**Author details**

Anastasiia Ledyaeva\*, Sergey Klauchek and Mikhail Ledyaev Volgograd State Medical University, Volgograd, Russia

\*Address all correspondence to<EMAIL_ADDRESS>
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**47**

*Circadian Rhythm of Blood Pressure in Children and Adolescents*

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*DOI: http://dx.doi.org/10.5772/intechopen.87112*

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*Circadian Rhythm of Blood Pressure in Children and Adolescents DOI: http://dx.doi.org/10.5772/intechopen.87112*


Combined with our results, however, these results suggests that the diminished reliance on mentalization and/or counterfactual information to *dynamically update* behavior may reflect core changes in the cognitive processing of social information that occurs over the aging process. This is as opposed to strategic reasoning, which refers to the *static* inferential process of guessing what others will do without any prior contractual agreement, which may well be preserved during aging. This hypothesis is consistent with previous findings of behavioral deficits in elderly

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patients in the"Iowa Gambling Task" (IGT; Bechara et al., 2000), as well with what is known about degeneration of the dopaminergic circuits and mPFC that supports value-based decision-making. In particular, longitudinal studies have found frontal lobes suffered the most drastic loss of volume as assessed through MRI (Resnick et al., 2003).

In contrast, we speculate that static reasoning capacities in the elderly may be partially preserved by reallocation of processing resources from other brain regions. Such compensatory processes at the neural level have been found across a variety of cognitive functions, including episodic retrieval and visual perceptual attention, and which occur even in the face of global declines in neural integrity (Davis et al., 2008). For example, there is abundant evidence that older adults compensate for declines in bottom-up sensory processing by over-recruitment of top-down processes mediated by PFC (Davis et al., 2008; Dennis and Cabeza, 2008).

In the case of social cognitive functioning, there is substantial evidence that, during development, the so-called "mentalizing system" – consisting of the anterior mPFC, the posterior superior temporal sulcus at the temporoparietal junction (pSTS/TPJ), and the anterior temporal lobe (ATL) – undergo substantial changes in their functional response to social information such as mental states (Paus, 2005; Blakemore, 2008; Burnett et al., 2009). Adolescents have been shown, for example, to exhibit greater activity within the mPFC than do adults in social cognition tasks (Burnett et al., 2009). In contrast, we know much less about how neural responses change over adulthood in the social cognition and behavior (Castelli et al., 2010; Beadle et al., 2012; Moran et al., 2012), and whether they might have compensatory functions that have been documented for other cognitive functions. A more complete account of these age-related changes, however, is only possible with a proper characterization of the computational and structural integrity of the underlying neural systems and their interactions.

#### **ACKNOWLEDGMENTS**

This research was supported by the Risk Management Institute and a CEDA Pilot grant from the University of California, Berkeley (Ming Hsu).

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 30 May 2012; accepted: 19 August 2012; published online: 21 September 2012.*

*Citation: Zhu L, Walsh D and Hsu M (2012) Neuroeconomic measures of social decision-making across the lifespan. Front. Neurosci. 6:128. doi: 10.3389/fnins.2012.00128*

*This article was submitted to Frontiers in Decision Neuroscience, a specialty of Frontiers in Neuroscience.*

*Copyright © 2012 Zhu, Walsh and Hsu. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## Social information and economic decision-making in the ultimatum game

## **Celia Gaertig1,2, Anna Moser 1,3, Sonia Alguacil 1 and María Ruz 1\***

1 Department of Experimental Psychology, University of Granada, Granada, Spain

2 Laboratory for Biological and Personality Psychology, Department of Psychology, University of Freiburg, Freiburg, Germany

3 Department of Cognitive, Perceptual and Brain Sciences, University College London, London, UK

#### **Edited by:**

Ming Hsu, University of California Berkeley, USA

#### **Reviewed by:**

George Christopoulos, Nanyang Technological University, Singapore Claudia Civai, University of Minnesota, USA

#### **\*Correspondence:**

María Ruz, Department of Experimental Psychology, University of Granada, Campus Universitario Cartuja s/n, 18071 Granada, Spain. e-mail<EMAIL_ADDRESS>
The present study tested how social information about the proposer biases responders' choices of accepting or rejecting real monetary offers in a classic ultimatum game (UG) and whether this impact is heightened by the uncertainty of the context. Participants in our study conducted a one-shot UG in which their responses had direct consequences on how much money they earned. We used trait-valenced words to provide information about the proposers' personal characteristics.The results show higher acceptance rates for offers preceded by positive words than for those preceded by negative words. In addition, the impact of this information was higher in the uncertain than in the certain context. This suggests that when deciding whether or not to take money from someone, people take into account what they know about the person they are interacting with. Such non-rational bias is stronger in an uncertain context.

**Keywords: ultimatum game, decision-making, social information, uncertainty**

## **INTRODUCTION**

Within the emerging field of judgment and decision-making, it is broadly accepted that humans are not purely rational decisionmakers (see Camerer, 2003). A recent line of research within this field tries to capture the nature of decision-making in social contexts. This is particularly interesting as many of our everyday choices involve or affect other people.

Regarding the aspects that influence such decisions, different studies stress the importance of emotions as a biasing factor. It has been shown that displayed positive and negative facial expressions (e.g., Scharlemann et al., 2001; Ruz and Tudela, 2011) as well as induced emotions unrelated to the task (Harlé and Sanfey, 2007) influence decision-making in inter-personal interactions. Further aspects that have been found to influence decision-making include the physical attractiveness and the gender of people with whom we interact (e.g., Solnick and Schweitzer, 1999; Solnick, 2001; Eckel and Grossman, 2008).

As another clear biasing factor, social information has been shown to have an effect on economic choices in social contexts with high degrees of uncertainty, such as those in the Trust Game (Delgado et al., 2005). In an iterated Trust Game, trading partners described as morally praiseworthy were trusted more often than those with neutral or untrustworthy moral character, even when the descriptions had no predictive value regarding the actual behavior of the partners. As the reciprocity of the unknown partner in this game has direct consequences on the monetary outcome of the truster, it seems useful to rely on any relevant information available to guide trust choices. This matches previous data in non-social contexts showing that uncertainty increases the value of information. For example, people might be more disposed to being influenced when they lack complete knowledge of the situation (e.g., Behrens et al., 2007; Rushworth and Behrens, 2008) and might try to make use of any additional piece of information they can gather. As a practical example, when making decisions on the stock market, investors facing unstable prices are more receptive to new tips than during stable periods (Schachter et al., 1985).

There are other social situations in which the degree of uncertainty is smaller than in the Trust Game, such as when making choices of accepting or rejecting offers in the Ultimatum Game (UG; Güth et al., 1982). In this game two people interact to divide a sum of money between them. One of them, the *proposer*, receives a certain amount of money. He has to split it into two parts, one for him and one for his counterpart, the *responder*. The responder then can either accept or reject the proposal. If he accepts it, both receive their part; if he rejects it, neither the proposer nor the responder gets any payoff. For the responder the degree of uncertainty of this situation is low, seeing that he reacts to a given decision of the proposer in form of a monetary offer.

From the economic point of view, the self-interested, incomemaximizing *homo economicus* should accept every kind of offer, no matter how little it is (Nash, 1950). However, such predicted behavior is not confirmed in experimental settings where small offers (of 20% or below of the initial amount) are rejected about half of the times (Camerer, 2003). Irrational rejection of unfair offers in the UG may be explained by several factors, such as inequity aversion (Fehr and Schmidt,1999) and emotions accompanying the perception of unfairness. Responders often feel wounded pride and anger when facing unfair offers and tend to punish their selfish game partner favoring emotional satisfaction over money gains (Pillutla and Murnighan, 1996). Physiological (van't Wout et al., 2006) and neuroimaging studies (Sanfey et al., 2003) support the important role of emotions in the UG, showing, for example, that emotionally relevant brain regions, such as the right anterior insula, are activated when participants are faced with unfair offers (Sanfey et al., 2003).

