean age of 22 years. Participants had normal or

corrected-to-normal vision (people with astigmatism or farsightedness were not able to participate due to this experiment's usage of VR glasses). Participants did not suffer from epilepsy or other neurological disorders and in general did not experience carousel sickness. 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. The study was approved by the local ethics committee of the University of Amsterdam, and informed consent was obtained from all participants.

Instruments

Computerized task. Stimulus presentation and response registration were constructed using Unreal Engine 4, a suite of integrated tools to design and build games, simulations and

visualizations. Stimuli were presented on a 21-inch monitor (1920 x 1080 pixels). We used virtual reality glasses (Oculus Rift DK2) to immerse participants in VR and allow them to see the VE we

(9)

build with the Unreal Engine. A camera that was fixed upon the computer screen would connect to the VR glasses in order to align the participant correctly (centered) into the VE. The appendices provide screenshots of the different conditions that subjects would see during the task and a simulation of the task can be found here: https://www.youtube.com/watch?v=B87NGwa5jlU

Input Device. A custom-made input device was constructed for this experiment. The goal of this input device is to make participants expand considerable effort and to enhance one’s sense of presence by mimicking the virtual environment (VE). In the VE, participants are driving a mine cart, similar to a human powered handcar. To mimic the motion made by operating such a cart, we chose a bicycle pump as the base of our input device. Operating a bicycle pump (pumping)

resembles the motion one would make operating a handcar. Furthermore, the air resistance felt while operating the bicycle pump makes its employment an effortful activity. In order to make the bicycle pump an appropriate input device for operating on the computer, we attached a strip of aluminum to the handle of the pump. This strip covers the entire length of the pump and since this strip is only attached to the handle it goes up and down, along with the pumping motion. A

computer mouse (Logitech G300) was fixed on the aluminum strip. Consequently, when moving the handle of the bicycle pump up or down, the aluminum strip moves similarly along the fixed

computer mouse. In this manner, the computer mouse can register the motions of the pump.

Reward Modifier. The reward modifier makes sure that for each high effort (HE) choice the difference between track rewards reduces, and likewise, for each low effort (LE) choice the

difference between track rewards grows. For each trial the reward for both tracks is calculated by subtracting the total effort values from both tracks (one for green, two for orange and four for red), multiplying this value with the reward modifier. The outcome is then added to ten for the HE track and subtracted from ten for the LE track. The value of the reward modifier ranges between 0, meaning no difference between rewards, and 1.25, which is the maximum possible reward (20 coins) divided by the maximum possible difference between tracks (16). Each trial starts with a

(10)

reward modifier value of 0.625, which is the maximum reward modifier value divided by two. For each HE choice a value is subtracted from the reward modifier value and for each LE choice a value is added to the reward modifier. The added or subtracted value increases through each consecutive choice of the same effort type: subsequently adding or subtracting 0.02, 0.05, 0.1 and from then 0.2 for a fourth and each next consecutive choice of the same type of effort, so that choice prospects stay balanced. With each switch in effort type this value drops 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.

Point of Indifference. 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 is found to be a reliable method for measuring individual differences in subjective effort exertion (Westbrook, Kester & Braver, 2013). The POI is reached when a subject no longer expresses a preference for the available options. At this point the subject will choose the high effort/high reward (HE/HR) option equally compared to the low effort/low reward (LE/LR) option. The POI may 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 is calculated. When the POI is low, the subject needs less reward to choose the HE/HR option. A higher POI means that the subject needs a higher reward to choose the HE/HR option. When the POI is 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, reward, or both. 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.

(11)

is a diagnostic questionnaire used to measure the experienced sense of presence in a virtual environment. An adapted 13 item IPQ is used and translated to Dutch to fit the current experiment.

Manipulation Check. A five item questionnaire to measure the experienced effect of the three conditions (baseline, realistic reward and realistic effort).

