Emotion Regulation and Bidding in Auctions

Bachelor Thesis, 2012

73 Pages, Grade: 1,0




1 Introduction

2 Emotion regulation
2.1 Basic principles by Gross
2.1.1 Emotion regulation strategies
2.1.2 Effects and consequences
2.1.3 Psychophysiological measures
2.2 Heart rate variability (HRV)
2.2.1 Emotions and heart rhythm patterns
2.2.2 Physiological basics of HRV
2.2.3 HRV analysis
2.3 Existing experimental studies in economics
2.3.1 The Ultimatum Game
2.3.2 The Framing Effect
2.3.3 Decision-making under risk and uncertainty
2.3.4 Auctions and financial market
2.3.5 Overview

3 Experimental results
3.1 Experiments
3.1.1 All experiments
3.1.2 Regret experiment
3.1.3 Emotion induction experiment
3.1.4 Human versus computer experiment
3.2 Discussion

4 Conclusion and outlook

Appendix A. Joint analysis of all experiments
A.1 Frequency diagrams of the distributions of the variables
A.2 Linear regressions showing effects of gender, age and reappraisal
Appendix B. Further analysis of the regret experiment
Appendix C. Further analysis of the emotion induction experiment
C.1 Linear regressions regarding SDNN
C.2 Linear regressions with LF/HF-ratio, HF and risk behavior
Appendix D. Further analysis of the computer versus human experiment

List of Abbreviations

List of Figures

List of Tables


1 Introduction

“Emotion has taken the center stage in decision theory, and with it emotion regulation promises to play an increasingly prominent role in psychology, economics and cognitive neuroscience”. This quotation from Heilman, Crisan, Houser, Miclea and Miu (2010) alludes to the rising interest in the investigation of emotional processing in economics in the last few years and to the recognition of the importance of the connection between emotions, physiology and behavior in economic decision-making. But what is the relation of emotions to economics? Gross and Thompson (2006) state that “emotions arise when an individual attends to a situation and sees it as relevant to his or her goals”. Beside this definition of emotion there exist many other definitions, for example that “emotions reflect the status of one’s ongoing adjustment to constantly changing environmental demands” (Thayer & Lane, 2009) which is relevant in economics. Emotions can impair decision-making and can lead to irrational decisions which are not regarded in common prospect theory (Kahneman & Tversky, 1979)

Emotions are regulated by humans in different ways. Two main emotion regulation strategies are pointed out by Gross and John (2003): cognitive reappraisal and expressive suppression which intervene in the emotion generative process at different points of time. The emotion regulation depends on an individual’s ability to adjust physiological arousal on a momentary basis (Appelhans & Luecken, 2006). This is reflected by the resting heart rate variability (HRV) as an objective measure for individual differences in regulated emotional responding. HRV is considered a measure of heart-brain interactions and the flexible dynamic regulation of the autonomic nervous system (McCraty & Childre, 2010) as well as a sensitive indicator of inhibitory control mechanisms relevant for decision-making (Sütterlin, Herbert, Schmitt, Kübler & Vögele, 2011a). Because of their influence in economics it is important to better understand cognitive processes and that can be achieved by physiological measurements of HRV which give an objective insight in the emotional processing of individuals. Physiological parameters like skin conductance and heart rate are related to economic decision-making. Studies have already shown that physiological arousal can be a predictor for decision-making behavior (Adam, Gamer, Hey, Ketter & Weinhardt, 2009).

In this work I want to focus on the influence of different emotion regulation strategies – which are related to the current emotional state – on bidding behavior in first-price sealed bid auctions. In the framework for emotional bidding (Adam, Krämer, Jähnig, Seifert & Weinhardt, 2011) which is shown in figure 1, I want to prove the connection P6 between emotional state and bidding strategy / behavior. The emotional bidding framework is based on the conceptual model of Rick and Loewenstein (2008) on emotions in economic behavior. The four upper boxes represent the bidding process in an auction. The other boxes indicate the emotional processing of a participant during the auction. For the present work, only the left part of the figure is interesting. The emotional state (before the auction has actually started) is influenced by the auction system and the environment.

