The Influence of Virtual Reality on Perceived Effort and Decision Making.
Terrence Geisterfer
Studentnumber: 10187294 University of Amsterdam
Abstract
In the present study we investigated the influence of Virtual Reality on perceived effort and decision making. We also looked at the role of Dopamine and Anhedonia in decision making. We did this to come to a better understanding of decision making in everyday life and improve the ecological validity in research. Forty-nine participants were recruited who all participated in 3 conditions; the two-dimensional-, three-dimensional- and virtual-reality condition. Within all 3 conditions
participants were asked to perform a decision task. We further investigated sense of presence using the IPQ, Dopamine-levels using a Dutch translation of the listening span and degree of Anhedonia using the TEPS-questionnaire. Going against our hypothesis, results showed Virtual Reality did not affect perceived effort and decision making. Moreover, Dopamine and Anhedonia did not play a role in decision making. We did find an effect of condition on sense of presence. The latter offers
Introduction
Whether we are aware of it or not, in everyday life we make decisions throughout the whole day. Decision making can be seen as an essential quality for both humans and all other living creatures. One simple decision may have big consequences. Human decision making is influenced by many aspects. One of these aspects is the degree of effort needed to achieve the desired result of a certain decision. Of course, when in the need of making a decision, one should obviously think you would choose in a way of getting the highest reward possible for the decision in dispute. However, when making a decision, a certain amount of effort is necessary to reach the desired output of the decision. Let’s take a simple example. For instance, you could be working at home behind your computer and start feeling a bit hungry. At first you are not really willing to make something to eat, nonetheless your appetite keeps increasing so after a while you decide you will get yourself
something to eat. All the same you are still not really willing to make yourself something to eat so now you have to decide; you can choose to get yourself an easy pre-made snack (low effort) and thereby satisfy your appetite for a short period of time (low reward), you weren’t really willing to get yourself something to eat after all. On the other hand you can choose to make yourself a bigger meal (high effort) and thereby satisfy your appetite for a longer period of time (high reward). This event where one makes the consideration between low effort and low reward or high effort and high reward is also called effort based decision making (EBDM).
According to the prospect theory deciding what to choose is distinguished in two phases: 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’’ (Tversky & Kahneman, 1992). So we first investigate the possible options to choose when we make a decision, and give the possible outcomes a certain value thereafter. How we assign values to a certain outcome can be influenced by many factors.
amount of research is done to do achieve this. Research showed that EBDM can be influenced by many aspects. EBDM can be influenced by personality (Holt, Green & Myerson, 2003), Depression (Treadway, Buckholtz, Schwartzman, Lambert & Zald, 2009), and many mental disorders (Rahman, Sahakian, Cardinal, Rogers & Robbins, 2001). One of these mental disorders is Anhedonia. Anhedonia is described as the loss of pleasure or lack of reactivity to pleasurable stimuli. More and more
research showed the role of Anhedonia in EBDM. Subjects with a high degree of Anhedonia show less interest in high effort and are also less willing to wait for reward (Treadway, Bossaller, Shelton & Zald, 2012). Furthermore, an increasingly amount of research revealed the importance of Dopamine in EBDM. Floresco, Maric & Ghods-Sharifi, (2008) showed that decreasing Dopamine transmission in rats by blocking Dopamine receptors using the Dopamine antagonist Flupenthixol decreases the amount of effort showed to obtain a larger reward. Moreover, research indicated that Dopamine levels in the ACC(Anterior Cingulate Cortex), the Insula and the Ventral Striatum play an important role in EBDM, by which higher activity in these areas result in higher effort (Croxson, Walton, O'Reilly, Behrens & Rushworth, 2009).
Even though there has been an increasingly amount of research done to investigate EBDM and the role of Dopamine and Anhedonia in EBDM in humans, this kind of research, and all other psychological research, is mainly performed in a controlled lab-setting whereby participants generally perform a simple 2D task on a computer screen. Doing so, researchers try to simulate a real life situation in order to generalize their results to the entire population in the actual environment. This isn’t necessarily a wrong method. However, one could imagine that obtaining data from a more natural setting would produce more realistic results and thereby give the opportunity to come to a better understanding of psychological events, such as EBDM.