UPPS Impulsive Behavior Scale (UPPS). The UPPS is a diagnostic questionnaire designed by Whiteside and Lynam (2001) to measure impulsivity across dimensions of the Five Factor Model of personality. Consisting of 45 items, I translated the original English inventory to Dutch. The UPPS utilizes four sub-scales as to account for impulsivity: sensation seeking (e.g., 'I would enjoy water skiing'), lack of premeditation (e.g., 'I am a cautious person'), urgency (e.g., 'When I am upset I often act without thinking'), and lack of perseverance (e.g., 'Once I get going on something I hate to stop'). Whiteside, Lynam, Miller and Reynolds (2005) report good convergent validity across assessment method and good discriminant validity from each other, with

alpha reliabilities of .87, .89, .85, and .83 for (lack of) premeditation, urgency, sensation seeking, and (lack of) perseverance, respectively. In addition, Cyders and Smith (2008) report average internal consistencies ranging from .83 to .94 and test-retest reliabilities ranging from .62 to .81 for the four sub-scales in an earlier study performed by them. The items were counterbalanced to avoid acquiescence, with approximately half of the items reverse-scored.

Measures. The scores for the 13 IPQ questions are obtained by scaling the answers on a five-point Likert-type scale (e.g., 1 = not at all, 2 = partially no, 3 = neutral, 4 = partially yes, 5 = very much). The scores for the 45 UPPS questions are obtained by scaling the answers on a four-point Likert scale (e.g., 1 = I agree strongly, 2 = I agree somewhat, 3 = I disagree somewhat, 4 = I disagree strongly).

Procedure

(12)

which the camera was mounted that connected to the VR glasses they were instructed to put on. Calibration of the glasses was performed before the start of each condition, in order to center the subject's field of vision. The participants started with at least one test trial in every condition, or environment, to make sure they understood the amount of effort the different colors represented, knew how to operate the cart and knew where to look for effort, reward and progress information. The practice trials were followed with participants completing three conditions in counterbalanced order, with each condition consisting of 13 trials, all implemented in VR. Participants were

instructed to power a mine cart driving down 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 the representation of different tracks informed participants of the amount of effort a route would require; green sections of the track required no pumping input, whereas orange sections required medium effort and red sections required high effort pumping. In the baseline condition, the different route options and coin rewards (HE/HR or LE/LR) 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 yellow bars. After choosing a route with a click of the mouse, 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. Second, in the realistic reward condition participants were in the same virtual environment. Choices were again represented on two different screens, with rewards now realistically represented as stacks of golden coins on the left and right side of a desk in front of the participant and thus no longer on the computer screens. After choosing, the coins of the chosen route would fly into a 'treasure' chest in front of the participant. Participants would then drive the cart the same way as in the baseline condition, and saw their progress in a third centered screen. Last, in the realistic effort condition, participants found themselves in a mine cart inside a room with two screens displaying the different route options and abstract coins as yellow bars. After

(13)

selecting a route, a large door would open and they would see themselves driving down a track through a natural landscape, using the same bicycle pump that they now saw integrated into their cart. Some sections of the outside tracks were overgrown to represent the required amount of effort, with either grass (medium effort) or shrubs (large effort), or would either remain flat (no effort).

Statistical procedure

The hypotheses were tested by using a one-way repeated measures ANOVA and

subsequently a mixed ANOVA, enabling to see if there was an effect of realism representation and individual differences in risk propensity in the first place and secondly in what direction they would point. To test whether participants would experience a sense of presence while being immersed in a VE and see if risk propensity would influence this sense of presence, a one-way repeated measures ANOVA was used, analyzing if a realistic effort/reward representation, and a risk seeking nature would positively effect presence. A mixed ANOVA was used to test if (a) the realism of reward would have a positive effect on exerted effort, (b) the realism of effort would have a negative effect on the exerted effort, and (c) risk propensity would have a positive effect on exerted effort, with a larger effect due to realistic reward representation and a smaller effect due to realistic effort representation when compared to the baseline.