As the responder in the UG finds himself in a situation where the decision of the proposer has already taken place, there is no obvious reason why his choices should change depending on the information he has about the person he interacts with. However, even in such a certain context social information about the proposer seems to influence decision-making.

Using a modified version of the UG, Ruz et al. (2011) showed that personal descriptions of game partners biased decisions to the same set of offers. Offers preceded by negative words were rejected with higher probability than those preceded by positive words. In addition, rejection responses were faster after negative words, whereas acceptances were faster following positive words, which suggests that the social information primed action tendencies. Thus, even though the words did in no manner predict how fair the following offer was going to be, social information regarding the partner affected the decisions of participants in this game. Furthermore, these authors introduced a manipulation of the uncertainty of the social situation and found that the social bias had a much larger effect when the context of the game was uncertain. Some characteristics of this study, however, limit the scope of the results. First, Ruz et al. (2011) used a modification of the UG instead of the original task setting. In their version, the difference between the two parts of a split was either one (fair offers) or four (unfair offers) and the responder's part of the split could be either higher or lower than their partner's amount. Thus, offers could be either convenient or inconvenient for the participant. Furthermore, to enable measurement of response times, they required participants to take their decision within a time limit of 1500 ms. When participants did not respond on time, they saw a message stating that the higher amount of the split would be added to their partner's earnings. This leaves open the question of whether similar results would be obtained in a version closer to the classic UG. Additionally, they did not pay real money to participants, and thus it could be claimed that the social information biased responses because participants did not have anything at stake.

Two recent studies solve part of these problems. Campanhã et al. (2011) used the classic UG and demonstrated that friendship with the proposer modulated the choices made by the responder in the game. More specifically, responders rejected unfair offers less frequently when the proposer was believed to be a friend rather than an unknown person. However, as several rounds were played with the same partner, it is not sure that the responders' choices reflected responses to a single offer instead of bargaining behavior. It must also be noted that no real money was offered to participants. Furthermore, interactions with a friend can always be affected by the long-term relation we hold with this person, which may have affected the results found by Campanhã et al. (2011).

Another study (Marchetti et al., 2011) showed that the type of information about the proposer provided to the responder has an influence on decision-making in the UG. Most interestingly, they found an interaction between the psychological description of the partner and the fairness of the offer: a negative (selfish) description of the proposer led to a decreased acceptance rate of fair offers, while a positive (generous) description led to an increased acceptance rate of unfair offers. As this study employed

one-shot interactions with unknown partners, concerns regarding long-term interactions or even friendship do not arise. However, as in the game by Ruz et al. (2011) and in the study of Campanhã et al. (2011), participants in this study did not receive money in accordance with their decisions.

Thus, it still has to be tested whether social information regarding the partner in a classic UG biases people's decisions to offers of real money, which was the goal of the present study. We conducted a classic computerized, one-shot UG and used traitvalenced words to describe the moral characteristics of otherwise unknown partners. As previous results indicate that the level of uncertainty modulates the scope of the biasing information in a modified UG (Ruz et al., 2011), we included a manipulation of uncertainty to explore whether the level of uncertainty also affects responses in the classic UG.

Participants of our experiment played the role of the responders and received either fair or unfair offers from several different proposers represented by the computer. Following the findings of the classic UG it was hypothesized that more fair than unfair offers would be accepted. As a description of the partners' characteristics in each trial, each offer was preceded by a word with positive or negative valence highly linked to morality and trustworthiness (see **Table 1**). It was hypothesized that acceptance rates would be biased by this social information, with higher acceptance rates for offers preceded by positive than for those preceded by negative words. Additionally, participants had either full or incomplete information about the outcome of their choices, which modulated the uncertainty of the game. In the certain condition, participants were informed of which part of the split corresponded to them, while this information was not given in the uncertain condition. It was hypothesized that the influence of the valence of the word would be higher in the uncertain context. Experiment 1 confirmed these hypotheses. Experiment 2 showed that personal information only influenced choices when it was *attributed* to the partners in the game.

#### **EXPERIMENT 1**

Experiment 1 manipulated the level of uncertainty (uncertain vs. certain), the type of offer proposed by the partner in the trial (fair

**Table 1 | List of words selected as stimuli in the study and acceptance rates of offers depending on the word that preceded the offer.**

Standard deviations are given in parentheses.

vs. unfair) and the valence of the word preceding the offer (positive vs. negative).

## **MATERIALS AND METHODS**

#### **Participants**

Thirty-six native Spanish-speaking, right-handed students from the University of Granada participated in the study (23 female, 18–27 years, average 21.5). All participants signed a consent form approved by the Department of Experimental Psychology of the University of Granada. In exchange for their participation in the study, participants were paid. The payment amount depended on their earnings during the game task and ranged from about 3–6 Euros.

## **Stimuli**

Sixteen trait-valenced words were selected from the Spanish translation of the Affective Norms English Word database (ANEW; Redondo et al., 2007) as stimuli in the study. The words selected provided moral and trustworthiness information and had either a positive (in average 7.5, *SE* = 0.5) or a negative valence (in average 1.9, *SE* = 0.3). Positive and negative words were equated regarding number of letters and frequency of use (average number of letters: 6; average frequency: 21.72; all *p*s > 0.390). The English translation of the words used is listed in **Table 1**.

#### **Procedure**

First, an introduction explaining the rules of the UG was given to the participants. They were told that they were going to play the UG in the role of the responder and were going to receive offers that other participants had made in previous experiments of the lab. To enhance the plausibility of this cover story, participants completed a short questionnaire in which they themselves generated offers for 16 anonymous partners. For each partner they were asked to decide how to divide 10 Euros into two parts, one for them and the other one for their partner.

Before conducting the game, participants were informed that they were playing with actual money, which was to strengthen their motivation to make real decisions. They were informed that one point earned in the game was to be exchanged for 1.5 cents of Euro. To avoid possible influences of previous reciprocation, they were told that on each trial they were going to play with a different partner, who was never the same across the game.

The initial amount of the proposer was always 10 Euros, and the split proposed to the responder was presented in the middle of the screen. To every participant, the same set of splits including two kinds of fair offers (5/5, 4/6) and three different kinds of unfair offers (1/9, 2/8, 3/7) was presented. These types of offers match the range of offers that humans normally propose in the role of the proposers in the UG.

In total, participants received 128 offers and they had to accept or reject each of them by pressing the number 1 or 2 (counterbalanced across participants) of the computer keyboard. If they accepted the offer, their part of the split was added to their earnings and their partner for the trial received the other part. If they rejected the offer, no transaction was carried out.

Additionally, each offer was preceded by a word. Participants were told that the words represented personal descriptions of their partner and that these characteristics had been obtained through questionnaires completed by the same participants that made the offer proposals in previous experiments. The same words were presented in a different random order to each participant. Half of the words had a positive valence and the other half had a negative one (see **Table 1**). In reality, the valence of the words was not related in any way to the type of offer, as each word was followed equally often by fair and unfair offers.

To test whether the uncertainty of the context influenced decision-making in the classic version of the UG, the whole task consisted of two blocks (order counterbalanced across participants) differing in the information provided to participants. In the *certain* block, as in the original game, participants knew which part of the split would be added to their earnings if they accepted the offer. Therefore, for each split the two numbers were presented in different colors (green and red) and participants were told which of the colors corresponded to them (counterbalanced across participants). Accordingly to the range of offers normally proposed by humans in the game, the participants' part of the split always consisted in the smaller or equal (5/5 offer) number. In the *uncertain* block, in contrast, participants did not know which part of the split would be added to their earnings, as both numbers were presented in black. Therefore they lacked that part of the information.

The experiment was conducted using a PC running E-Prime software (Schneider et al., 2002). Each trial (see **Figure 1**) started with a fixation cross presented for 1500 ms (+ ; 0.4˚) in the center of the screen. Following this, the word (average 2.5˚) was displayed for 200 ms, and then, the fixation point was presented for another 700 ms. Subsequently, the offer (1.5˚), consisting of two numbers separated by a slash symbol, appeared in the center of the screen until the participant made the response. Following the decision of the participant, the next trial began. The whole experiment consisted of 128 trials and had an approximate duration of 10 min.