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Figure 1: Emotional bidding framework (Adam, Krämer et al., 2011)

Due to the subjectivity of questionnaires to identify emotions and emotion regulation, additional physiological measurements serve as objective measures of the emotional state. The question how the participant deals with his emotions can be answered by measuring the heart rate variability at rest. The assumption is that, depending on his emotional state, the participant’s bids are placed higher or lower.

I hypothesize now that higher HRV at rest, especially parasympathetically mediated HRV, leads to lower bids in the auction. The high frequency (HF) range of the HRV reflects vagal cardiac influence which stands for better emotion regulation (Root, 2009; Thayer, Ahs, Fredrikson, Sollers & Wager, 2012; Thayer & Lane, 2009).

Another question is if the identified emotion regulation strategies of the participants fit the obtained results of HRV measurements. Higher HRV and HF as physiological indicators of one’s capacity to effectively regulate one’s emotions (Denson, Grisham & Moulds, 2011) should be reflected in more effective emotion regulation like reappraisal whereas lower HRV and HF should be reflected in less effective emotion regulation like suppression.

The structure of the work is as follows: First of all, the emotion regulation strategies as proposed by Gross are presented as well as the consequences of the two main strategies reappraisal and suppression for emotion experience and physiological arousal. The second part deals with the physiological basics of heart rate variability and its measurement parameters for data analysis. The last part of the emotion regulation chapter gives an overview of different existing experimental studies in economics. In the main part of the work I analyze three different experiments of first-price sealed bid auctions regarding the connection between emotion regulation strategies, heart rate variability and bidding behavior and their results are interpreted and discussed. At last, the work and its results are summed up in a conclusion that also suggests further ideas for future research.

2 Emotion regulation

2.1 Basic principles by Gross

2.1.1 Emotion regulation strategies

To understand what happens when somebody controls his emotions it is very helpful to take a closer look at the processes of emotion regulation proposed by Gross. He states that we use different strategies to influence “which emotions we have, when we have them, and how these emotions are experienced or expressed” (Gross, 1998b). This can be reached by decreasing, maintaining or increasing one or more positive or negative aspects of emotion (Gross, 2010). The different strategies take place at different points in the emotion generative process. Therefore it can be distinguished between antecedent-focused and response-focused strategies as shown in figure 2. Antecedent-focused strategies act before emotion response tendencies are activated whereas response-focused strategies act after the arousal of emotions. These strategies can be classified in five more specific types: Situation Selection, Situation Modification, Attentional Deployment, Cognitive Change and Response Modulation. The first four types belong to antecedent-focused strategies, the last one to response-focused strategies.

illustration not visible in this excerpt

Figure 2: A process model of emotion regulation (Gross, 2003, p.282)

Situation Selection: The first strategy consists in avoiding (approaching) situations that are expected to give rise to negative (positive) emotions or have a higher chance to raise them than other situations. For example, if somebody is invited to a party with people he doesn’t like, he won’t go there.

Situation Modification: Sometimes it is impossible to avoid situations that are likely to augment negative emotions. Then, it is still possible to make an effort to modify the situation to change the feelings. In the example of the party, the person could ask if it’s possible to bring along a friend he likes. Sometimes situation modification ends up in a new situation which means that there is an overlap of the first two strategies.

Attentional Deployment: This strategy doesn’t try to change the environment. It just sets the focus on different aspects of the situation to alter its emotional impact. One possibility is to redirect the attention to one specific aspect of the situation like reading carefully the menu card at the party. Another possibility is to just distract oneself or to concentrate one’s thoughts on something else away from the whole situation like what to do the next day.