It is for that reason why we, in the present study, focus on the influence of Virtual Reality on perceived effort and decision making. Virtual Reality is a computer-simulated environment that can simulate physical presence in places in the real world or imagined world. By presence we mean the subjective experience that one feels as if he or she is really in the virtually created setting even
though it is not a real world. In this research, subjects will be asked to participate in a decision making task in 3 different conditions; 2D, 3D and Virtual Reality. The 3D condition will, like the 2D condition, be done looking at a computer screen. However, in the 3D condition participants will see a real life-like environment on the screen similar to the environment which is shown in the VR
condition. While in the 2D condition, a black background will be shown. We will then check if perceived effort differs between the 3 conditions. We will furthermore check to which degree Dopamine and Anhedonia play a certain role in decision making in Virtual Reality. The degree of Dopamine-levels will be estimated using a Dutch version of the Salt-House listening span task (Vos, Gunter, Kolk & Mulder, 2001). The Salt-House listening span is created to measure working memory capacity. However, working memory capacity is shown to strongly correlate with striatal Dopamine-levels (Cools, Gibbs, Miyakawa, Jagust & D'Esposito, 2008). Anhedonia will be measured using The Temporal Experience of Pleasure Scale (TEPS)(Gard, Gard, Kring & John, 2006). Which is an 18-item questionnaire.
If we are able to create a virtual setting in which participants experience a high degree of presence, we can produce more realistic data and thereby come to a better understanding of EBDM in real life. A high degree of presence has to be reached because when sense op presence increases, along with it will increase the effect of recreating a natural setting in Virtual Reality. To which degree one feels a sense of presence in Virtual Reality differs much and is influenced by many factors. Individual differences show to have a significant influence on sense of presence in Virtual Reality (Sacau, Laarni & Hartmann, 2008). So does locus of control (Murray, Fox & Pettifer, 2007), Mental health (Huang & Alessi, 1999), attachement (Wallach, Safir & Almog, 2009), the presence of real objects in the Virtual Reality (Lok, Naik, Whitton & Brooks Jr, 2003), and many other factors.
If it is true that according to the prospect theory we make decisions by investigating the possible options to choose when we make a decision, and give the possible outcomes a certain value thereafter, one could expect that subjects valuate outcomes differently when exposed to decision making in different settings. We therefore expect that the perceived reward of a certain degree of
effort in a VR-setting will be lower than in other conditions because we hypothesize that subjects will feel more present in VR. We also expect that perceived effort will be higher in VR-condition than in 3D-condition and 2D-condition since the effort feels realer because there is a higher sense of
presence in VR when compared to the other conditions. We further predict that perceived effort will be higher in 3D-condition when compared to the 2D-condition since we hypothesize that sense of presence will be higher in 3D-condition when compared to the 2D-condition. Furthermore, since previous research has shown that high Dopamine-levels result in choosing high effort more often and that a higher degree of Anhedonia results in low effort more often, one could expect to find similar results in the present study. However, we hypothesized that the role of Dopamine-levels and Anhedonia in EBDM would be more present in 3D than in 2D and more present in VR than in 3D. We do so because 3D seems realer to participants than 2D and VR seems realer to participants than 3D, since the sense of presence is higher in 3D than 2D and higher in VR than 3D. If, in real life, Dopamine and Anhedonia play a role in EBDM, one would expect that these effects of Dopamine and
Anhedonia are also most present in real life settings.