#### **RESULTS**

The acceptance rate measured in percentage of accepted offers was analyzed by a 2 (uncertainty: uncertain vs. certain) × 2 (offer:

fair vs. unfair) × 2 (valence of the word: positive vs. negative) multifactorial ANOVA.

On average participants accepted 62.1% of all offers across the experiment (**Figure 2**). There was a main effect of the fairness of the offer (*F*1,35 = 86.25, *p* < 0.001), as fair offers were accepted more often (*M* = 85%, *SE* = 17%) than unfair ones (*M* = 39%, *SE* = 25%). Decisions were also influenced by the valence of the word (*F*1,35 = 33.64, *p* < 0.001). Offers were accepted more often when they were preceded by positive words (*M* = 74%, *SE* = 16%) than by negative words (*M* = 50%, *SE* = 23%). **Table 1** shows the acceptance rates of offers separately for each trait-valenced word.

In addition, a significant interaction between uncertainty and the fairness of the offer was found (*F*1,35 = 12.38, *p* < 0.01). In the certain condition, the effect of the offer was higher (51%) than in the uncertain condition (39%). Furthermore, unfair offers were accepted more often in the uncertain (44%) than in the certain context (35%; *F*1,35 = 9.65, *p* < 0.01), whereas there was no significance difference between the acceptance rate of fair offers in both contexts (83 vs. 86%, *F*1,35 = 3.13, *p* = 0.086). Finally, as predicted there was a significant interaction between uncertainty and valence of the word (*F*1,35 = 8.01, *p* < 0.01). The effect of the valence of the word was higher in the uncertain (28%) than in the certain condition (20%).

In the analysis described above all offers are included (5/5, 4/6, 3/7, 2/8, and 1/9) for both certain and uncertain contexts. The experiment included the fair offer 5/5 to mimic the range of offers

normally proposed in the classic UG. Nevertheless, it is clear that when facing this offer participants knew that they would get five points, both in the certain and uncertain blocks. Therefore, we conducted a second analysis excluding the 5/5 offers to ensure that our main results were not affected by this. This analysis replicated all previous results.

### **DISCUSSION**

The results of Experiment 1 confirm our initial hypotheses. First of all and in agreement with typical findings in the UG (Camerer, 2003), the fairness of the offer influenced participants' choices in a major way as fair offers were accepted more often than unfair offers.

With regard to the main question of interest of this study, our results show that trait-valenced words influence decision-making in the classic UG in which the decisions of participants influenced how much money they earned. Offers preceded by positive traitwords were accepted more often than those preceded by negative trait-words. Crucially, we show that this bias exists in a classic UG in which long-term effects were not present, as participants were interacting only once with a partner not personally known to them. Furthermore, participants in our study knew that their aggregate decisions of each trial determined the amount of money that they were going to earn. Therefore, they were motivated to take their decisions seriously. However, their choices of accepting or rejecting an offer were influenced by the information they had

about their respective partners on each trial. Thus, they did not act purely rational, but took into account what they knew about the other person to accept or not the money. The social information biased choices, although objectively trait-valenced words and offers were not associated, insofar as positive and negative words were paired with the same set of offers. Due to such lack of association between words and offers, a learning effect can be excluded.

Two aspects might have influenced participants' reactions in our study using the classic UG. First, it is possible that the subjective perception of fairness for all kinds of offers was biased by the social information provided. Offers made by a negatively described person might have been perceived as less fair than those made by a positively described person. Research on the UG suggests that responders in the UG reject unfair offers due to the perceived unfairness and the negative emotions arising from this perception (Pillutla and Murnighan, 1996; Sanfey et al., 2003). When providing a negative description, responders' attention may have focused on the negative aspects of the offer (e.g., the proposer assigns more to himself than to me) rather than on the possible gains, as they were already expecting an unfair offer. Additional to and in congruence with biased subjective perception, negative emotional reactions may have been stronger in this condition, which would have led to lower acceptance rates. In the future, it could be useful to include emotional measures such as skin conductance, to directly evaluate the role that emotional reactions may play in the current paradigm.

Another accompanying explanation places the bias in a later stage of decision-making. From this perspective, offers would be perceived in the same manner, and the biasing effect would take place afterward, during the decision stage to punish the partner or not. Participants punished partners tied to a negative description more than those associated to positive characteristics. This explanation is consistent with real life experience, as people normally behave more nobly toward friendly persons, even when the chances of meeting the same person again are unlikely. We will take a closer look at the question of how to decide between these two approaches in the general discussion.

In addition to the offer fairness and the valence of the words, we manipulated the certainty of the context (uncertain vs. certain) as a third independent variable. In the uncertain context responders lacked the information about which part of the offer was assigned to them. Therefore, they could not judge whether the offer was advantageous for them or not, which is of particular relevance in the face of unequal splits.

We found that offer fairness interacted with uncertainty, as unfair offers were accepted more frequently in the uncertain than in the certain context, while the acceptance rate of fair offers did not differ in both conditions. The higher acceptance rate of unfair offers in the uncertain condition was not predicted. However, the limited possibility of objectively judging offers as convenient or inconvenient and thus, a possible lower arousal of negative emotions in the face of unfair offers, might explain this effect (see also Pillutla and Murnighan, 1996). It is also possible, that in the uncertain context responders simply feared the rejection of a convenient split and therefore accepted more offers consisting of an unequal split.

Finally and as predicted, uncertainty modulated the weight of the social information. The influence of the words was not restricted to uncertain situations, but it was higher in this context. As responders were not able to judge the convenience of the offer in the uncertain context, they might have weighted the information they received more highly and used it to generate expectations about the offer.

The results clearly show that the valence of the information about the proposer influences decisions made by participants in the classic UG. However, an alternative and less appealing explanation of our results could be that the presentation of positive and negative words primed participants with a valence-consistent mood in an automatic manner. To rule out that this is the case in our findings, we conducted Experiment 2.

A possible control experiment to study the automatic effect that valenced words may have on acceptance rates would be to use non-trait-words as primes, matched in valence and arousal ratings to the words used in Experiment 1. However, such a control would entail a change both in the words and the *instructions* (as words can no longer be attributed to the personal characteristics of the partners), which would make the interpretation of the results difficult. An alternative option, which is the one we chose for our control study, is to use exactly the same items but to change the instructions regarding the social meaning of the words in the game.

## **EXPERIMENT 2**

We conducted a second experiment to rule out the possibility that the impact of the words in Experiment 1 could be explained by an "automatic" priming effect driven by the mere presentation of words with high positive and negative connotations.

In Experiment 2 we told participants that the computer presented the words at random before each offer, and thus that they had nothing to do with the person who initially proposed the offer. Except for this minor change in the whole set of instructions, the experimental design was exactly the same as in Experiment 1. It was hypothesized that if the mere presence of the words, regardless of their association to the partners in the game, generates priming, results from Experiment 2 should be quite similar to those of Experiment 1. In contrast, if the key manipulation is the association of the words with the personality characteristics of the people we interact with, decision-making should not be influenced by words in the current experiment.

## **MATERIALS AND METHODS**

**Participants**

Thirty-six native Spanish-speaking students from the University of Granada participated in the study (27 female, 18–38 years, average 23.3). All participants signed a consent form approved by the Department of Experimental Psychology of the University of Granada. In exchange for their participation in the study, participants were paid. The payment amount depended on their earnings during the game task and ranged from about 3–6 Euros.

#### **Stimuli and procedure**

Stimuli and Methods were the same as in Experiment 1 with the exception that participants received a different instruction regarding the meaning of the words preceding each offer. They were told that the words were randomly presented by the computer program and that they were in no manner linked neither to the offers, nor to their partners in the game. As in Experiment 1, we manipulated the variables uncertainty (uncertain vs. certain), offer (fair vs. unfair), and valence of the word (positive vs. negative).