Results

The Effect of Realism in Effort/Reward Representation, and Risk Propensity on Presence To test whether a realistic effort/reward representation or risk seeking nature would have a positive effect on presence a one-way repeated measures ANOVA was used, with six participants being excluded due to either not completing the IPQ or failing to complete all three experimental conditions due to nausea. Results of the test show that the realism with which effort/reward were represented had a positive effect on the sense of presence that people experienced (F(1.44, 63.38) =

(14)

15.00, p < .001). When comparing the two experimental conditions to the baseline condition, an effect of the realistic effort condition could be found (F(1, 44) = 20.60, p < .001), in contrast to the realistic reward condition, in which no significant effect could be found (F(1, 44) = 3.54, p = .067), as is shown in Figure 1. Table 1 presents descriptive statistics of the within-subject variance in presence over the three conditions. These results indicate that the VE that was constructed to carry out this experiment succeeded in immersing participants in VR, but is possibly more convincing in the realistic effort condition due to the richness in ecological validity.

In addition to these findings, a main effect of risk propensity on a sense of presence was found (F(1.213, 14.552) = 4.90, p = .038). A risk seeking nature seems to make participants more prone to experience a sense of presence in the VE, even though the amount of risk seekers was reduced to almost a third of the total number of people partaking in the experiment (see Table 2 and Figure 2).

Figure 1. Sense of presence in VR. Presentation of the mean scores & standard errors on the IPQ between conditions (B = Baseline, RR = Realistic Reward & RE =

Realistic Reward). 36 37 38 39 40 41 42

IPQ scores

B RR RE

(15)

Table 1.

Descriptive statistics of presence measured by IPQ

Condition N Mean Std. Deviation

Baseline 45 38.00 4.61

Realistic Reward 45 38.69 4.96

Realistic Effort 45 41.11 4.64

Table 2.

Descriptive statistics presence of risk seekers through IPQ

Condition N Mean Std. Deviation

Baseline 13 39.62 4.81

Realistic Reward 13 40.31 5.30

Realistic Effort 13 42.23 4.85

Figure 2. Risk seekers' sense of presence in VR. Presentation of the mean scores & standard errors on the IPQ between conditions (B = Baseline, RR = Realistic Reward & RE =

Realistic Reward). B RR RE 36 37 38 39 40 41 42 43 44 IPQ scores

(16)

The Effect of Realistic Effort/Reward Representation on Effort Exertion

To test whether the realism with which both effort and reward were represented would effect participants' exerted effort a factorial mixed ANOVA was used, with 17 participants being excluded due to ceiling effects in measurements. Results of the factorial mixed ANOVA show that, ultimately, no significant effect of the conditions on the amount of exerted effort was found (F(2, 64) = 0.55, p = .579). The results indicate that a realistic effort representation positively affects perceived effort exertion, while a realistic reward representation negatively affects perceived effort exertion, however, because of the absence of a significant effect in this experiment these results provide no evidence for the posed hypothesis.

The Effect of Risk Propensity on Effort Exertion

Second, the effect of risk propensity was tested by using a factorial mixed ANOVA, in which no significant effect could be found: individual differences in risk propensity, namely a risk seeking personality, did not significantly effect the amount of exerted effort (F(1, 32) = 0.05, p = . 830). In addition, no interaction effect could be found between the three conditions (realistic effort representation, realistic reward representation and baseline) and risk propensity (F(2, 64) = 1.83, p = .169), which could be assumed due to the previous insignificant effects found in the before mentioned result section.

Discussion

The current study examined how the realism of effort and reward representation would influence effort-based decision making, while taking the effects of risk propensity into account. It was first hypothesized that realism of effort, realism of reward and a risk seeking nature would have a positive effect on presence, which was found to be true, mainly due to the effects of a risk seeking nature and realism of effort, as the results discounted a positive effect of realism of reward. Second,

(17)

it was thought that realism of reward would have a positive effect on exerted effort, while in

contrast realism of effort would have a negative effect on exerted effort. However, no evidence was found to support this claim. And last, it was proposed that risk propensity would have a positive effect on exerted effort: with a larger effect due to realistic reward representation and a smaller effect due to realistic effort representation, when compared to the baseline condition. This hypothesis could not be validated by the present study due to a lack of significant evidence.