Cognitive Change: Cognitive change means to think about one aspect or the situation itself in a different way. To give a different meaning to the situation can change the person’s emotional response; this is meant by Cognitive Reappraisal. Referring to the example above, the person could see his situation in a positive way. He could think that he should be happy to be invited to a party compared to people who are never invited to any party.

Response Modulation: The last presented type of emotion regulation strategies attempts to influence emotions after response tendencies have already been initiated. It is represented on the right side of figure 2 and belongs to the response-focused strategies. One type of response modulation is Expressive Suppression which means decreasing ongoing expressive behavior by hiding one’s feelings. The person on the party for example would stay calm and not show his anger in a heated discussion.

As can be seen there are a lot of different possibilities to regulate emotions and they are often used in combination. The most researched types of emotion strategies are reappraisal and suppression representing each one example of the two emotion regulation strategies (antecedent-focused and response-focused). To investigate behavior in this work I will refer only to these two emotion regulation strategies.

2.1.2 Effects and consequences

The question is whether the two different strategies reappraisal and suppression also differ in their consequences. Reappraisal as an antecedent-focused strategy comes relatively early in the emotion generative process in contrast to suppression as a response-focused strategy where emotion has already been triggered. There exist several studies that investigate the effects and consequences of the different strategies. In one study (Gross, 1998a), participants watched a disgusting film under one of three instructions. The first group was told to watch the film objectively without emotional feeling (reappraisal), the second group had to hide their emotions so that nobody from the outside could see what their feelings were (suppression) and the third group simply watched the film (control). Both reappraisal and suppression participants showed less emotional response and behavioral signs of disgust than the control group. However, suppression didn’t decrease negative emotion experience, whereas reappraisal effectively decreased the intensity of emotional impact. Different studies confirmed the results of Gross, also with different emotions like sadness or amusement (Gross & Levenson, 1997). It’s worth noting that suppression of positive emotions led indeed to a decrease in positive emotion experience, but reappraisal increased positive experience and expression (Gross, 2002).

2.1.3 Psychophysiological measures

Using psychophysiological measures is a good method to study ongoing emotion responses to stimuli during experiments. In the experiments of Gross (1998), five measures were taken: finger pulse amplitude, finger temperature and skin conductance level representative for the activation of the sympathetic branch of the autonomic nervous system. In addition, the general somatic[1] activity and cardiac interbeat interval were measured. Suppression caused an increased sympathetic activation of the cardiovascular system, i.e. decreased finger pulse amplitude and finger temperature, and increased skin conductance level. Increases in heart rate, somatic activity and blood pressure also occurred for suppression participants. Participants using reappraisal didn’t show higher activity of physiological responding. Consequently, reappraisal is more successful in inhibiting emotions and is preferable to suppression strategies.

These results show that there is a connection between emotion regulation and physiological responses. One of the most important physiological measures is the heart rate variability which is explained in the following subchapter.

2.2 Heart rate variability (HRV)

2.2.1 Emotions and heart rhythm patterns

One might think that the brain is constantly sending neural signals to the heart and the heart is responding to them. In fact, there are more signals coming from the heart to the brain than vice versa. In addition, the heart signals have “a significant effect on brain function – influencing emotional processing as well as higher cognitive faculties like attention, perception, memory, and problem-solving” (HeartMath). During stress and negative emotions (like anger, frustration, fear, sadness, envy or disgust), cognitive functions of the brain such as memory, learning ability and effective decision-making, are inhibited which is caused by a disordered heart rhythm pattern (see figure 3). This heart activity also reinforces the emotional experience. That confirms the examined effect of suppression impairing memory in contrast to reappraisal (Gross, 2002) because suppression doesn’t lessen the emotional experience. An incoherent heart rhythm pattern reflects less synchronization of the parasympathetic and sympathetic branches of the autonomic nervous system (McCraty & Tomasino, 2004).

