Investigating the relationship between intertemporal decision-making and emotion regulation.


What role emotions and, similarly, emotion regulation play in intertemporal decision-making remains an unanswered question in the field of Neuroeconomics. The current study aimed to address this gap in the literature by investigating the relationship between intertemporal decision-making and emotion regulation. Participants have filled out ten questionnaires and executed two monetary decision-making tasks; an intertemporal choice task and risky choice task. Subsequently they completed an emotion regulation task, where participants watched neutral and negative valenced images while regulating their own emotions during two stages of the experiment. Findings revealed that, compared to simply observing negatively valenced IAPS pictures, participants experienced a significantly higher valence and arousal of emotion while up-regulating their emotion, and a significant lower valence and arousal of emotion while down-regulating their emotion. Another result indicates that participants who experience better down-regulation of emotion were also more risk seeking. An additional outcome indicates subjectively better down-regulation can be predicted by a bad acceptance of feelings. This parameter measures the ability of individuals accepting their own emotions. Taken together findings show that there are multiple factors influencing each other in the relationship between intertemporal decision-making and emotion regulation. Furthermore results indicate an important relationship between the ability to down-regulation aversive emotions and decision-making.

MA student Cognitive Neuroscience: Felix Hermsen I6099110 / U329288.

Primary supervisor: Dr. Jan Engelmann, Post Doc., Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen.

Secondary supervisor: Dr. Sanae Okamoto-Barth, Post Doc., Department of Economics and Cognitive Neuroscience, University Maastricht.

Date: 31 august 2015. 


Every day we are overloaded with information, and we make millions of decisions that can be influenced by many variables. Decision-making is an important aspect in everyday life that can range from basic animal behavior like monkey foraging, to complicated human decision-making, such as investing or saving money. Decisions are typically based on an evaluation of the alternative choice options. It occurs whenever a choice has to be made from several alternatives based on subjective value. Subjective value is an important concept in decision-making. It is centered around the well-documented observation that people exhibit personal preferences over probabilities and amounts that lead to a distortion of objective value (e.g., Kahneman and Tversky, 1979). This process is called valuation, assigning value to our prediction of the consequences that are likely to result from each possible option (Rangel, Camerer, & Montague, 2008). Kahneman and Tversky introduced a framework of value maximization which states that after valuation of each option we select the option with the greatest subjective value (Kahneman & Tversky, 1979).

Only what happens if we need to make a choice between alternatives that are potentially delayed in time. Such decisions have been termed intertemporal decisions and it has been shown that delaying choice outcomes changes subjective value in a systematic manner (Berns et al., 2007). A typical experimental setup investigating intertemporal decision-making was used by Kable and Glimcher in 2007. In their experiment, subjects made a choice between an immediate option with a small monetary value which is called the sooner-smaller option (SS), or a delayed option with a larger monetary value than the SS choice, which is called the later-larger option (LL). Simultaneously brain activity was measured via functional Magnetic Resonance Imaging (fMRI). They showed that neural activity in several brain regions, particularly the ventral striatum, medial prefrontal cortex, and posterior cingulate cortex, tracks the subjective value of the monetary choice options. Activity in these brain regions increases as the objective amount of a reward increases and decreases as the imposed delay to a reward increases. This shows the correspondence between neural activity and decision making behavior, which provides evidence that the subjective value of potential rewards is explicitly represented in the human brain (Kable & Glimcher, 2007).

Similarly, Keren and Roelofsma (1995) used SS and LL options to study the relationship between time delay and uncertainty. The time delay manipulation existed of receiving money directly or over 4 weeks, or receiving money over 26 or 30 weeks. For the uncertainty manipulation they used three degrees of probability, namely 1.0, 0.9 and 0.5, meaning in the first case subjects always receive the monetary value of the option they chose, in the second case they receive it in 90% of the cases, and in the latter case subjects only receive in half of the cases the monetary value of the option they chose, so there is a high uncertainty. In the study they used Fl., which was the Dutch currency, $1.00 was approximately Fl. 2.00. They present findings where a large majority of subjects prefer the immediate reward of Fl. 100 over a 4 weeks delayed award of Fl. 110, only 18% of the subjects choose the LL option with a 1.0 probability. However when uncertainty is included, with a probability of 0.5, 61% of the subjects choose for the LL option. Together, these results indicate that when uncertainty is increased, the number of subjects choosing the LL option increases. Thus, the uncertainty manipulation affects intertemporal choices (see table 1) (Keren & Roelofsma, 1995).