Even though no substantial support was found for the larger part of the presented

hypotheses, the use of VR seems to hold a certain value for psychological research, as the indication is that a realistic representation of prospects allows for higher ecological validity and

generalizability, in accordance to earlier research by Bohil, Alicea and Biocca (2011). As a sense of presence is needed to establish a seemingly realistic experience for the user in a VE, this research supports the fact that the use of a VR device could benefit EBDM research by proper construct presentation. As Weibel, Wissmath and Mast (2010) found that different dispositions in personality lead to different immersive tendencies, VR-driven experiments could also prove to be rich research devices for the field of trait psychology in understanding the differences between personality domains. Because of the strong connection between risk propensity and extraversion it is probable that extraverts, like risk seekers in the current study, experience a stronger sense of presence in VR.

The realism of reward did not, however, affect a sense of presence as effectively as the realism of effort did, which could be subscribed to an insufficient contextually rich scenario, decreasing the level of immersion compared to the latter condition. The realistic effort condition provided participants with a greater amount of multimodal stimuli by expanding their surrounding environment and adding a more interactive element to effort exertion, which seemingly heightened users' feeling of presence, as was the case with Bowman and McMahan (2007). This stresses that VR in its early stage, although promising with regards to future psychological research, is subject to limitations in multimodal sensory stimulation, which shows the importance of manipulation checks

(18)

in a technologically new endeavor.

As this research is largely inconclusive regarding the role of risk propensity in EBDM it could be useful to view the results in the light of prospect theory (Kahneman & Tversky, 1979). First, prospect theory refutes the supposition of a rationality of economic agents, underlining the impulsive nature of decision making, affected by framing effects and people being unable to apply linear decision weights (Kahneman & Tversky, 1992). They also mention that the interpretations of risk propensity towards decision making are varied and not conclusive, urging us to take the dual nature of risk taking into account, being both general and domain-specific. Kahneman and Tversky (1992) themselves value theories of choice to be approximate and incomplete at best, as decision making is a constructive and contingent process. When faced with a complex problem, people employ heuristics in order to simplify the representation and evaluation of prospects. Supposedly, these heuristics of choice do not readily lend themselves to formal analysis because their application depends on formulation, context of choice and method of prospect evocation. These facts exemplify why it is hard to do research in the field of decision making and risk propensity alike; in trying to achieve an even controlled experimental operationalization, the possibility of multiple confounding factors, dependent on people's subjective interpretation through the framing and valuation phase, could be what make it difficult to accomplish this.

Practically, possible limitations are probably due to the uniformity of the reference group and overall ceiling effects. As we encountered an amount of scores at the upper level, the

occurrence of ceiling effects restricts the variance in realism of effort/reward representation in the dataset and with that their effect on effort exertion. This reduction in sensitivity may have led to this experiment not detecting an effect of EBDM on effort exertion. Second, a problem with measuring risk propensity lay in the fact that because of low maximum scores on the UPPS, risk seeking individuals were perhaps not properly represented in the subject pool. The insufficient diversity in risk propensity between subjects could be explained by the uniformity of the sample, which largely

(19)

consisted of students and people sharing a similar age. As Nicholson et al. (2006) found that risk propensity is inversely related to age, a larger discrepancy in age within participants could benefit the diversity in risk propensity occurrence. In addition, prescreening participants on risk propensity before they are allowed to enter research could contribute in obtaining a reference group that is 50 percent risk seeking and 50 percent risk averse.