#### **RESULTS**

On average participants accepted 65.8% of all offers across the experiment (**Figure 3**)*.* There was a significant main effect of offer fairness (*F*1,35 = 67.84, *p* < 0.001), as fair offers were accepted more often (*M* = 89%, *SE* = 18%) than unfair ones (*M* = 42%, *SE* = 28%). The uncertainty of the context also influenced participants' choices. Offers in the uncertain context were accepted more often (*M* = 69%, *SE* = 16%) than in the certain context (*M* = 63%, *SE* = 18%), *F*1,35 = 8.82, *p* < 0.05. Furthermore, there was a significant interaction between these two variables (*F*1,35 = 11.13, *p* < 0.01). The effect of the offer in the certain condition was higher (55%) than in the uncertain condition (40%). Again, whereas the acceptance rate of fair offers did not differ significantly in both contexts (89 vs. 90%, *F*1,35 = 0.14, *p* = 0.713), unfair offers were accepted more often in the uncertain (49%) than in the certain context (35%; *F*1,35 = 16.38, *p* < 0.001). There was neither a significant main effect of the valence of the word, nor any other interaction (all *p*s > 0.28).

#### **DISCUSSION**

As it was the case in Experiment 1, in Experiment 2 participants' choices were influenced by the fairness of the offer. In contrast to

Experiment 1, however, participants' choices were not affected by the words and there was no interaction between block uncertainty and word-valence. Hence, the mere presentation of valenced words does not prime action tendencies that lead participants to modify their acceptance decisions. This result strongly suggests that the key element for such biasing to occur is the link of the words to social characteristics of the partners in the game.

It might be argued that given that the instructions clearly told participants that the words were not related with the subsequent offers they did not pay attention to this information, which would explain the lack of effect of the words on acceptance decisions. Withdrawing attentional resources is indeed a normal consequence of deeming something as irrelevant to the task at hand (e.g., Driver, 2001), and thus it is likely that this took place in our experiment. Future studies should test the level of processing accrued by irrelevant words in this procedure by means of both explicit and more implicit memory tests (e.g., Ruz and Fuentes, 2009) as well as with brain imaging techniques (e.g., Ruz et al., 2005).

## **GENERAL DISCUSSION**

The aim of the present study was to determine the influence of social information about people with whom we interact in a classic UG and to test whether such impact was modulated by the uncertainty of the context. We showed that positive and negative trait-words influenced acceptance rates to the same set of offers in a one-shot UG with unknown partners in which participants earned money, and that this effect was higher in the uncertain context. As Experiment 2 showed, the key aspect for the influence

of social information was the link between trait-valenced words and the characteristics of the proposer.

Our results extend the findings of the study of Ruz et al. (2011), which used a modified version of the UG instead of the original game. Responders in our current experiment received a single offer from different anonymous partners and thus no longterm strategies can explain their behavior, as it might be the case when playing several rounds of the UG with the same partner or when interacting with a friend (Campanhã et al., 2011). As participants accumulated money with each accepted offer, their final payment depended directly on their choices in the game. Showing that, nevertheless, social information and offer fairness influenced acceptance rates of such monetary offers, the present study nicely complements recent reports using the UG without money payment (Campanhã et al., 2011; Marchetti et al., 2011; Ruz et al., 2011). Furthermore and in addition to previous studies, the manipulation of uncertainty allowed us to test how this variable modulates the impact of the social information.

It could be argued, however, that the payment associated to each offer was too small, as one point earned in the game was exchanged for 1.5 cents. Given the small amount participants were able to earn with each accepted offer, they still might not have been motivated to take the decisions seriously, and this could have led them to take social information into consideration. Perhaps, if the outcome had been higher they might have weighted their own and the others' outcome in each trial more. However, previous studies using the UG showed that this explanation is unlikely, as it is commonly found that raising the stakes to a large amount has only a weak impact on rejection rates (Camerer, 2003). Although it is always possible to claim that bigger amounts of money could obliterate a positive result, our results show that by using payments within common ranges used in the UG we can prove that people take into account their impressions of others to accept or reject their monetary offers.

Other limits regarding the experimental setting nevertheless persist. To assure an adequate control, the experiment was conducted in the laboratory and monetary offers were presented through the computer. In addition, every participant interacted only once with each partner to avoid effects of reciprocity. One of the drawbacks of this artificiality is the caution it imposes regarding the generality of the effects to less artificial, daily life situations. Note, however, that our design replicates basic phenomena found in many previous studies, such as the rejection of unfair offers even when they are beneficial to the participants in economic terms.

On the other side, the experimental setting of the current study provides clear benefits for the design of a research study to explore the neural mechanisms underlying the biasing effect of social information. Previous studies employing the ERP methodology (Boksem and De Cremer, 2010; Campanhã et al., 2011) suggest that fair and unfair offers are perceived differently, and that this effect takes place at a relatively early stage of processing.Campanhã et al.'s (2011) ERP results further indicate that the medial frontal negativity responds to social distance, as its polarity is reversed

#### **REFERENCES**

Behrens, T. E.,Woolrich, M. W.,Walton, M. E., and Rushworth, M. F. (2007). Learning the value of information in

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Boksem, M. A. S., and De Cremer, D. (2010). Fairness concerns

when the offer is made by a close friend rather than an unknown proposer. In the future, it would be interesting to further explore the biological basis of inter-personal decisions and to analyze how the inclusion of social information modulates these mechanisms. To date, it remains unclear whether the effects are driven by a rather automatic kind of processing, which leads to a different perception of the offer after the presentation of valenced information, or by a controlled process at a later stage of decision-making. Further studies using electrophysiological methods could provide a closer look on the cognitive processes occurring when perceiving the offer, dependent on the valence of the social information, and, thus, could help exploring at which level of information processing social information affects decision-making.

Additional research is also needed to explore the relation between the influence of social information on responders' choices and a possible expectancy or framing effect. Sanfey (2009) showed that expectations of fairness have a strong influence on responders' decisions in the UG. The importance of framing effects is also discussed in Marchetti et al. (2011) to explain that positive and negative descriptions of the proposer bias the acceptance of offers in the UG. This study used psychological attributes very closely linked to fairness expectations (generous vs. selfish). Our results complement their findings, showing that different positive and negative descriptions of the partner in the UG influence decisionmaking. This influence exists although objectively the design of the task does not associate the trait-valenced words with the offers, as positive and negative items are presented equally often with fair and unfair offers. Future studies should test expectation generation more directly to help fully understand the relation between social information and a framing effect.

Other future lines of research could include the combination of an emotional introduction and the presentation of social information. While induced negative emotions are correlated with higher rejection rates of unfair offers (Harlé and Sanfey, 2007), in the present study the same effect was shown regarding negative social information. It would be interesting to link both approaches, testing whether the introduction of positive or negative moods biases the impact of social information.

Overall, our results show once again that human behavior is motivated by more than pure income maximization. The opinion we hold regarding the moral characteristics of people with whom we interact modulates our tendencies to accept or reject the money that they offer us. The uncertainty of the context furthermore has an effect on how we make use of this information. The present study provides further insights into the complex decision-making processes during inter-personal interactions and gives way to new questions for future research using economic games.

#### **ACKNOWLEDGMENTS**

Financial support to this research came from the Spanish Ministry of Science and Innovation through a "Ramón y Cajal" research fellowship (RYC-2008-03008) and grants PSI2010-16421 and SEJ2007.63247 to María Ruz.

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Camerer, C. F. (2003). *Behavioral Game Theory: Experiments in Strategic Interaction*. Princeton: Princeton University Press.