Table 1. Proportion (actual numbers in parentheses) of subjects preferring the more immediate outcome of Fl. 100 for three different levels of uncertainty. Table is cited from (Keren & Roelofsma, 1995).

Intertemporal decisions are ubiquitous in real life and include, for instance, buying a product online and receiving it days later in the mail, saving money at the bank or doing investments on the stock market. Studies provided evidence that unique aspects of temporal delays, such as deliberations about our future self and potentially the increasing anxiety during the delay (Wu, 1999) affect our behavior (Engelmann & Brooks, 2009).

It has been shown that intertemporal decisions follow certain patterns that can be captured by econometric models of the effects of temporal delay on subjective value and behavior. Three discount functions are commonly used in the literature (see Figure 1): (1) exponential discounting, which assumes a constant rate of discounting, meaning the value of the reward decays by the same proportion for each minute that its occurrence is delayed. (2) Hyperbolic discounting is generally greater for short time periods than long periods, assuming the value of the reward decays faster for short compared to long time periods. (3) Quasi-hyperbolic discounting is a piecewise function that follows a similar form as exponential discounting, but with the important difference that early and late discount periods are modeled via separate parameters. This approach suggests that the value of the reward decays rapidly in the first year, and thereafter, it decays slower and by the same proportion as for exponential discounting.

Figure 1. Discount functions cited from Berns, Laibson, & Loewenstein, 2007. Exponential discounting assumes a constant rate of discounting, e.g. dt where d is the discount rate (here, d = 0.95). Hyperbolic discounting is generally greater for short time periods than long periods, and can be described by a function of the form 1/(K * t + 1). Here, K = 0.1. Quasi-hyperbolic discounting is a piecewise function that follows a form similar to exponential discounting after the first discount period (i.e. the first year): 1, β _ δ, β _ δ 2, . . ., β _ δ t. (Here, β = 0.792 and δ = 0.96).

The discount function that empirically best fits human intertemporal decisions is a quasi-hyperbolical function, which decays at a more rapid rate in the short run than in the long run. This implies that most people are more impatient when making short-run tradeoffs than when making long-run tradeoffs. However, we also care about our future and about our health and wealth later in life, e.g., imagine buying a house, or planning for retirement. So suggested is the splicing of two systems with different perspectives toward the future, for short run and long run preferences (Berns, Laibson, & Loewenstein, 2007).

As stated earlier, our internal states and emotions influence the manner how we adapt to our environment. This has important consequences for our physical and mental well-being and the way we make decisions. Recent reviews of the literature on Affective and Cognitive Neuroscience conclude that several underlying brain mechanisms of emotion and cognition are intertwined from early perception to higher-order reasoning (Phelps, 2006; Pessoa, 2008). These interactions between emotions and cognitions suggest that emotions likely influence cognitive processes that support decision-making. Indeed, over the past 30 years it has repeatedly been shown that emotions do influence decision-making. Many purely behavioral studies documented the effects of emotions on decision-making. Smith and Dickhaut showed that heart rate as a proxy for emotion predicts bidding in Dutch auctions (Smith & Dickhaut, 2005). Results from studies by Lerner et al. indicate that inducing negative mood diminishes and reverses the endowment effect (Lerner, Small, & Loewenstein, 2004), and Maner et al. showed that anxiety is associated with risk avoidance (Maner, et al., 2007).

But, we are not merely slaves of our emotions, the interaction between emotions and cognitions is a two-way route. In order to take control of our internal climates and our emotions, we can employ regulation strategies; such strategies allow us to fully or partially alter the nature, magnitude and duration of our emotional responses, and also to initiate new ones (Gross & Munoz, 1995). Emotion regulation strategies at neural levels can be observed from a series of neuroimaging meta-analyses and reviews, which have shown that a particular network of brain regions is involved in the cognitive control of emotion (Kober, et al., 2008; Wager, Lewis, Haviland-Jones, & Barrett, 2008). Based on a recent meta-analysis, Ochsner et al. proposed a neural model of cognitive control of emotion (MCCE), which suggests that the amygdala, as well as lateral PFC regions and the anterior cingulated cortex are crucial to perform computations central for regulating emotions (Ochsner, Silvers, & Buhle, 2012).