Future studies on EBDM specifically, but also psychological research in general, may benefit from utilizing VR as it seems promising in improving ecological validity while maintaining experimental control. Because the implementation of VR has only just taken hold, an important part to focus on is how to correctly translate constructs into virtual counterparts as to produce significant results for researchers to work with.

While this study started out with trying to examine the relationship between reward and perceived effort and its dependance upon the realism with which they are presented, all the while taking risk propensity into account, eventually it became more a case study for the use of VR in psychological research. And though the present results are not conclusive on the effects of risk propensity within EBDM, this experiment is yet another indication that risk seekers, or impulsive people, are inclined to have high immersive tendencies, which underlines that the utilization of VR in psychological research can be of great significance in future studies on personality.

References

Assadi, S. M., Yücel, M., & Pantelis, C. (2009). Dopamine modulates neural networks involved in effort-based decision-making. Neuroscience and Biobehavioral Reviews, 33, 383-393. Beauducel, A., Brocke, B., & Leue, A. (2006). Energetical bases of extraversion: Effort, arousal,

eeg, and performance. International Journal of Psychophysiology, 62, 212-223.

Bohil, C. J., Alicea, B., & Biocca, F. A. (2011). Virtual reality in neuroscience research and therapy.

(20)

Bowman, D. A., & McMahan, R. P. (2007). Virtual reality: How much immersion is enough?

Computer, 40(7), 36-43.

Croxson, P. L., Walton, M. E., O'Reilly, J. X., Behrens, T. E. J., & Rushworth, M. F. S. (2009). Effort-based cost-benefit valuation and the human brain. The Journal of Neuroscience,

29(14), 4531-4541.

Cyders, M. A., & Smith, G. T. (2008). Clarifying the role of personality dispositions in risk for increased gambling behavior. Personality and Individual Differences, 45, 503-508. Doya, K. (2008). Modulators of decision making. Nature Neuroscience, 11(4), 410-416.

Eysenck, S. B. G., & Eysenck, H. J. (1977). The place of impulsiveness in a dimensional system of personality description. British Journal of Social and Clinical Psychology, 16(1), 57-68. Kahneman, D., & Lovallo, D. (1993). Timid choices and bold forecasts: A cognitive perspective on

risk taking. Management Science, 39(1), 17-31.

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk.

Econometrica, 47(2), 263-292.

Kahneman, D., & Tversky, A. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297-323.

Kurniawan, I. T., Guitart-Masip, M., & Dolan, R. J. (2011). Dopamine and effort-based decision making. Frontiers in neuroscience, 5.

Lauriola, M., Panno, A., Levin, I. P., & Lejuez, C. W. (2014). Individual differences in risky decision making: A meta-analysis of sensation seeking and impulsivity with the balloon analogue risk task. Journal of Behavioral Decision Making, 27, 20-36.

Levin, C., & Coburn, T. (2011). Wall Street and the financial crisis: Anatomy of a financial collapse. Majority and Minority Staff Report, Permanent Subcommittee on Investigations, United States Senate.

(21)

domain-specific risk taking. Journal of Risk Research, 8(2), 157-176.

Schubert, T., Friedmann, F., & Regenbrecht, H. (2001). The experience of presence: Factor analytic insights. Presence, 10(3), 266-281.

Treadway, M. T., Buckholtz, J. W., Cowan, R. L., Woodward, N. D., Li, R., Ansari, M. S., et al. (2012). Dopaminergic mechanisms of individual differences in human effort-based decision-making. The Journal of Neuroscience, 32(18), 6170-6176.

Walton, M. E., Bannerman, D. M., & Rushworth, M. F. S. (2002). The role of rat medial frontal cortex in effort-based decision making. The Journal of Neuroscience, 22(24), 10996-11003. Walton, M. E., Kennerley, S. W., Bannerman, D. M., Phillips, P. E. M., & Rushworth, M. F. S.

(2006). Weighing up the benefits of work: Behavioral and neural analyses of effort-related decision making. Neural Networks, 19, 1302-1314.