Methods
Participants
The participants of this experiment were recruited through subscription on lab.uva.nl where the experiment was advertised. Participants were also recruited by addressing them on the streets. The participants were informed about the content en procedure of the experiment and participation was completely voluntarily. A written informed consent was signed and obtained by all participants before participating. All the participants were assigned to all three conditions. Our sample consisted of 7 men and 19 women (Mean age = 22.96 years. SD = 2.62).The participants received either a monetary compensation (€20,-), research credits for participating in the experiment or participated voluntarily. The ethics committee of the University of Amsterdam approved the experiment. To be
included in the experiment participants had to be healthy and between 18 and 28 years of age. Participants with epilepsy, other neurological deficits, heart diseases, high sensitivity for dizziness or nausea in carousels or rollercoasters, astigmatism or presbyopia were excluded from the experiment. Participants with myopia or lenses were able to take part in the experiment
Materials
During the experiment we examined many variables using the following questionnaires; The NEO-FFI to check for personality (Costa & McCrae, 1989), the STAI-T to check participants degree of anxiety (Spielberger, Sydeman, Owen & Marsh, 1999), In addition to the STAI-T the ASI was also used to check participants degree of anxiety (Peterson & Heilbronner, 1987), the BIS-BAS to check for individual differences in the sensitivity for behavioral approach- and behavioral inhibition systems (Carver & White, 1994), the Barratt Impulsiveness Scale to check for degree of impulsiveness (Patton, Stanford & Barratt, 1995), the BDI-II to check participants degree of depression (Beck et al., 1996), the IPQ to check for sense of presence(Schubert, Friedmann & Regenbrecht, 2001) and the SQUASH to evaluate physical strength (Wendel-Vos, Schuita, Sarisc & Kroumhout, 2003). As noted before, individual differences in working memory capacity was used as a proxy for Dopamine-levels. Working memory capacity was tested using a Dutch version of the Salt-House listening span task (Vos, Gunter, Kolk & Mulder, 2001). The participants had to listen to a set of sentences. At the end of a set,
participants were asked to recall the last word of the sentences in that particular set and also answer questions about the content of that same set of sentences. As the listening span proceeded, the number of sentences per set increased. Throughout the listening span the task looks as the following. At the beginning of the task a 3 different sets of 1 sentence is presented and the questions about that particular sets are asked, following with 3 sets of 2 sentences, increasing after every 3 sets with one sentence until the final 3 sets of 7 sentences is reached. As noted above, at the end of a set, participants were asked to recall the last word of the sentences in that particular set and also answer questions about the content of that same set of sentences. The final score on the task is increased by
1/3 point when all the answers in a set are correctly answered and the last words of the given sentences are written down correctly. A high score on the listening span will most probably with a higher working memory capacity. To check for participants’ degree of Anhedonia the Temporal Experience of Pleasure Scale (TEPS) was used (Gard, Gard, Kring & John, 2006). The TEPS is an 18-item questionnaire specifically designed to measure anticipatory and consummatory facets of pleasure. Every question was presented with 1 to 6 Likert-scale going from 1.: Entirely incorrect for me, to 6.: Entirely correct for me. Participants were asked to tick the boxes that represented how they felt about the given question. A lower score on this questionnaire represents a higher degree of Anhedonia. The Oculus Rift DK2 was used to test in a Virtual Reality setting (Oculus VR. (2014). Oculus DK2. Retrieved from https://www.oculus.com/dk2/).
Procedure
During the experiment participants were asked to fill in the given questionnaires for approximately 1 hour (Block 1) and to complete the three decision tasks for approximately 1 hour (Block 2). The order of the tasks was randomized between participants. Whereas half of the participants first fill in the questionnaires and second participated in the decision tasks, and the other half of the participants did this in opposite ways. Also, the order of the questionnaires was randomized for each participant. During block 2 participants had to perform 3 decision tasks. All 3 tasks were developed in Unreal Engine 4 (Epic Games. (2012). Unreal Engine 4. Retrieved from
https://www.unrealengine.com/unreal-engine-4). The 3 tasks consisted of a two-dimensional task (Figure 1), a three-dimensional task (Figure 2) and a head-mounted Virtual Reality task (Figure 2*).
Figure 1. A presentation of the two-dimensional task presented on a computer screen.
Figure 2. A presentation of the three-dimensional task presented on a computer screen.
*The VR condition and the 3D condition were identical. However, in the VR condition the task was presented on the Oculus Rift DK2, not on a computer screen.
The tasks were conceptually identical. The start of every trial offered 2 choice alternatives. Participants could either choose low effort or high effort with corresponding degrees of reward. Effort was schematically displayed as five different colored rectangles with the colors green, orange and red respectively representing low, moderate and high effort. Reward was displayed as a number on the computer screen. High and low effort differed in degree of effort needed to complete the trial and physical length, but the amount of time needed to complete the trial wasn’t influenced by high or low effort. Therefore, the time needed to complete a trial was always identical. Participants were told so in advance. As such, the tradeoff between high or low effort should only be influenced by the amount of reward received by the corresponding degree of effort. Via a staircasing procedure (Tversky & Kahneman, 1992) the amount of reward per unit effort was adapted throughout the task. A built-in valuation function constantly updated the reward per unit effort. As a result, choosing for low effort in one trial would cause higher reward for high effort and lower reward for low effort in the following trial. Vice versa, choosing high effort in one trial would cause lower reward for high effort and higher reward for low effort the following trial. This method should prevent floor- and ceiling effects, but even more important make it able to identify a ‘’point of indifference’’. It is at this point were one who consecutively chooses high effort switches to low effort and one who
Figure 3. Schematical presentation of the Point of Indifference.