This network is divided into two pathways linking control and affect systems. The first key region of the affect pathway is the amygdala, which generally is sensitive to detecting and triggering responses to arousing stimuli (Anderson, et al., 2003), but exhibits a bias toward detecting cues signaling potential threats, like the expressions of fear (Vuilleumier & Pourtois, 2007). The amygala is also involved in the perception and encoding of stimuli relevant to affective goals like rewards or punishments to facial expressions of emotions (Cunningham, van Bavel, & Johnsen, 2008). Another key region of the affect system is the ventral striatum, which is involved in learning which cues predict rewarding or reinforcing outcomes (Knutson & Cooper, 2005; O’Doherty, 2004). Information from regions as the amygdala and the ventral striatum are integrated in the ventromedial prefrontal cortex (vmPFC). This region is a central component of the cognitive control system, as it integrates affective valuations of stimuli, and it receives also input from other regions like the medial temporal lobe that provides historical information about prior encounters (Murray, O’Doherty, & Schoenbaum, 2007; Fellows, 2011). The vmPFC also tracks positive or negative valuations of stimuli in a curtain context and goal dependent manner (Bartra, McGuire, & Kable, 2013; Roy, Shohamy, & Wager, 2012), having an important effect on decision-making in different situations. The ventral striatum is found to be involved in rewarding and reinforcing outcomes, whereas the insula plays a role in negative affective experiences in general (Wager & Feldman Barrett, 2004). The insula and ventral striatum valuate stimuli, and vmPFC integrates this information, but choice options can be of similar value, they may conflict with eachother. In case of conflict, a control system like the anterior cingulate cortex activates and monitors the conflict, this may cause motivation to value changes in such conflicts. These changes of value are called reappraisal and involves focusing on and interpreting or reinterpreting one’s own emotional states. Such computations are likely performed by dorsomedial prefrontal regions, which have been implicated in attributing mental states in prior research (Olsson & Ochsner, 2008). In its core this model specifies how prefrontal and cingulate control systems modulate activity in affect systems as a function of one’s regulatory goal, tactics, and the nature of the stimuli and emotions being regulated (Ochsner, Silvers, & Buhle, 2012). The model gives a general overview on how the brain encodes emotion regulation and cognitive control of emotion.

Researchers have been conducting studies among three levels of analysis namely, neural systems, processes and behavior (Ochsner, Silvers, & Buhle, 2012). In order to fully understand emotion regulation, strategies and decision-making, the research conducted here concentrates on the behavioral level, and will focus subsequently on the neural systems and processes to get a full insight among all levels of inquiry.

This behavioral experiment consists of a combination of three tasks that examined individual differences in emotion regulation on intertemporal and risky decision-making. Subjects have first participated in an intertemporal choice task (ITC) and in a Risky choice task (RC), the order of which was randomized. In the ITC task they made decisions between SS and LL options with real monetary payouts, in order to properly incentivize choices. In the RC task, every choice scenario offered them an alternative between choosing a probabilistic lottery of amounts or a sure amount. The second part of the experiment assessed each subject’s emotion regulation ability (ER). During this task, subjects viewed neutral and negative images from the International Affective Picture System (IAPS) (Lang, Bradley, & Cuthbert, 2008) while autonomic arousal is assessed via skin conductance responses, heart rate and self-reports. Subjects watch valence- and arousal-matched series of either neutral or negative images, and were instructed to regulate (weakening or strengthening) their emotions in some conditions.