Weibel, D., Wissmath, B., & Mast, F. W. (2010). Immersion in mediated environments: The role of personality traits. Cyberpsychology, Behavior, and Social Networking, 13(3), 251-256. Westbrook, A., Kester, D., & Braver, T. S. (2013). What is the subjective cost of cognitive effort?

Load, trait, and aging effects revealed by economic preference. PLOS ONE, 8(7), Article e68210. Retrieved May 4, 2016, from http://journals.plos.org/plosone/article/asset? id=10.1371%2Fjournal.pone.0068210.PDF

Whiteside, S. P., & Lynam, D. R. (2001). The five factor model and impulsivity: Using a structural model of personality to understand impulsivity. Personality and Individual

Differences, 30, 669-689.

Whiteside, S. P., Lynam, D. R., Miller, J. D., & Reynolds, S. K. (2005). Validation of the UPPS impulsive behaviour scale: A four-factor model of impulsivity. European Journal of Personality, 19, 559-574.

Wittmann, M., & Paulus, M. P. (2007). Decision making, impulsivity and time perception. TRENDS in Cognitive Sciences, 12(1), 7-12.

(22)

Zuckerman, M., & Kuhlman, D. M. (2000). Personality and risk taking: Common biosocial factors. Journal of Personality, 68(6), 999-1029.

(23)

(24)

(25)

(26)

(27)

UPPS

Hieronder staan een aantal uitspraken die beschrijven wat mensen doen en denken. Geef bij elke uitspraak aan in welke mate u het eens of oneens bent met de uitspraak. Als u het

Zeer Eens bent omcirkel dan 1, als u het Enigszins Eens bent omcirkel dan 2, als u het Enigszins Oneens bent omcirkel dan 3, en als u het Zeer Oneens bent omcirkel dan 4.

Zeer Enigszins Enigszins Zeer Eens Eens Oneens Oneens

1. Ik heb een gereserveerde en voorzichtige houding t.o.v. het leven. 2. Ik heb moeite met het controleren van

mijn impulsen.

3. Ik ben over het algemeen op zoek naar nieuwe en spannende

ervaringen en gevoelens.

4. Ik vind het prettig om eenmaal

begonnen taken af te maken. 5. Mijn manier van denken is vaak voorzichtig en doelbewust.

6. Verleidingen kan ik moeilijk weerstaan (eten, sigaretten, etc.).

7. Ik probeer dingen in ieder geval één keer.

8. Ik heb de neiging om snel op te geven.

9. Ik ben niet iemand die zomaar iets zegt zonder erbij na te denken.

10. Ik kom vaak in situaties terecht waar ik liever niet in had willen zitten.

11. Ik hou van sporten en spellen waarin je snel moet handelen.

12. Niet afgemaakte taken irriteren mij.

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

(28)

13. Ik denk liever even na voordat ik handel.

14. Wanneer ik me slecht voel, doe ik vaak dingen die me op het moment zelf goed doen, maar waar ik later spijt van heb.

15. Ik zou het leuk vinden om te waterskiën.

16. Als ik eenmaal bezig ben met iets dan vind ik het niet fijn om te stoppen. 17. Ik hou er niet van om met een project

te beginnen, als ik nog niet precies weet wat er van mij verwacht wordt. 18. Soms kan ik niet stoppen met iets te

doen, ook al zorgt dat ervoor dat ik mij alleen maar slechter ga voelen.

19. Ik hou ervan risico te nemen.

20. Ik kan mij gemakkelijk concentreren. 21. Ik zou het leuk vinden om parachute te springen.

22. Ik maak af wat ik begin.

23. Ik ben meestal rationeel en verstandig in mijn doen en laten.

24. Als ik ontdaan ben handel ik meestal zonder erbij na te denken.

25. Ik verwelkom nieuwe en spannende ervaringen en gevoelens, zelfs als ze een beetje eng of ongewoon zijn. 26. Ik ben goed in het op tijd afmaken van zaken.