By this way we were able to investigate how valuable participants considered their own effort. Once the participants made their decision, they were instructed to move the thumbsticks of an Xbox-One controller (Microsoft. (2013). Xbox One Wireless Controller. Retrieved from www.xbox.com/en-US/xbox-one/accessoires/controllers/wireless-controller.) repeatedly back and forth in opposite motion. The way to do this was by making two fists and letting those fists rest on the thumbsticks while keeping your arms levitating in the air, as if the participant was holding two joysticks. A power-bar was presented on the screen which had to be hold above a certain point by moving the
thumbsticks back and forth. The thumbsticks had to be moved more often for high effort and less often for low effort. After completing the 3 tasks, participants were asked to fill in the IPQ.
Data analysis
To check if there is a difference in mean point of indifference between the 3 conditions we conducted a repeated-measures ANOVA. We similarly conducted a repeated-measures ANOVA to check if there is a difference in mean sense of presence between the 3 conditions. To check to which degree Dopamine-levels played a role in EBDM we calculated Pearson’s correlation coefficient between subjects score on the listening span and degree of effort shown in 2D, 3D and VR. Finally, we calculated Pearson’s correlation coefficient between subjects score on the TEPS-questionnaire and degree of effort shown in 2D, 3D and VR
Results
Twenty-three participants were excluded. Nine participants were excluded since we altered the task after their participation which made their data useless. Eleven participants were excluded seeing that they were non-responsive subject. This means that these participants did not respond to the degree of effort or degree of reward and almost always chose high or low effort. Two participants were excluded as their data was incomplete. And one participant was excluded after he confessed that he cheated on the task by using his thumbs instead of his fists. After exclusion of these 23 participants we examined the data of 26 subjects.
We conducted a repeated-measures ANOVA to check if there was a difference in mean point of indifference between the 2D-condition (M = 15.81, SD = 3.34), 3D-condition (M = 15.42 SD = 3.46) and the VR-condition (M = 17.19, SD = 4.34). Mauchly’s test indicated that the assumption of
sphericity had not been violated, X^2(2) = 5.27, p = .072. The results show that POI was not affected by condition, F(2, 50) = 0.080, p = .923 (Figure 4). This means that perceived effort did not differ between conditions. Participants experienced their degree of effort the same in all 3 conditions.
Figure 4. Mean Point of Indifference in all three conditions.
Error bars represent the standard error of the mean, as computed over within-subject normalized data.
Furthermore, we conducted a repeated-measures ANOVA to test if there was a difference in mean sense of presence between the 2D-condition (M = 21.73, SD = 4.70), 3D-condition (M = 34.00, SD = 7.45) and VR-condition (M = 49.73, SD = 5.61). Mauchly’s test indicated that the assumption of sphericity had not been violated, X^2(2) = 1.51, p = .47. The results show that sense of presence was affected by condition F(2, 50) = 186.56, p = < .01. On average, participants experienced more sense of presence in the 3D-condition (M = 34.00, SD = 7.45) when compared to the 2D-condition ( M = 21.73, SD = 4.70). This difference, -12.27, BCa 95% CI [-15.31, -9.42], was significant t(25) = -7.63, p = <.01, and represented a high-sized effect, d = 2.61. Furthermore, participants experienced more sense of presence in the VR-condition (M = 49.73, SD = 5.61) when compared to the 3D-condition (M = 34.00, SD = 7.45). This difference, -15.73, BCa 95% CI [-18.08, -14.15], was significant t(25) = -12.11, p = .001, and represented a high-sized effect, d = 2.11 (Figure 5). This means that even though condition did not affect perceived effort, subjects did experience a different sense of presence between all 3 conditions.
Figure 5. Mean Sense of Presence in all three conditions.
Error bars represent the standard error of the mean, as computed over within-subject normalized data.