Moreover, subjects participated in an online survey with ten questionnaires first, after completion they are permissible for the second part of the study. In the second part subjects did in random order the ITC task and the RC task first to get an unbiased assessment of their intertemporal preferences (Figner, et al., 2010); the ER task will be done afterwards. The study examined to what extend emotion regulation ability influences decision-making. Based on discussed literature, we hypothesize that subjects who experience to be better in emotion regulation, are more risk neutral (less risk averse) and discount less steeply (choose more often the LL choice). Also further analyses will be conducted to investigate different relationships between emotion regulation and decision-making. This behavioral study precedes an fMRI study, in order to assess the relationship between ER, risk taking and intertemporal choice on all levels of analysis, namely at the behavioral level and the level of neurocircuitry. The potential applications are significant for a span of fields that include: (1) Psychiatry, where many psychiatric disorders involve a failure of both, decision-making and emotion regulation; (2) The judicial system, where it is important to define and measure whether individuals are in full command of their decision-making abilities; (3) Artificial Intelligence, which uses our understanding of the neural mechanisms underlying decision-making to model optimal choice processes by artificial systems; (4) Marketing, which tries to improve our understanding of how marketing affects decision-making, and (5) Psychology and Economics, where it is important to develop interventions to train individuals to become better decision-makers, especially in conditions of extreme time-pressure and large stakes (Rangel, Camerer, & Montague, 2008).



In total 31 individuals participated in this study that was approved by the ethical review board of Radboud University. The participants were aged between 19 and 33 with an average of 23,8 years of age and a standard deviation of 4 years. The group of participants consisted of 23 women and 8 men, all with normal or corrected to normal vision. The participants gave written consent before participating in the study by signing an informed consent first for the monetary choice task (appendix 1) and second for the ER task (appendix 2). The recruiting of participants was done at the Radboud University of Nijmegen. Flyers and an online participation system were used for getting the attention and interest of possible participants.

Tasks and stimuli.

This study existed of three behavioral experiments; the intertemporal choice task (ITC task), the risky choice task (RC task) and the emotion regulation task (ER task). Subjects did in random order the intertemporal choice task or the risky choice task first, and afterwards the emotion regulation task.

Intertemporal choice (ITC task).

The intertemporal choice task (Figner, et al., 2010) exists of in total 72 binary choices, each offering a sooner-smaller reward or a later-larger reward. The design of the task is altered compared to the original task, this to create a better visualization for the difference in time delay between the SS and LL option (see figure 2).


Figure 2. Screenshot of the ITC task, in this example trial is on the left the SS option ‘now 41.68’ and on the right the LL option ‘in 4 weeks 50.29’. The numbers in the task are ‘Lab$’, participants get instructions before the task about the values in the experiment are Lab$ with an exchange rate of 1 Lab$ = 0.1546 Euros.

For every choice option participants had a decision time of maximal 8 seconds. The trials represented a full factorial design that varied: (1) The time of the SS option, which is now or in 2 weeks, resulting in 36 now trails where the SS option is an immediate reward, and 36 not-now trials were the SS and the LL are both delayed rewards; (2) The time interval between SS and LL, which was 2 weeks or 4 weeks; and (3) the relative difference in the reward amount of the SS and LL, the LL was either 1, 5, 10, 20, 25, 30, 50 or 75% larger than the SS amount. These relative magnitudes of the SS and LL rewards allowed us to create choice conflict. For trails with a relative difference of 1%, there is low choice conflict because the difference is so small that it is not worth waiting two weeks. On the other hand for trials with a relative difference of 75% there is also low choice conflict due to a huge difference in reward magnitude, the LL amount is almost two times as high as the SS, and is worth waiting two weeks. In the remaining trials were the LL reward is moderately larger than the SS reward, there is expected high choice conflict. Neither option clearly stands out, and participants needed to resist the more seductive immediate SS reward instead of taken the higher LL option (Figner, et al., 2010). Participants were presented simultaneously with the SS and LL option on a computer screen, where the SS option appeared randomly on the left or right side on the screen, this to prevent participants for blindly pressing a key for solely the SS or LL amount. Participants needed the press the ‘A’ button for choosing the left option, and ‘L’ for choosing the right option on the screen. When pressing the button, a big red square surrounded the selected option, and subsequently the next trial started. To keep the ecological validity of this experiment high, subjects were informed before the task that they receive an standardized percentage of an amount they chose in one of the trials. This amount is chosen at random at the end of the experiment. An algorithm will pick out one trail and a percentage (1 Lab$ = 0.1546 Euros ) of the chosen option in that trial is paid out to the bank account of the participant. Depending on the time delay of the selected choice, the subject will receive the amount directly or in weeks via bank transfer. The measures of interest for this task are the choices participants made, no physiological measurements were be acquired.

The Risky choice task ( RC task).