27. Ik probeer meestal goed en logisch na te denken om tot oplossingen te komen. 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

(29)

29. Ik zou graag een vliegtuig leren besturen.

30. Ik ben iemand die zaken voor elkaar krijgt.

31. Ik ben een voorzichtig persoon.

32. Het is voor mij moeilijk om niet achter mijn gevoel aan te gaan.

33. Ik hou er soms van om enge dingen te doen.

34. Ik maak bijna altijd taken af die ik begonnen ben.

35. Voordat ik met iets begin wil ik weten wat er van mij verwacht wordt.

36. Vaak maak ik de dingen erger, omdat ik handel zonder erbij na te denken wanneer ik ontdaan ben.

37. Ik zou het leuk vinden om hard van een steile berg af te skiën.

38. Soms moet ik zoveel kleine dingen doen dat ik uiteindelijk niets doe.

39. Ik denk meestal zorgvuldig na voordat ik iets doe.

40. Voordat ik een beslissing neem, weeg ik eerst alle voor- en nadelen tegen elkaar af.

41. In een heftig gesprek zeg ik vaak dingen waar ik later spijt van heb. 42. Ik zou graag eens diepzeeduiken. 43. Ik hou mijn gevoelens altijd in toom. 44. Ik hou ervan om hard te rijden (auto,

motor, etc.).

45. Soms doe ik impulsieve dingen waar ik later spijt van krijg.

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

(30)

Score instructies

De volgende vragen (per sub-schaal aangegeven) zijn contra-indicatief gescoord: Urgentie items 2, 6, 10, 14, 18, 24, 28, 32, 36, 41, 45 Sensatie zoekend items 3, 7, 11, 15, 19, 21, 25, 29, 33, 37, 42, 44

(tekort aan) Doortastendheid

(31)

27-06-16 17:53 dVragenlijsten

dVragenlijsten

Je krijgt straks een vragenlijst voorgelegd over je ervaring tijdens de verschillende taken (2D, 3D & Virtual Reality). Geef bij elke vraag voor elke taak een antwoord op de vraag die het beste bij jouw ervaring past.

*Vereist

1. Proefpersoonnummer *

2. 1. Ik had het gevoel dat ik hard moest werken tijdens ... * Markeer slechts één ovaal per rij.

Helemaal niet Grotendeels niet Een beetje niet

Neutraal _{beetje Grotendeels}Een Heel_erg

het rijdende mijnkarretje. het stilstaande mijnkarretje. het stilstaande mijnkarretje met geldkistje.

3. 2. Ik vond de beloning aantrekkelijk tijdens ... * Markeer slechts één ovaal per rij.

het rijdende mijnkarretje. het stilstaande mijnkarretje. het stilstaande mijnkarretje met geldkistje.

Igroup Presence Questionnaire (IPQ)

4. 1. Ik had het gevoel dat de virtuele wereld 'echt' was tijdens... * Markeer slechts één ovaal per rij.

Helemaal niet Gedeeltelijk niet Neutraal Gedeeltelijk wel Helemaal wel

... het rijdende mijnkarretje. ... het stilstaande mijnkarretje. ... het stilstaande mijnkarretje met geldkistje.

5. 2. Ik had het gevoel opgenomen te zijn in de virtuele wereld tijdens ... * Markeer slechts één ovaal per rij.

(32)

6. 3. Ik had het gevoel slechts plaatjes te aanschouwen tijdens ... * Markeer slechts één ovaal per rij.

7. 4. Ik had niet het gevoel in een virtuele omgeving aanwezig te zijn tijdens ... * Markeer slechts één ovaal per rij.

8. 5. Ik had meer het gevoel bezig te zijn in de virtuele wereld, dan dat ik het gevoel had iets van

buitenaf te bedienen tijdens ... * Markeer slechts één ovaal per rij.