Since we were interested in the role of Dopamine-levels in decision making we conducted a bivariate correlation analysis. We therefore checked if Listening-span scores correlated with POI in all 3 conditions. Analysis showed no correlation between Listening-span scores and POI in the 2D-condition, r = -.033, p = .872. Neither was there a correlation between Listening-span scores and POI in the 3D-condition, r = -.126, p = .539. We did not find a correlation between Listening-span scores and POI in the VR-condition either, r = .081, p = .695. This means that in the present study Listening-span scores did not play a role in decision making. Since we used the Listening-Listening-span scores as a proxy to estimate the degree of levels, we can suggest that, in the present study, Dopamine-levels might not have an effect on decision making. Finally we checked if Anhedonia played a role in decision making, and if so, to which degree. To do so we conducted a bivariate correlation analysis. By which we checked if TEPS-questionnaire scores correlated with POI in all 3 conditions. Analysis showed no correlation between TEPS-questionnaire and POI in the 2D-condition, r = -.041, p = .854. Neither was there a correlation between Listening-span scores and POI in the 3D-condition, r = .270, p = .213. We did not find a correlation between Listening-span scores and POI in the VR-condition either, r = .061, p = .784. This means that in the present study degree of Anhedonia did not play a role in decision making.
Discussion
The results of the present study showed no significant effect of Virtual Reality on perceived effort and decision making. After analysis of the data we found that there was no significant difference in POI between the 2D-, 3D- and VR-condition. If a participant finds his effort too high after choosing high effort several times, you would see that this participant at some point decides to choose low effort instead. It is at this point where the participant reaches his point of indifference. Based on the prospect theory (Tversky, A., & Kahneman, D., 1992) we expected that perceived effort, and thus POI, would be higher in VR-condition than in 3D-condition and 2D-condition. However, the results of the present study did not confirm to such predictions, so they did not support the hypothesis. Participants perceived their own effort equal across the 3 conditions.
Nevertheless, we did find a difference in sense of presence between all 3 conditions. It was observed that participants scored significantly higher on the IPQ-questionnaire in the 3D-condition when compared with the 2D-condition. Also, results showed that participants scored significantly higher on the IPQ-questionnaire in the VR-condition when compared with the 3D-condition. Thus, even though condition didn’t affect perceived effort, participants did experience all 3 conditions in different ways. Feeling least present in the 2D-condition, and most present in the VR-condition. These findings support our hypothesis.
Furthermore, we found that Listening-span scores didn’t play a role in Decision making. Participants with a high score on the Listening span did not choose to perform high effort to get high reward more often than participants with a low score on the listening span. Since we used the Listening-span scores as a proxy to estimate the degree of Dopamine-levels, we can suggest that, in the present study, Dopamine-levels might not have an effect on decision making. Also Anhedonia didn’t play a role in decision making. We used the TEPS-questionnaire to measure participants’ degree of Anhedonia. Participants with a low score on the TEPS-questionnaire did not choose to perform low effort to get low reward more often than participants with a high score on the TEPS-questionnaire. We can thus conclude that, in the present study, Anhedonia didn’t play a role in
decision making.
Taken together, our manipulation did not create the hypothesized results and argue against earlier findings. Nevertheless, one has to take in mind that most likely our manipulations did not have the desired effect. In the present study participants were asked to move thumbsticks of an Xbox-One controller repeatedly back and forth in opposite motion. The way to do this was by making two fists and letting those fists rest on the thumbsticks while keeping their arms levitating in the air, as if the participant was holding two joysticks. When a participant chose high effort, the thumbsticks had to be moved back and forth more often, on average, when compared to low effort. However, participants indicated that even though they chose high effort, they did not perceive the effort as if it was high. This way, participants chose high effort most often in order to achieve the highest reward possible without taking effort in to account. One could say we experienced a ceiling effect through which the independent variable no longer had an effect on the depending variable. Thus, it is possible that Virtual Reality does has an influence on perceived effort, but that we in the present study did not manage to create enough effort to test such hypothesizes. For further research it would be highly recommended to increase effort in such a way that high effort is actually
experienced as high effort, so that participants in future research take the degree of effort in account when deciding what kind of effort to choose. This could for instance be achieved by hanging weights on participants’ wrist, making it heavier for participants to move their arms back and forth. Also, in future research one could attempt to use 2 different joysticks instead of 2 thumbsticks on an Xbox-One controller, and thus increase the required movement of the arms. This way, participants are forced to perform more effort, and thus ceiling effects will be avoided.