In the risky choice task participants made decisions concerning monetary payoffs. Each choice scenario offered them an alternative between choosing a probabilistic lottery and a sure amount. For every trial Participants have 7 seconds time to decide. If they were too late, the trial was presented again at the end of the experiment, in this case participants were encouraged to respond rapidly, but always completed the 126 trials. For this task was the option on the right side of the computer screen always the sure amount. On the left side appeared the lottery choice, both choices always appeared simultaneously on the screen (see figure 3). The lottery always offers one potential payoff that is greater than the sure amount, and one that is smaller. These lottery payoff amounts appeared at the top and bottom of the pie chart, and participants received a percentage of only one of the two amounts displayed (as specified for the ITC task). Lottery outcomes were determined by flipping a biased virtual coin that will choose one of the monetary amounts, based on the probabilities associated which each amount. The probabilities used in this task are: 5, 10, 20, 50, 80, 90, and 95%. Each probability is indicated via colored pie charts, where blue slices always represented the probability of the amount that occurred on top, while yellow slices represented the probability of the amount presented on the bottom. Slices are demarcated through white lines and each slice represented a 10% probability. Choosing the sure amount on the right always provided the specified amount and does not entail chance.


Figure 3. Screenshot of the RC task, in this trial is the sure amount 24 (right side) and the lottery choice (left side) exist of a 20% chance of winning 38 and a 80% chance of winning 14. Participants clicked on the buttons ‘select this option’ at the bottom of both choices to select their preferred option. In the upper right corner of the screen was a decision time countdown included, and in the top left of the screen was the trial number represented.

When participants clicked on the ‘select this option’ button, a window popped up asking participants to confirm their choice. They could click ‘no’ in case they changed their mind and want to reconsider, and click ‘yes’ to confirm their selection and move on to the next trial. Participants were asked to select the option that is more attractive to them, and they were informed that there is no right or wrong answer. Between choice scenarios there was always a 2 seconds break, the total task consists of 126 choice scenarios and took around 20 to 25 minutes. Also in this task were the numbers in ‘lab$’, which has the same exchange rate as in the ITC task (1 Lab$ = 0.1546 Euros ). An algorithm randomly selected one trail, and a percentage of the amount won in that trail was transferred to the bank account of the participant the same day. As well as for the ITC task, the measures of interest are the choices participants made. No physiological measurements were acquired.

Emotion Regulation ability task (ER task).

For the emotion regulation task participants viewed images on the computer screen while electrophysiological data was acquired. They saw images on the computer screen and passively watched the images. After every block of images the subjects were asked to answer a few questions about the pleasantness of the pictures. The participants saw 18 images per block for 7 seconds each. There were 9 blocks, with a total duration of 28 minutes. Two different kinds of blocks were shown to the subjects, blocks containing only neutral images and blocks containing only images depicting negative scenes. All negative images were taken from the IAPS set (Lang, Bradley, & Cuthbert, International affective picture system (IAPS): Affective ratings of pictures and instruction manual., 2008), which allowed us to use the pre-existing valence and arousal ratings to create matched sets of neutral and negative images. So every negative block consisted of different negative images but with the same overall negative valence. In order to probe subjects’ regulation abilities, participants were asked to strengthen and weaken their emotions in two of the four negative blocks. This was done via an instruction before the start of the block, which stated in case of emotional up-regulation; ‘While you watch the next series of images, we would like you to make your emotions stronger. Please try and make yourself feel closer to the event in the picture. While watching, try to experience the sounds, smells and sights of the event. You can do this by imagining that you are present in the scene shown in the picture and that the event is happening to yourself, or a person you love. Please talk to your experimenter should you have any questions about how to make your emotions stronger. If you are ready, press ENTER to continue.’ In case of emotional down-regulation, subjects were instructed beforehand as follows; ‘While you watch the next series of images, we would like you to make your emotions weaker. Please try to distance yourself from the events in the picture. Try to view the picture as a detached observer that is far away from the scene and not at all personally connected to the person or the scene shown in the picture. You can do this by imagining that you are seeing this picture in an old newspaper . Please talk to your experimenter should you have any questions about how to make your emotions weaker. If you are ready, press ENTER to continue.’ The different blocks of images were shown and instructions were given in the following order:

Just watch the pictures, Neutral Block (bring everyone to mood baseline)