Marchetti, A., Castelli, I., Harlé, K. M., and Sanfey, A. G. (2011). Expectations and outcome: the role of proposer features in the ultimatum game. *J. Econ. Psychol.* 32, 446–449.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 29 March 2012; accepted: 20 June 2012; published online: 06 July 2012. Citation: Gaertig C, Moser A, Alguacil S and Ruz M (2012) Social information and economic decision-making in the ultimatum game. Front. Neurosci. 6:103. doi: 10.3389/fnins.2012.00103*

*This article was submitted to Frontiers in Decision Neuroscience, a specialty of Frontiers in Neuroscience.*

*Copyright © 2012 Gaertig , Moser, Alguacil and Ruz. This is an openaccess article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## Toward an affective neuroscience account of financial risk taking

## **Charlene C.Wu1\*, Matthew D. Sacchet 1,2 and Brian Knutson1,2**

1 Department of Psychology, Stanford University, Stanford, CA, USA

2 Neurosciences Program, Stanford University School of Medicine, Stanford, CA, USA

#### **Edited by:**

Kerstin Preuschoff, École Polytechnique Fédérale de Lausanne, Switzerland

#### **Reviewed by:**

Bruno B. Averbeck, National Institute of Mental Health, USA Shunsuke Kobayashi, Fukushima Medical University, Japan

#### **\*Correspondence:**

Charlene C. Wu, Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall, Building 420, Stanford, CA 94305, USA. e-mail<EMAIL_ADDRESS>
To explain human financial risk taking, economic, and finance theories typically refer to the mathematical properties of financial options, whereas psychological theories have emphasized the influence of emotion and cognition on choice. From a neuroscience perspective, choice emanates from a dynamic multicomponential process. Recent technological advances in neuroimaging have made it possible for researchers to separately visualize perceptual input, intermediate processing, and motor output. An affective neuroscience account of financial risk taking thus might illuminate affective mediators that bridge the gap between statistical input and choice output. To test this hypothesis, we conducted a quantitative meta-analysis (via activation likelihood estimate or ALE) of functional magnetic resonance imaging experiments that focused on neural responses to financial options with varying statistical moments (i.e., mean, variance, skewness). Results suggested that different statistical moments elicit both common and distinct patterns of neural activity. Across studies, high versus low mean had the highest probability of increasing ventral striatal activity, but high versus low variance had the highest probability of increasing anterior insula activity. Further, high versus low skewness had the highest probability of increasing ventral striatal activity. Since ventral striatal activity has been associated with positive aroused affect (e.g., excitement), whereas anterior insular activity has been associated with negative aroused affect (e.g., anxiety) or general arousal, these findings are consistent with the notion that statistical input influences choice output by eliciting anticipatory affect. The findings also imply that neural activity can be used to predict financial risk taking – both when it conforms to and violates traditional models of choice.

**Keywords: neuroeconomics, neurofinance, FMRI, accumbens, striatum, insula, activation likelihood estimation, meta-analysis**

## **INTRODUCTION**

Imagine a world where people act as computers, consistently taking in, analyzing, and responding to all of their sensory impressions. These "rational" actors should not show volatile and inconsistent changes in preferences, and so their future choices should be predictable based on their past behavior. Such a world may be hard to imagine, because it is not the world we live in. Instead, people often show sudden, pronounced, and inconsistent changes in choice. For instance, although most people will never win the lottery or lose a limb, the same individuals will often pay a high premium both for a tiny chance to hit the jackpot as well as to compensate for the unlikely possibility of dismemberment. To explain financial risk taking, decision theorists have either appealed to the objective statistical properties of financial options or to the subjective emotional experience of individuals. Do these distinct accounts conflict with or complement each other, and can they be reconciled?

#### **ECONOMIC AND FINANCE MODELS OF RISK TAKING**

Traditional economic models assume that people seek to maximize value. Blaise Pascal and Pierre de Fermat historically concluded that the expected value of uncertain gambles could be calculated by multiplying the magnitude of the gamble outcomes by their probability. Thus, they mathematically defined "expected value" as the mean (or the first statistical moment) of repeated outcomes. In economics,*expected value* (and its close cousin *expected utility*) provide a foundational guide to choice by providing a common metric that individuals can use to compare different and diverse financial options (von Neumann and Morgenstern, 1944). One implication of preferences for expected value is that people should not only prefer gambles with the best outcomes, but also those with more chances to obtain a good outcome. Beyond expected value, financial theorists have additionally and separately considered the role of risk, which can be mathematically defined as variance (or the second statistical moment) of repeated outcomes (Markowitz, 1952). Resulting *mean-variance* financial models further assume that while people are attracted to expected value, they are instead repelled by risk. One implication of preferences against risk is that people should prefer gambles with relatively steady outcomes over those with more variable outcomes.

Behavioral research, however, suggests that neither expected value nor mean-variance models fully account for individuals' financial risk taking (Edwards, 1954). As a result, some theorists have suggested that anomalies in choice (e.g., the lack of diversity in investors' portfolios) might result from preferences for large yet

improbable outcomes, which has been mathematically defined as skewness (or the third statistical moment; Mitton and Vorkink, 2007). One implication of preferences for skewness (according to some theories) is that people might prefer "long shot" gambles (e.g., those with high magnitude but low probability outcomes) over others. Despite some behavioral evidence that skewness can influence preferences (Kraus and Litzenberger, 1976; Coombs and Lehner, 1981), either by enhancing (Menezes et al., 1980) or interacting with risk (Alderfer and Bierman, 1970; Chiu, 2005), only a few models of financial risk taking consider skewness. For instance, cumulative prospect theory (Tversky and Kahneman, 1992) and rank-dependent utility models (Quiggin, 1982) have attempted to account for skewness by overweighting large but unlikely positive and negative outcomes. In doing so, however, these models sacrifice their ability to explain tolerance for variance (Levy and Levy, 2004). Although most economic theories do not account for the influence of skewed outcomes, skewed outcomes may nonetheless influence choice, at both the individual and the market levels (Patton, 2004). Thus, while traditional economic and finance theories consider the influence of mean and variance on risky choice, most remain agnostic about the influence of higher order statistical moments such as skewness. By implication, a theory that accounts for individuals' preferences for skewness in addition to mean and variance might generate more accurate predictions about risky financial choice.

### **EMOTION AND RISKY CHOICE**

If people base risky financial choices solely on statistics (e.g.,mean, variance, skewness), then all individuals should show similar choices, generating predictable market movements. Psychological theorists, however, have argued that financial choices likely result from multicomponential processes that generate heterogeneous choices. If multicomponential processes drive financial risk taking, then those processes may unfold over time and be influenced by factors other than statistical moments.

Early economic theorists suspected that emotions influence choice. Smith (1759) argued that behavior was determined by a struggle between the "passions" and an "impartial spectator." The passions included emotions such as fear and anger, as well as motivational feeling states arising from self- or other-regarding interests. Smith argued that although behavior may be influenced by passions, individuals can overcome their impulses by observing their actions from the perspective of an "impartial" outsider. Due to a subsequent emphasis on rational decision-making (partly encouraged by von Neumann andMorgenstern's work on expected value), interest in the influence of the passions diminished.

More recently, although traditional economic theorists have endorsed the rationally grounded "Efficient Markets Hypothesis" (Samuelson, 1965; Fama, 1970), unpredicted and rapid rises and crashes of the market valuation of technology and housing sectors have raised new questions about investor rationality. Critics of the Efficient Markets Hypothesis have contended that investors consistently exhibit irrational tendencies including overconfidence (Barber and Odean, 2001; Gervais and Odean, 2001), loss aversion (Kahneman and Tversky, 1979; Shefrin and Statman, 1985; Odean, 1998), herding (Huberman and Regev, 2001), psychological accounting (Tversky and Kahneman, 1981), miscalculation

of probabilities (Lichtenstein et al., 1981), and regret (Bell, 1982; Clarke et al., 1994). These "irrational" biases have been attributed to psychological factors with emotional overtones – including fear, greed, and other affective reactions to price fluctuations and shocks to wealth. In an attempt to explain individual and market anomalies, an expanding field of research has begun to examine links between emotion and "irrational" decision-making (Loewenstein, 2000).

Beyond the notion that emotion acts peripherally to undermine choice, some theorists have proposed that affect can play an even more central role by providing a "common currency" that allows individuals to compare and choose between different options (Peters et al., 2006). Despite the difficulty of measuring affect, scientists have had some success by examining associations between different affective reactions and choice (Mellers, 2000). Although most of this research has focused on "consequential" affect, which arises in response to choice outcomes, some have additionally argued for the importance of "anticipatory" affect, which occurs *prior* to choice (Loewenstein et al., 2001).