Also, one has to take in account that we used the Listening-span as a proxy for Dopamine-levels. We know that working memory capacity (measured by the Listening-span) predicts Dopamine synthesis in the striatum (Cools, Gibbs, Miyakawa, A.Jagust & D'Esposito, 2008). However, via this way one measures Dopamine-levels indirectly. Dopamine levels will not always be as predicted for each participant when using the listening span because working memory capacity is not only
influenced by Dopamine-levels. Because we measured Dopamine-levels indirectly via the listening span our results could be biased. In future research one can prevent such biases using more accurate methods to measure levels. For instance one could use PET-Scans to measure Dopamine-levels directly and thus get a better estimation of Dopamine-Dopamine-levels. This way Dopamine-Dopamine-levels can be measured more accurate and one can make conclusions with more certainty and reliability.
Altogether we can conclude that more research is required to understand the role of Virtual Reality on perceived effort and EBDM. In this research we contributed to come to this better
understanding. In future research we need to alter the procedure of the present study to understand the exact effect of Virtual Reality on EBDM. This way, leading us to more knowledge about decision making and improving modern day science.
References
Beck, A. T., Steer, R. A., Ball, R., & Ranieri, W. F. (1996). Comparison of Beck Depression
Inventories-IA and-II in psychiatric outpatients. Journal of personality assessment, 67(3),
588-597.
Carver, C. S., & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: the BIS/BAS scales. Journal of
personality and social psychology, 67(2), 319.
Cools, R., Gibbs, S. E., Miyakawa, A., Jagust, W., & D'Esposito, M. (2008). Working memory capacity predicts dopamine synthesis capacity in the human striatum. The Journal of
Neuroscience, 28(5), 1208-1212.
Costa, P. T., & McCrae, R. R. (1989). Neo PI/FFI manual supplement. Odessa, FL: Psychological
Assessment Resources.
Croxson, P. L., Walton, M. E., O'Reilly, J. X., Behrens, T. E., & Rushworth, M. F. (2009). Effort-based cost–benefit valuation and the human brain. The Journal of neuroscience, 29(14),
4531-4541.
Floresco, S. B., Maric, T. L., & Ghods-Sharifi, S. (2008). Dopaminergic and glutamatergic
regulation of effort-and delay-based decision making.Neuropsychopharmacology, 33(8),
1966-1979.
Gard, D. E., Gard, M. G., Kring, A. M., & John, O. P. (2006). Anticipatory and consummatory components of the experience of pleasure: a scale development study. Journal of
Research in Personality, 40(6), 1086-1102.
Holt, D. D., Green, L., & Myerson, J. (2003). Is discounting impulsive?: Evidence from temporal and probability discounting in gambling and non-gambling college students. Behavioural
processes, 64(3), 355-367.
Huang, M. P., & Alessi, N. E. (1999). Mental health implications for presence.CyberPsychology &
Behavior, 2(1), 15-18.
avatar fidelity on cognitive task performance in virtual environments. In Virtual Reality, 2003. Proceedings. IEEE (pp. 125-132). IEEE.
Murray, C. D., Fox, J., & Pettifer, S. (2007). Absorption, dissociation, locus of control and presence in Virtual Reality. Computers in Human Behavior, 23(3), 1347-1354. Patton, J. H., Stanford, M. S., & Barratt, E. S. (1995). Factor structure of the Barratt impulsiveness
scale. Journal of clinical psychology, (51), 768-74.
Peterson, R. A., & Heilbronner, R. L. (1987). The anxiety sensitivity index:: Construct validity and factor analytic structure. Journal of Anxiety Disorders,1(2), 117-121.
Rahman, S., Sahakian, B. J., Cardinal, R. N., Rogers, R. D., & Robbins, T. W. (2001). Decision making and neuropsychiatry. Trends in cognitive sciences,5(6), 271-277. Sacau, A., Laarni, J., & Hartmann, T. (2008). Influence of individual factors on
presence. Computers in Human Behavior, 24(5), 2255-2273.
Spielberger, C. D., Sydeman, S. J., Owen, A. E., & Marsh, B. J. (1999). Measuring anxiety and anger with the State-Trait Anxiety Inventory (STAI) and the State-Trait Anger Expression
Inventory (STAXI).
Treadway, M. T., Bossaller, N. A., Shelton, R. C., & Zald, D. H. (2012). Effort-based decision-making in major depressive disorder: a translational model of motivational anhedonia. Journal of
abnormal psychology, 121(3), 553.