9. 6. Hoe bewust was u zich van de echte omgeving (bv. geluiden van buiten, kamertemperatuur),

terwijl u bezig was met de taak tijdens ... * Markeer slechts één ovaal per rij.

10. 7. Ik was me niet bewust van mijn echte omgeving tijdens ... * Markeer slechts één ovaal per rij.

Helemaal

niet Gedeeltelijkniet Neutraal Gedeeltelijkwel Helemaalwel

11. 8. Ik lette nog op de echte omgeving tijdens ... * Markeer slechts één ovaal per rij.

(33)

12. 9. Ik ging volledig op in de virtuele wereld tijdens ... * Markeer slechts één ovaal per rij.

13. 10. Hoe echt kwam de virtuele omgeving op u over tijdens ... * Markeer slechts één ovaal per rij.

14. 11. In hoeverre kwam uw ervaring in de virtuele omgeving overeen met uw ervaringen in de

echte wereld tijdens ... * Markeer slechts één ovaal per rij.

15. 12. Hoe werkelijk kwam de virtuele wereld op u over tijdens ... * Markeer slechts één ovaal per rij.

16. 13. De virtuele wereld kwam echter op mij over dan de werkelijke wereld tijdens ... * Markeer slechts één ovaal per rij.

17. Geef hieronder voor elke taak aan hoe inspannend je deze hebt ervaren.

Markeer slechts één ovaal per rij.

0. Geen inspanning 0.5 Zeer lichte inspanning 1. Lichte inspanning 2. Enige inspanning 3. Gemiddelde inspanning 4. Een beetje zwaar 5. Zwaar 6. 7.Erg zwaar 8. 9. 10. Extreem zwaar (maximaal) Het rijdende mijnkarretje. Het stilstaande mijnkarretje. Het stilstaande mijnkarretje met geldkistje.

(34)

18. Geef hieronder voor elke taak aan hoe veel je je best hebt gedaan. * Markeer slechts één ovaal per rij.

Heel weinig Weinig Niet zo veel Gemiddeld Redelijk veel Veel Heel veel

Tijdens het rijdende mijnkarretje.

Tijdens het stilstaande mijnkarretje.

Tijdens het stilstaande mijnkarretje met geldkistje.

19. De afstand die ik heb moeten afleggen voor deze keuze optie voelde tijdens het rijdende

mijnkarretje ... *

Markeer slechts één ovaal.

veel langer dan tijdens het stilstaande mijnkarretje. langer dan tijdens het stilstaande mijnkarretje. even lang als tijdens het stilstaande mijnkarretje. korter dan tijdens het stilstaande mijnkarretje. veel korter dan tijdens het stilstaande mijnkarretje. 20. 1. Geef hieronder aan hoe leuk je elke taak vond. *

Het rijdende mijnkarretje. Het stilstaande mijnkarretje. Het stilstaande mijnkarretje met geldkistje.

21. 2. In welke mate vond u het jammer dat de taak voorbij was?

22. 3. In welke mate heeft u zichzelf vermaakt tijdens het uitvoeren van de taak? * Markeer slechts één ovaal per rij.

(35)

Mogelijk gemaakt door

23. 4. In hoeverre zou u het leuk vinden om deze ervaring nogmaals te beleven? * Markeer slechts één ovaal per rij.

24. 5. In welke mate vond u de ervaring van zonet interessant? * Markeer slechts één ovaal per rij.

25. Ervaarde u door toedoen van de taak één of meer van de volgende gevoelens? Zo ja, in welke

welke mate was dat het geval? * Markeer slechts één ovaal per rij.

Niet Zwak Gemiddeld Sterk

Hoofdpijn Misselijkheid Zweten Ongemakkelijk gevoel Duizeligheid Desoriëntatie Benauwd

26. Had u een specifieke strategie bij het maken van de keuze voor een track? Zo ja, welke?

n.b. had u in uw gedachte specifieke grenzen gesteld (van moeite of beloning) waarop u bepaalde keuzes maakte?