To assess affect, behavioral researchers have primarily relied upon self-reported experience. For instance, investigators can compare a single individual's affective reactions to different stimuli (e.g., gambles) in two dimensions (e.g., valence on a continuum from bad to good, and arousal on a continuum from not aroused to aroused). Valence and arousal ratings can then be mean-deviated across stimuli within an individual and mathematically rotated through affect space (by 45˚) to derive indices of positive and negative arousal (Knutson et al., 2005). Using these and related methods, investigators have shown that anticipation of uncertain monetary gains elicits positive arousal, whereas anticipation of uncertain monetary losses elicits negative arousal – even before outcomes are revealed, and when measured either online during anticipation or retrospectively (Samanez-Larkin et al., 2007; Nielsen et al., 2008).

Since anticipation of uncertain gains or losses elicits selfreported affect, this might subsequently influence risky choice. Unfortunately, anticipatory affect is difficult to assess because most affective self-reports are retrospective (and thus prone to memory and other biases) and online probes of affect may change the very nature of the choice being made (e.g., introducing reflection, distraction, delays, and other biases into the decision process). Ideally, investigators could also collect online physiological probes of anticipatory affect in order to validate and augment self-report measures. Fortunately, advances in neuroimaging at the end of the twentieth century may provide these probes.

#### **NEURAL TARGETS**

In initial attempts to link physiological measures of affect to financial risk taking, researchers collected peripheral physiological measures (including skin conductance, blood volume pulse, heart rate, muscular tone, respiration, and body temperature) from financial traders at work. The investigators observed increased physiological reactions during periods of market volatility, and reported greater increased physiological reactions to market volatility in less experienced traders (Lo and Repin, 2002). Subsequent findings suggested that the strength of physiological reactions correlated with poor trading performance (Lo et al., 2005). Contrary to the notion that

emotions play no role in financial risk taking, these findings suggested that market events correlated with both self-reported and physiological reactions, even in experienced professional traders. The correlational nature of these findings, however, could not establish whether financial events caused the arousal, or whether arousal might reciprocally influence financial choice.

Advances in the temporal and spatial resolution of neuroimaging techniques (such as functional magnetic resonance imaging or FMRI) have enabled researchers to visualize changes in brain activity as individuals anticipate and make financial choices. Critically, these advances allow investigators to examine changes in neural activity in anticipation of choice. Thus, investigators can temporally capture neural responses to statistical properties of financial options before outcomes are revealed. Enhanced temporal resolution also raises the possibility of using anticipatory neural activity to predict choice. Advances in spatial resolution also matter, since FMRI allows investigators to probe activity in deep subcortical as well as cortical circuits. Based on evolutionary reasoning, while more recently evolved cortical circuits may play critical roles in the representation of language and numeric symbols, more ancient subcortical circuits that share greater homology across mammalian species may play a more prominent role in emotional and motivational functions that can promote immediate survival (MacLean, 1990). Specifically, decades of brain stimulation in animals suggest that animals will work to the exclusion of all other rewards to stimulate subcortical regions that lie along the ascending mesolimbic dopamine pathway, extending from the ventral tegmental area of the midbrain through the lateral hypothalamus to ventral striatal regions (including the nucleus accumbens or NAcc) and medial and orbital prefrontal cortices (MPFC; Olds and Fobes, 1981). In contrast, animals will work equally hard to avoid stimulating other subcortical pathways that extend from the periaqueductal gray of the midbrain up through the stria terminalis and the medial hypothalamus to the lateral amygdala, and possibly the anterior insula (Panksepp, 1998). Based on its subcortical

spatial resolution, FMRI could allow investigators to test for the involvement of affect not only in choices linked to immediate survival, but also more abstract choices related to financial risk taking.

To link activity in these deep brain circuits to affective experience and ultimately choice, we have outlined an anticipatory affect model (Knutson and Greer, 2008). The model posits that uncertainty elicits increased aroused affect, while potential gains versus losses elicit positive versus negative affect. Since most future events are subjectively uncertain, potential gains should elicit positive arousal (e.g., feelings like excitement) as well as correlated neural activity in the NAcc, but potential losses should elicit negative arousal (e.g., feelings like anxiety) as well as correlated neural activity in the anterior insula. The anticipatory affect model has additional implications for motivated behavior, since the evolved function of positive arousal is to promote approach, whereas the function of negative arousal is to promote avoidance (**Figure 1**).

Most risky financial propositions (e.g., gambles, stocks) require concurrent assessment of uncertain gains and uncertain losses. According to the anticipatory affect model, if positive arousal increases, uncertain gains should appear more prominent, which should lead people to approach the risk (all else being equal). On the other hand, if negative arousal increases, uncertain losses should appear more prominent, which should lead people to avoid the risk. Consistent with this account, in an initial FMRI study that used neural activity to predict financial risk taking, anticipatory NAcc activity predicted increased financial risk taking, whereas anticipatory anterior insula activity predicted decreased financial risk taking (Kuhnen and Knutson, 2005).

Although initially inspired by a combination of animal brain stimulation (Panksepp, 1998) and human neuroimaging findings, the anticipatory affect model shares features with earlier "somatic marker"and"risk as feelings"models, both of which posit that anticipation of uncertain outcomes can generate emotional arousal (Bechara et al., 1996; Loewenstein et al., 2001). Critically,

**FIGURE 1 | An anticipatory affect model (adapted from Knutson and Greer, 2008).** An incentive cue for uncertain future outcome first elicits brain activation in at least two brain regions (NAcc and anterior insula) associated

with anticipatory affect (positive arousal and negative arousal, respectively). The balance of activation in related circuits then promotes either approach toward or avoidance of risk.

however, the anticipatory affect model does not require mediation through bodily sensations (i.e., requiring only brain activity, unlike somatic marker accounts), and specifically distinguishes anticipatory positive arousal from negative arousal (which have opposite effects on subsequent approach versus avoidance behavior, unlike the risk as feelings model). Finally, the anticipatory affect model links positive and negative arousal to activity in distinguishable neural circuits, implying that neuroimaging data could be used to directionally predict risky choice (e.g., Kuhnen and Knutson, 2005).

Different statistical moments of financial options might influence either the same or different neural circuits. The anticipatory affect model implies that distinct statistical moments should exert different but overlapping influences on affect and associated neural activity. First, financial options with high means involve large potential gains, and so should elicit positive arousal and correlated NAcc activity. Second, financial options with high variance involve both large potential losses and gains, which should elicit negative arousal and correlated anterior insula activity as well as positive arousal and correlated NAcc activity. Third, financial options with high (overall) skewness involve even larger potential losses and gains, which should elicit even more negative arousal and correlated anterior insula activity, as well as positive arousal, and correlated NAcc activity. However, positive skewness and negative skewness might have divergent impacts, since options with high positive skewness involve large potential gains, which should elicit positive arousal and correlated NAcc activity, while options with high negative skewness involve large potential losses, which should elicit negative arousal and correlated anterior insula activity. By implication, since anticipatory affective circuits are especially sensitive to the best or worst potential outcomes, they may de-emphasize probability and other considerations that require simulation or integration of many potential outcomes over time (and which may rely more on prefrontal circuits such as the MPFC).

Consistent with the anticipatory affect model, previous selfreported affect findings suggest that anticipating the outcomes of higher mean gambles elicits greater positive arousal (Knutson et al., 2005). Anticipating the outcomes of higher variance gambles (with equal mean) elicits both greater negative arousal and positive arousal. Additionally, anticipating the outcomes of positively skewed gambles (with equal mean and variance) elicits greater positive arousal, whereas anticipating the outcomes of negatively skewed gambles (with equal mean and variance) elicits greater negative arousal (**Figure 2**; Wu et al., 2011). But beyond self-reported affect, do patterns of neural activity also align with the anticipatory affect model? Since a number of recent studies have investigated the impact of financial statistical moments on FMRI activity, we now survey their collected findings.