Positive emotional experiences (pleasure, appreciation, love) have the opposite effects – a stable, harmonious heart rhythm pattern that improves cognitive functions. This highly ordered, coherent heart rhythm pattern indicates better synchronization of the two branches.

Heart rhythm patterns illustrate different emotional states and are independent of the heart rate. That means that both coherent and incoherent patterns can appear for higher or lower heart rates (McCraty & Childre, 2010).

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Figure 3: Heart rhythm patterns during different emotional states (HeartMath)

2.2.2 Physiological basics of HRV

The heartbeat is coming from the sinoatrial node of the heart, where an electrical impulse is generated to initiate the heart muscle contraction. The sinoatrial node generates such impulses about 60-100 times per minute at rest and is influenced by the permanent control of the autonomic nervous system (ANS) which is part of the peripheral nervous system. When humans experience emotion, the ANS plays an important part in the generative process of physiological arousal. The ANS controls all organs and systems of the body and consists of two branches (beside the enteric nervous system): the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS).

The excitatory SNS activates the body’s “fight-or-flight” response (Cannon, 1982). It increases blood pressure and accelerates HR in physically or psychologically stressful situations. In the process the heart response has a delay of several seconds to increase HR due to the neurotransmission of norepinephrine released by the sympathetic neurons (Appelhans & Luecken, 2006; Thayer & Lane, 2000).

The inhibitory PNS (“rest and digest”) acts complementary to the SNS, so that different states of physiological arousal are possible. It lowers blood pressure and decreases HR when the body is in a state of emotional stability and security. The heart responds almost immediately to environmental conditions (around half a second) because of the parasympathetic regulation by acetylcholine neurotransmission (Appelhans & Luecken, 2006). Both sympathetic and parasympathetic (vagal) branches are constantly influencing cardiac activity; depending on the situation one is dominating the other.

These reciprocal changes in the activities of the autonomic branches are also occurring during breathing (Gary G. Berntson & Cacioppo, 2007): HR increases with inspiration and decreases with expiration due to the influence of the PNS that is inhibited or activated. This mechanism which is in synchrony with respiration is called respiratory sinus arrhythmia (RSA) (Yasuma, 2004) and is mediated predominantly by parasympathetic influences on the sinus node (Gary G. Berntson & al., 1997) because the parasympathetic response is fast enough to follow the respiration rhythm in contrary to the sympathetic response.

Besides respiration, there are some other factors that influence the autonomic regulation of the heart (for example cardiac output[2], humoral factors (rennin-angiotensin system (RAS)[3] ), thermoregulation[4] and blood pressure) which are marked in green in figure 4. This figure shows the important effect of the baroreflex or baroreceptor reflex on blood pressure. This mechanism regulates and maintains blood pressure. A rise in pressure stimulates baroreceptors that trigger reflex parasympathetic activation and sympathetic inhibition, with subsequent decreases in HR (Lanfranchi & Somers, 2002) which cause a decrease in blood pressure and vice versa.

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Figure 4: Schema showing the baroreflex functionality (Biocom Technologies)

The SNS and the PNS are constantly influencing the heart rate and therefore the heart rate variability (HRV) which reflects their dynamic interaction. It depends on how rapidly the ANS is able to vary the heart rate. Appelhans and Luecken (2006) say that “flexible ANS allows for rapid generation or modulation of physiological and emotional states in accordance with situational demands”.

The HRV is the variation in interbeat intervals (also called beat-to-beat interval or normal-to-normal (NN) interval), the length of time between two consecutive so-called R-spikes which indicate the heartbeats. Faster heart rate means thus shorter interbeat intervals and slower heart rate longer intervals. It is very important to note that individuals don’t have an exact regular heartbeat rhythm at rest. These changes in heart rate indicate an individual’s psychological and physiological well-being. Higher HRV is a marker of health and safety, lower HRV signifies stress and disease. Specifically in our case, higher HRV at rest means better emotional self-regulation and lower HRV poor emotion regulation (Denson et al., 2011).