Just watch the pictures, Negative Block 1

Just watch the pictures, Neutral Block (back to baseline)

Just watch the pictures, Negative Block 2 (to assess adaptation effects)

Just watch the pictures, Neutral Block

“Try to strengthen your emotion”, Negative Block 3

Just watch the pictures, Neutral Block

“Try to weaken your emotion”, Negative Block 4

Just watch the pictures, Neutral Block

The two emotion regulation blocks ( Negative block 3 and 4) were counterbalanced. After each block, subjects were asked to rate their emotional state (arousal x valence) and, when appropriate ( in negative blocks 3 and 4), how well they think they did in regulating their emotions. While doing the task, subjects were sitting in front of a computer screen and got two physiological measurements. First skin conductance response (SCR) was measured via electrodes on the middle and ring finger of the non-dominant hand. Second heart rate (HR) was measured via electrodes around the heart. Before starting the computer task both signals were checked on noise to make sure there was a proper signal.


Participants signed up online for this study at the SONA systems, which is the university research registration system to apply for studies. After applying online for Part 1 of the research, a window pops up with an online survey in Qualtrics (Qualtrics, 2015). This survey exists of 10 questionnaires, and some general questions about the participants age, gender, contact information and their field of study. The 10 questionnaires included are:

Emotion Regulation Questionnaire (ERQ) (Gross & John, 2003), which is designed to measure respondents’ tendency to regulate their emotions in two ways: (1) cognitive reappraisal and (2) expressive suppression.

Reappraisal Motivation and Ability (Troy, Wilhelm, Shallcross, & Mauss, 2010), which examines the ability of cognitive reappraisal.

Emotion-Regulation Skills Questionnaire (ERSQ) (Berking, et al., 2008), which is designed to examine specific emotion regulation skills.

Self Control Scale (Tangney, Baumeister, & Boone, 2004) which measures individual differences in self-control.

State Trait Anxiety Inventory (STAI) (Spielberger & Sydeman, 1994), which is an introspective psychological inventory that measures anxiety at both poles of the normal affect curve (state vs. trait).

State Trait Anger Inventory (STAXI) (Spielberger & Sydeman, 1994) which comprises six scales for assessing the experience, expression and control of anger.

Beck depression inventory (BDI) (Beck, Ward, & Mendelson, 1961), which is an instrument for measuring the severity of depression in adolescents.

Barrat Impulsiveness Scale 11 (BIS-11) (Barrat, 1994), this scale measures trait impulsivity with three lower-level subtraits; motor impulsiveness, non-planning impulsiveness and attention or cognitive impulsiveness.

Reactive-Proactive Aggression Questionnaire (RPQ) (Raine, et al., 2006), which measures reactive and proactive aggression.

Interpersonal Reactivity Index (IRI) (Davis, 1996), which is a multidimensional measure of empathy for an adult population.

After completing the total survey, participants were directly linked to SONA systems where they enrolled for a timeslot for part 2 of the study. After online registration for part 2 of the study, the participants were tested individually in one of the laboratory rooms from the Behavioral Science Institute at the Radboud University Nijmegen. The three tasks were conducted in a one-hour experiment, in which the sequence of the three tasks was always first the ITC or Risky choice task, and afterwards the ER task. Before starting the experiment the participants were asked to sign the informed consent for the monetary choice tasks (appendix 1). Hereafter, the participants were asked to turn off their mobile phones, and to sit in front of a computer screen. In case the participant started with the ITC task, he or she received on paper the instructions for the ITC task (appendix 2) and were asked to read this thoroughly, while the researcher started the task in Presentation (Presentation, 2015). The researcher stayed in the room during the first four test trials, for answering questions and making sure the participant understands the task. Afterwards the researcher left the room and the participant started doing the real task. After finishing the task, the participant could call the researcher again, or during the task if the participant had any questions. Subsequently after completing the ITC task, the RC task instructions (appendix 3) were provided on paper to the participant. While the participants reads this instruction, the researcher starts the RC experiment in Z-Tree (Z-Tree, 2015). The participant was asked to explain the first choice he or she made. This to assess if the participants correctly understands the task. Upon completion of the RC task, the participant received an informed consent for the ER task with additional information about the task (appendix 4). After signing the informed consent, the participant was set up for collecting electrophysiological data. This means first, the BIOPAC (BIOPAC, 2015) system with AcqKnowledge software was switched on and ECG electrodes were attached to the body for measuring heart rate (see Figure 4).