### **PRESENT AIMS**

Although earlier reviews have considered how financial risk influences neural activity (Knutson and Bossaerts, 2007; Mohr et al., 2010a), none have integrated both economic and psychological accounts by explicitly linking different statistical moments of financial options to neural responses. The purpose of this metaanalysis was to examine whether different statistical moments of financial options (i.e., mean, variance, and skewness) recruit distinct or overlapping neural circuits implicated in anticipatory affect, and to explore implications of these findings for subsequent choice. To address these aims, we conducted a quantitative meta-analysis of FMRI studies of statistical moments on financial risk using the activation likelihood estimation (ALE) method (Eickhoff et al., 2011, 2012; Turkeltaub et al., 2012). Based on the anticipatory affect model, we predicted that distinct statistical moments would elicit overlapping patterns of activation, such that moments involving large gains (high mean, high variance, positive skewness) should increase activity in the ventral striatum (including the NAcc), and moments involving large losses (high variance, negative skewness) should increase activity in the anterior insula.

## **MATERIALS AND METHODS**

**STUDY SELECTION**

We reviewed FMRI studies of financial risk taking. Studies were identified for meta-analysis via a search of the PubMed database using key phrases "mean" OR "reward" OR "expected

value" OR "variance" OR "risk" OR "uncertainty" OR "skewness" AND "finance" OR "monetary" AND "human" AND "FMRI." This search (performed on July 10, 2012) identified 248 studies. We specifically searched for FMRI studies that used monetary incentives to manipulate one or more of the first three statistical moments of interest (i.e., mean, variance, skewness). We further identified recent review papers about risky choice that explicitly addressed neural correlates of financial risk taking (Knutson and Bossaerts, 2007; Mohr et al., 2010a). All studies found through the database search or cited by the review papers underwent a selection process. Inclusion criteria were: (1) assessment of healthy young adults (i.e., between 18 and 60 years); (2) acquisition of whole brain FMRI data; (3) availability of peak activation coordinates from group activation tables; (4) information about the probability of uncertain outcomes was provided to participants (as opposed to ambiguity); (5) at least one of the three statistical moments of interest (i.e., mean, variance, or skewness) were objectively manipulated, independent of subjective interpretations (i.e., risk tolerance/aversion measures).

These inclusion criteria were chosen to ensure that results would generalize to the population of healthy young adult humans. Several studies suggest that aging may alter brain structure and function (Cabeza et al., 2005). Furthermore, older adults often show qualitatively different activation patterns than young adults (Park et al., 2004). Therefore, this meta-analysis focused on studies that investigated risk processing in younger healthy adults (Criterion 1). Because some studies focus on specific brain regions, they may not report whole brain results. Partial findings, however, impede the detection of unexpected activations in unscanned or unreported brain regions, so studies were excluded if they acquired or reported only partial brain data (Criterion 2). As the ALE approach requires activation foci, only studies that reported peak activation coordinates of group statistical maps were included (Criterion 3). Because risk is often conceptually distinguished from ambiguity – a form of uncertainty in which probabilities are unknown – only studies in which probabilities were known or estimated by subjects were included (Criterion 4). Since the focus of this metaanalysis was to examine neural responses to statistical moments of uncertain financial options, only studies that systematically varied mean, variance, and/or skewness (as opposed to linear probability) of monetary incentives were included (Criterion 5). Studies evaluated for variance and skewness were only included if lower order moments (e.g., mean, mean and variance) were held constant. If studies manipulated multiple moments simultaneously, the lowest appropriate manipulated moment was included in the meta-analysis (e.g., studies that manipulated variance without controlling for mean were included only for mean).

Activation maps were constructed for three distinct contrasts. For the mean map, we included contrasts of neural activity during processing of monetary incentives with high versus low mean. For the variance map, we included contrasts of neural activity during processing of monetary incentives with high versus low variance (but which controlled for mean). For the skewness map, we included contrasts of neural activity during processing of

monetary incentives with high (either positive or negative) versus low skewness (but which controlled for variance and mean).

Activation foci coordinates for contrasts in the 28 studies that met inclusion criteria were submitted to ALE meta-analyses (**Table 1**). Of these, 21 contrasts were included in the mean map, 10 in the variance map, and 4 in the skewness map. Three studies that separately modeled mean and variance were included in both maps, and 2 studies that separately modeled variance and skewness were included in both maps. Yacubian et al. (2006) replicated their results in a second sample, thus their replication findings were separately included in the mean map. Symmonds et al. (2011) separately modeled positive skewness and negative skewness in different whole brain analyses, so these results were separately included in the skewness map.

### **Table 1 | Studies included in the ALE meta-analysis, with associated contrasts.**

1Whole brain coordinates acquired via personal communication.

3

2Modeled positive skewness and negative skewness trials separately. Included a separate replication sample.

 Due to this difference in research goals, meta-analytic techniques have been adapted to fit the format of FMRI findings. Specifically, whereas the key results of behavioral studies are test statistics (*p*, *t*, or *z* scores) and effect sizes, test statistics in FMRI studies usually only have meaning when paired together with the information about the location of the effect, often revealed by the location of voxels with the highest test statistics. One frequently used meta-analytic technique that utilizes this spatial information is ALE analysis (Eickhoff et al., 2011, 2012; Turkeltaub et al., 2012). ALE analysis is a quantitative meta-analytic technique that compares activation likelihoods calculated from a group of observed activation foci with a null distribution of randomly generated activation foci. The ALE metaanalytic method provides advantages over traditional label-based meta-analytic methods because it relies upon activation foci coordinates, which show greater reliability across FMRI studies than do anatomical labels.

Meta-analyses were conducted using the ALE algorithm implemented with Ginger ALE software available from www.brainmap.org (Laird et al., 2005). Foci originally reported in Montreal Neurological Institute coordinates were converted to Talairach coordinates using the icbm2tal transformation prior to analysis (Lancaster et al., 2007). In the ALE analyses, each contrast's activation foci are modeled as the peaks of Gaussian functions, the spatial extent of which is dependent on the number of subjects included in the corresponding analysis. The resulting distributions of values (called "activation likelihood estimates") represent the probability of activation occurring in a given voxel (i.e., the ALE values). For the whole brain ALE values, significance was assessed against 5000 sets of randomly distributed foci with a nonparametric statistical permutation test. Statistically thresholded maps were then computed using a false discovery rate procedure that corrected for multiple comparisons across the whole brain [FDR (*q*) = 0.01, minimum cluster size = 100 mm3 ].

### **RESULTS**

The contrast of neural responses to high versus low mean (studies = 21, foci = 210, subjects = 407) had the highest probability of activating foci in the bilateral NAcc of the ventral striatum. Highly significant foci were also observed in the anterior cingulate cortex, followed by the bilateral anterior insula. Other significant foci were observed in the left red nucleus, thalamus, and putamen (**Table 2**; **Figure 3**).

The contrast of neural responses to high versus low variance (studies = 10, foci = 82, subjects = 164) had the highest probability of activating foci in the left subgenual cingulate cortex and left anterior insula. Significant foci were also observed in the left superior temporal sulcus, left medial prefrontal cortex, right ventral striatum, and right anterior insula.

The contrast of neural responses to high versus low general skewness (studies = 4, foci = 23, subjects = 92) had the highest probability of activating foci in the left NAcc of the ventral striatum.

**Table 2 | ALE of neural foci implicated in processing high versus low mean, variance, and skewness.**

(In Talairach space, x = right-left; y = anterior-posterior, and z = superior-inferior coordinates; predicted peak foci in **bold**).

## **DISCUSSION**

This meta-analysis aimed to determine whether distinct statistical moments of risky financial options (i.e.,mean, variance, skewness) elicit different patterns of neural activity. Rather than recruiting either the same or completely distinct circuits, statistical moments activated overlapping circuits implicated in anticipatory affect. Specifically, statistical moments that promised large gains (i.e., high mean, high variance, high skewness) maximally activated the ventral striatum (particularly in the NAcc), whereas moments that threatened large losses (i.e., high variance) maximally activated the anterior insula. The deep subcortical localization of these circuits is noteworthy (as opposed to neocortical structures implicated in symbolic representation and working memory), as it implies that affective rather than cognitive processes play a critical role in financial risk assessment.