Treadway, M. T., Buckholtz, J. W., Schwartzman, A. N., Lambert, W. E., & Zald, D. H. (2009). Worth the ‘EEfRT’? The effort expenditure for rewards task as an objective measure of
motivation and anhedonia. PLoS One, 4(8), e6598. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of
uncertainty. Journal of Risk and uncertainty, 5(4), 297-323. Vos, S. H., Gunter, T. C., Kolk, H. H., & Mulder, G. (2001). Working memory constraints on
syntactic processing: An electrophysiological investigation.Psychophysiology, 38(01),
41-63.
environment. Virtual Reality, 13(3), 205-217. Wendel-Vos W. G. C., Schuita, J. A., Sarisc. W. H. M., & Kroumhout, D. (2003). Reproducibility and
relative validity of the Short Questionnaire to Assess Health-enhancing physical activity. Journal of Clinical Epidemiology, 56, 1163–1169 Witmer, B. G., & Singer, M. J. (1998) Measuring presence in virtual enviroments: a presence
Reflectieverslag
Terugkijkend op het Bachelorproject heb ik het idee dat ik veel van het project heb geleerd.
Voorgaand aan het project had ik nogal een simplistisch beeld over onderzoek doen. Ik heb gemerkt dat onderzoek doen moeilijker is dan je denkt. Vooral de voorbereiding naar het onderzoek toe. We zijn geregeld met de hele groep samen gekomen om uiteenlopende zaken te bespreken/voor te bereiden. Dit nam soms meer tijd in beslag en ging soms minder snel dan ik dacht. Niet omdat het niet goed ging, maar omdat goed onderzoek doen nou eenmaal niet simpel is. Bedenken hoe de taak eruit moet zien, proefpersonen werven, de taak werkend krijgen in het lab, vragenlijsten
zoeken/opstellen, vragenlijsten zo programmeren dat ze werken en de ingevulde data opslaan, ga zo maar door. Er moest veel gebeuren. Het mooie vind ik vooral dat iedereen taken op zich nam om het allemaal sneller en makkelijker te laten lopen. Ik vond dan ook dat er een goede samenwerking was binnen de groep. De goed samenwerkende groep is naar mijn idee bepalend geweest om het project goed te laten verlopen. Wat minder goed liep is naar mijn idee de eerste dagen dat we gingen meten in het lab. De taak wilde nog niet naar behoren werken, apparatuur deed het niet naar behoren. Nogal wat gestress. Hiermee bedoel ik overigens niet te zeggen dat dit per defintie slecht is. Het feit dat er nogal wat gestress is vlak voordat het onderoek begint hoort er volgens mij ook bij en geeft je denk ik juist een duidelijker beeld over hoe onderzoek in de realiteit er aan toe gaat. Toen de taak eenmaal liep was het leuk om even te ervaren hoe het is om onderzoeker te zijn. We hebben er naar mijn idee alles aan gedaan om proefpersonen zo goed mogelijk van tevoren te informeren over het onderzoek en om de proefpersonen op hun gemak te laten voelen. Voorgaand aan het onderzoek werd duidelijk verteld wat de effecten van het onderzoek konden zijn, wanneer er niet meegedaan mocht worden aan het onderzoek en dat de proefpersonen ten alle tijden konden stoppen met het onderzoek wanneer zij dat wilden. Wat betreft het schrijven van mijn onderzoek denk ik dat dit wel aardig is gelukt. Ik heb er voor gekozen om in het engels te schrijven omdat dat mij een goede oefening leek en ik op deze manier ook kon testen of ik daar toe wel in staat was. Naar mijn idee is dat aardig gelukt. Verder denk ik dat het schrijven wel goed liep omdat je op een gegeven moment
zo veel met het onderzoek bezig bent, waardoor je al veel kennis hebt opgedaan over het
onderwerp. Het moeilijkste gedeelte vond ik mijn inleiding. Om goed uit te leggen waarom wij voor dit onderzoek hebben gekozen en wat de meerwaarde er van is. Daarnaast om het ook leuk te houden om te blijven lezen. De methode sectie vond ik het leukst/makkelijkst omdat ik het leuk vond om precies te omschrijven hoe ons onderzoek in elkaar zat. Ik denk dan ook dat dit het sterke punt van mijn verslag is.