Figure 4. Three ECG electrodes were attached to the participants body. The white lead was attached underneath the right clavicle, the black lead underneath the left clavicle and the red lead on the ribs on the left side underneath the left breast.

Thereafter SCR electrodes were filled with conductance gel and these finger electrodes were attached to the middle and ring finger of the non-dominant hand of the participant with a velcro strap. The subject was instructed to place the hand in a comfortable position to avoid cramping and to keep the hand still during the experiment. The researcher started the acquisition system and looked at the quality of the data. In this way possible noise in the signal or other system failures could be solved before starting the task. The researcher explained the ER task and prepared the participant for the blocks with the up and down regulation. The participant is also told that the experiment can be stopped at anytime and that the researcher will be in the room directly next to the laboratory room for questions or difficulties. After finishing the task, electrophysiology data was directly saved and the participant could detach all equipment from the body. When the equipment was off, participants were asked to fill out an exit questionnaire (appendix 5). Next participants received their â,¬10 payment and fill out a form for receiving this cheque. This finishes the experiment and subjects were thanked for participation and left the laboratory.

Behavioral and statistical analyses and formulas.

Behavioral data was recorded via Z-tree (Risky Choice Task), or Presentation (ER and ITC tasks, (Presentation, 2015) software and in-house software. The data was analyzed in R (R for Statistical Computing, 2015) and SPSS (SPSS, 2015). Electrophysiological data was recorded via AcqKnowledge software (BIOPAC, 2015) and analyzed via in-house scripts programmed in Matlab (Matlab, 2015). Analyzing the electrophysiological data (SCR and HR) is beyond the scope of this dissertation. The questionnaires analyzed are the Emotion Regulation Questionnaire (ERQ) (Gross & John, 2003) and the Emotion-Regulation Skills Questionnaire (ERSQ) (Berking, et al., 2008). Multiple parameters were estimated for the RC task and the ITC task using maximum likelihood estimation of econometric models that are outlined in detail below.

The following four parameters are analyzed for the RC task; risk taking (alpha), the intercept (beta) and curvature (gamma) for probability weighting and consistency (temp). These parameters were estimated based on subjects’ choices using maximum likelihood estimation. Estimated coefficients represent individual LBW (Lattimore, Baker, & Witte, 1992) weighting function parameters:

EV Sure, i = swai

EV Lottery, i = (1 — wi (p1) ) z1ai + wi (p1) z2ai

ΔEVi = EV lottery, i — EV sure, i

wi (p1)= (βi P1γi)/(βi P”1″ γi+(1-p”1 ” )γi)

Pi(ChooseLottery)= 1/(1+exp⁡〖(-tiΔEVi )〗 )

In these equations, sw was the sure win, the lottery amount z1 was smaller than sw, and the lottery amount z2 larger than sw. The winning probability was p1, and wi(p1) was the weighting function. The risk premium was indicated with ΔEVi and Pi(ChooseLottery) was the probability of choosing the lottery instead of the sure amount. The a measured the degree of risk aversion, what reflects the curvature of the subjective value function v(x). Elevation of the probability weighting function w(p) was indicated with β, and this measured the attractiveness of gambling. γ stands for the curvature of w(p), measuring weighting discriminability (Schmid, Engelmann, Chumbley, & Fehr, 2013). t measures the sensitivity of choice probability to the value difference and, hence, measures the degree of randomness in choice behavior (Hsu, Krajbich, Zhao, & Camerer, 2009).

To extract discounting parameters from choice in the ITC task, we employed a quasi-hyperbolic discounting function that models the sum of two exponential functions to separately estimate early and late discounting (Kable & Glimcher, 2007; Engelmann et al., 2013):

The sum of two exponentials is one formulation of an economic model that explains hyperbolic-like discounting by the combined action of two systems (Laibson D. , 1997; McClure et al., 2004): one (β) that discounts more steeply than the person’s resulting behavior, and a second ( ) that discounts less steeply than the person’s resulting behavior (Kable & Glimcher, 2007). This parameter was entered into follow-up regression analyses investigating the relationship between early discounting, emotion regulation and subscales of the questionnaires.

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