Most of the findings were consistent with the anticipatory affect model. Specifically, high versus low mean maximally activated ventral striatum (including the NAcc), high versus low variance maximally activated the anterior insula (and secondarily the ventral striatum), and high versus low skew maximally activated the ventral striatum. However, high versus low mean also activated the anterior insula to a lesser extent. This may be due to the fact that while most studies of higher order moments (e.g., variance and skewness) controlled for lower order moments (e.g., mean), studies of lower order moments typically did not control for higher order moments. Because increasing lower order moments (e.g., mean) often also increases higher order moments (e.g., variance), studies of lower order moments may inadvertently elicit activity related to higher order moments. Reduced control of higher order

moments in studies of lower order moments might also account for the larger overall number of activation foci observed in the high versus low mean contrast.

Findings for skewness partially conformed to the anticipatory affect model. While general skewness activated ventral striatum (including the NAcc), as predicted, common activation of the anterior insula was not as apparent. The omission is unexpected given that all three surveyed studies of skewness have reported that skewed gambles tend to activate the anterior insula (Burke and Tobler, 2011; Symmonds et al., 2011; Wu et al., 2011). The small number of relevant studies and variability of activation foci in the anterior insula may have precluded a common finding. The anticipatory affect model also specifically predicts that positively skewed gambles will more powerfully activate the ventral striatum, as was found in one study (Wu et al., 2011). However, this prediction could not be evaluated in the context of the meta-analysis because all studies did not provide contrasts for positive versus negative skewness, though this represents an important direction for future research. Finally, some studies modeled statistical moments during the uncertain anticipatory period before gambles were evaluated, whereas others modeled the entire gambling episode from anticipation to outcome. Since the anticipatory affect model is most relevant to the uncertain anticipatory period, it might best predict neural activity that occurs then.

#### **INTEGRATING ANTICIPATORY AFFECT AND FINANCIAL RISK TAKING**

The meta-analytic findings support neither monolithic nor modular views of neural responses to the statistical moments of financial options. Specifically, ascending from mean (lower order) to skewness (higher order moments) neither repeatedly activates all the same regions, nor does it recruit wholly distinct regions at each step. Thus, ordering the findings by objective statistical properties of the options does not yield a coherent framework for predicting associated neural activity (**Table 3**).

Alignment by affective impact, however, reconfigures the statistical moments in a coherent way that generates more consistent predictions about associated neural activity (**Table 4**). Specifically, financial options that involve uncertain large gains are likely to elicit positive arousal (e.g., high mean, positive skewness) and recruit NAcc activity, but financial options that involve uncertain large losses are likely to elicit negative arousal (e.g., high

variance, negative skewness) and recruit anterior insula activity. Reordering these statistical moments by affective impact thus scaffolds a more parsimonious and coherent framework for predicting choice. Thus, statistical moments representing objective financial risk may be translated into subjective feelings of risk indexed by neural circuits associated with affect, which together promote choice. Of course, statistical moments may also influence choice through other neural routes as well. For instance, statistical information might recruit circuits involved in symbolic representation and working memory for numerical computation (e.g., dorsolateral and parietal cortices), or might activate circuits implicated in following habits or rules (e.g., the dorsal striatum and premotor cortex). The current analysis, however, suggests that an affective neuroscience account may provide an initial viable framework both for describing and predicting financial risk taking.

#### **IMPLICATIONS FOR FINANCIAL CHOICE**

While traditional economic (e.g., expected value) and finance (e.g., mean-variance) models can account for a range of choices, other choices elude these models' explanatory reach. For instance, individual choices may be influenced by higher order statistical moments (e.g., skewness, kurtosis) as well as by incidental factors that are not relevant to the choice at hand (e.g., news, weather, nutrition, sleep, etc.). By both encompassing and transcending the explanatory reach of traditional models, an affective neuroscience approach may eventually offer a more comprehensive account of financial risk taking.

Although this meta-analysis focused on the influence of financial options on neural activity, the anticipatory affect account also has implications for choice. Indeed, neuroimaging evidence suggests that while ventral striatal activity (and NAcc activity in particular) predicts risk seeking stock choices, anterior insula activity instead predicts risk avoidant bond choices in investment tasks (Kuhnen and Knutson, 2005). Extended to higher order statistical moments, individual differences in NAcc activation as well as positive arousal predict subsequent preferences for positively skewed gambles (Wu et al., 2011). These findings suggest that even given the same statistical gambles, individual differences in affective and neural responses may provide finer-grained predictions that describe not only group behavior, but also individual choice. These findings also imply the novel prediction that even

#### **Table 3 | Predicted maximum activity organized by statistical moments (lower to higher order).**

#### **Table 4 | Predicted maximum activity organized by affective impact.**

after holding mean and variance constant, ventral striatal (NAcc) activity should predict approach toward positively skewed gambles, while anterior insula activity should predict avoidance of negatively skewed gambles – a prediction worthy of further investigation. Thus, different types of financial risk (e.g., variance versus skewness, positive versus negative skewness, etc.) may differentially recruit circuits involved in financial risk taking.

An affective neuroscience account also yields novel predictions about the influence of incidental stimuli on financial risk taking. Specifically, stimuli that increase positive arousal should encourage financial risk taking, whereas stimuli that increase negative arousal might discourage financial risk taking, even when those stimuli are irrelevant to the task at hand. Indeed, in a neuroimaging study of heterosexual males, exposure to positive images (i.e., erotic – versus neutral office supplies or aversive snakes and spiders) tended to increase choices of higher risk (i.e., higher variance) gambles, and this effect was partially mediated by NAcc activation (Knutson et al., 2008). In a follow-up behavioral study that included males and females, prior presentation of positive images increased financial risk taking, but prior presentation of negative images decreased financial risk taking (Kuhnen and Knutson, 2011).

While these influences may hold in tightly controlled and carefully incentivized laboratory demonstrations, do they generalize to "real world" choices? Researchers have speculated that investors continue to show biases in choice despite financial advice or knowledge to the contrary. Some of these, such as the lack of diversity in investment portfolios, may result from preferences for skewness (Mitton andVorkink, 2007). Additionally, because individuals are willing to pay more for positively skewed investments but receive more for accepting negatively skewed investments (Ang

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In summary, to explain human financial risk taking, economists have traditionally referred to objective statistical properties of financial options, while psychologists have emphasized subjective emotional and cognitive processes in the decision maker. An affective neuroscience account bridges these perspectives by proposing that the brain translates statistical input into affective experience, which then can influence choice. Importantly, this affective neuroscience account generates novel yet testable predictions of how various statistical moments might influence choice, and further specifies which neural components should translate statistical input into choice output. The existing findings summarizing neural responses to the first three statistical moments of financial options (i.e., mean, variance, and skewness) lends support to an affective neuroscience approach. Future work using brain activity to predict choice may generate predictions that transcend traditional economic and psychological theories. Ultimately, a better understanding of the neural mechanisms that influence financial risk taking may not only improve individuals' financial choices, but also societal welfare by better informing public policy.

#### **ACKNOWLEDGMENTS**

This research was supported by grant NSF Grant 0748915 to Brian Knutson and an NSF Graduate Research Fellowship to Charlene C. Wu. We thank Christopher Burke and Philippe Tobler for providing their data for reanalysis, as well as Grace Tang, Katja Spreckelmeyer, and two reviewers for comments on previous drafts.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 26 July 2012; paper pending published: 23 August 2012; accepted: 15 October 2012; published online: 02 November 2012.*

*Citation: Wu CC, Sacchet MD and Knutson B (2012) Toward an affective neuroscience account of financial risk taking. Front. Neurosci. 6:159. doi: 10.3389/fnins.2012.00159*

*This article was submitted to Frontiers in Decision Neuroscience, a specialty of Frontiers in Neuroscience.*

*Copyright © 2012Wu, Sacchet and Knutson. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*