2.2.3 HRV analysis

HRV measures may offer powerful tools for the clarification of relationships between psychological and physiological processes (Gary G. Berntson & al., 1997). The measurement of autonomic function which reflects the ability of emotion regulation and inhibitory capacity is a very simple process (Appelhans & Luecken, 2006). The calculation of HRV needs a continuous interbeat interval and therefore heart rate recordings that can be easily derived either from the electrocardiogram (ECG) or from pulse wave recordings. For the ECG, electrodes placed for example on the chest or on the wrist and for pulse wave recordings, photoplethysmographic optical sensors placed at the fingertip or earlobe are needed (McCraty & Tomasino, 2004). It is very important to avoid that there are no abnormal heartbeats or movement artifacts in the data as for example missing or extra R-spikes. Such artifacts can be detected and corrected by various algorithms like the software package ARTiiFACT which is a tool for processing of ECG and interbeat interval data and calculation of all basic HRV parameters (Kaufmann, Sütterlin, Schulz & Vögele, 2011).

There are two main methods (which can be subdivided again) to measure the heart rate variability: time domain and frequency domain measurements (see figure 5).

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Figure 5: Methods of measuring heart rate variability (Adinstruments)

Time-domain measures are the simplest to perform. They include NN interval and heart rate determination at any point in time (Task Force, 1996). Time-domain parameters that can be calculated are for example the mean HR, mean NN interval or the difference between the longest and shortest NN interval.

Time-domain methods can be subdivided in statistical methods and geometrical methods. Usual statistical time-domain variables calculated for long-term (usually 24h) and short-term (5 min) recordings are the standard deviation of all NN intervals (square root of variance) (SDNN), the standard deviation of the averages of NN intervals for short periods (SDANN) and the square root of the mean of the sum of the squares of differences between consecutive NN intervals (RMSSD) as well as NN50 (number of interval differences of consecutive NN intervals greater than 50ms and pNN50 (the proportion derived by dividing NN50 by the entire number of NN intervals).

Geometrical analyses estimate overall HRV as well but are inappropriate for short time analyses. They comprise the variability on the basis of geometric patterns constructed according to NN intervals like the sample density distribution of NN interval durations, the sample density distribution of differences between successive NN intervals or the Lorenz plot of NN intervals. One method is the RR triangular index, the integral of the sample density distribution of RR intervals divided by the maximum of the density distribution. Another method is the triangular interpolation of the NN interval histogram (TINN), the baseline width of the minimum square difference triangular interpolation of the maximum of the density distribution (Seyd, Ahamed, Jacob & Joseph K, 2008).

Frequency-domain measures consist of power spectral density analyses that demonstrate the distribution of variance (power) in heart rate depending on different frequencies. Frequency-domain measures can be classified as parametric and non-parametric. Both methods obtain similar results. Non-parametric calculations use in most of the cases fast Fourier transform (FFT) and have a higher processing speed. Parametric calculations are more complex and the suitability of the chosen model needs to being verified (Task Force, 1996). However, they permit an easy identification of the frequency bands and can be used for smaller time series.

Spectral components for short-term recordings are high frequency (HF) ranging from 0.15 to 0.4 Hz, low frequency (LF) ranging from 0.04 to 0.15 Hz and very low frequency (VLF) ranging from 0.0033 to 0.04 Hz. For long-term recordings there is an additional ultra low frequency band (0 - 0.0033 Hz). HF is occurring at the frequency of adult respiration and reflects parasympathetic activity due to the RSA (Appelhans & Luecken, 2006). It is considered to be a physiological indicator of effective emotion regulation like reappraisal and adaptive and flexible responding to environmental demands (Denson et al., 2011). Thus, parasympathetically mediated HRV is related to good psychological and physiological functioning (Ruiz-Padial, Sollers, Vila & Thayer, 2003). LF is derived from both parasympathetic and sympathetic activity (Lane et al., 2009) and belongs to the frequency range of the baroreflex (Eller-Berndl, 2010). The LF/HF-ratio represents the balance between sympathetic and parasympathetic autonomic influence and should be quoted with absolute values of LF and HF. Higher values of LF/HF-ratio reflect domination of the sympathetic system whereas lower values designate domination of the parasympathetic system. Lower values indicate higher autonomic flexibility (Sütterlin et al., 2011a).

The most common measures for HRV as described above are composed in table 1.

Table 1: Usual HRV Measures5

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2.3 Existing experimental studies in economics

2.3.1 The Ultimatum Game

Especially in recent years a lot of studies have investigated emotion regulatory behavior in traditional economic games with the help of psychophysiologic measures. One of these games is the Ultimatum Game for two players in which the first player has to split a given amount of money in two parts. The second player has the choice to accept (they get the money as proposed by player one) or to reject the offer (both get nothing). In relation to self-regulation it is interesting to see different reactions of participants using different emotion regulation strategies regarding unfair offers. At the same time cardiac parasympathetic activity at rest can be an indicator for decision-making and behavioral outcomes in the Ultimatum Game (Sütterlin, Herbert, Schmitt, Kübler & Vögele, 2011b). Several studies concentrated on this aspect like for example the study of Sütterlin et al. (2011b) and Vögele, Sorg, Studtmann, and Weber (2010) which both used HRV at rest as a measure of inhibitory control and self-regulation. In this part I want to focus on the second experiment because it included the examination of antecedent-focused and response-focused emotion regulation strategies proposed by Gross and described in 2.1.1. Some more aspects can be found in 2.3.5.

The participants were female adolescents (mean age 14.7 years) who played two rounds of an adapted version of the Ultimatum Game which should elicit feelings of anger (Vögele et al., 2010). In the first round they had to propose the offer and in the second round they had to respond to the offer proposed by the co-player. They thought that the co-player would be a person sitting in a room next door, but in reality, it was a computer. The offer of the computer was extremely unfair (€ 1 out of € 10) followed by a commentary which should increase their anger. The participants also had to complete a questionnaire to state their feelings and what they thought when they received the unfair offer. These thoughts helped to reproduce how the participants coped with the situation and which strategy they used, cognitive reappraisal strategies (extenuation) or anger rumination (brooding).

The results showed that participants effectively had a high level of anger during the Ultimatum Game (ruminators had a higher level than reappraisers) and that unfair offers were often rejected. These results confirm that negative emotions increase the probability of rejecting an offer (Andrade & Ariely, 2009; Bosman, Sonnemans & Zeelenberg, 2001). Physiological measures like HRV and HF indicated that parasympathetic (vagal) activation under resting conditions was higher for cognitive reappraisers than for anger ruminators. While making the decision, ruminators showed decelerating heart rate facing the unfair offer and increasing heart rate after the commentary. In opposition to ruminators, reappraisers showed first a short period of increasing heart rate, then decreasing heart rate and the last few seconds increasing heart rate again.

These findings reveal “differences in both tonic and phasic cardiac autonomic regulation in relation to emotion regulation” (Vögele et al., 2010). Tonic regulation regards the general ongoing emotional state whereas phasic regulation responds to short bursts of sympathetic activity usually after a special event.


[1] relating to the body

[2] The total volume of blood pumped by the heart per minute

[3] Hormone system that controls blood pressure and water balance

[4] Maintenance of a constant body temperature independent from the environmental temperature

[5] variables used in this work

Excerpt out of 73 pages


Emotion Regulation and Bidding in Auctions
Karlsruhe Institute of Technology (KIT)  (Institute of Information Systems and Management (IISM))
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emotion regulation, auctions, heart rate variability, Auktion, Herzratenvariabilität, Emotionsregulation
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Anonymous, 2012, Emotion Regulation and Bidding in Auctions, Munich, GRIN Verlag, https://www.grin.com/document/300838


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