Biological Psychology 107 (2015) 1–9

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Biological Psychology journal homepage: www.elsevier.com/locate/biopsycho

Unpleasant odors increase aversion to monetary losses Andrej Stancak a,∗ , Yuxin Xie b , Nicholas Fallon a , Patricia Bulsing c , Timo Giesbrecht c , Anna Thomas d , Athanasios A. Pantelous b a

Department of Psychological Sciences, University of Liverpool, Liverpool L69 7ZA, UK Department of Mathematical Sciences and Institute for Risk and Uncertainty, Liverpool, UK Unilever Research and Development, Vlaardingen, The Netherlands d Unilever Research and Development, Port Sunlight, UK b c

a r t i c l e

i n f o

Article history: Received 1 August 2014 Accepted 14 February 2015 Available online 21 February 2015 Keywords: Olfaction Loss aversion Decision making Prospect theory

a b s t r a c t Loss aversion is the tendency to prefer avoiding losses over acquiring gains of equal nominal values. Unpleasant odors not only influence affective state but have also been shown to activate brain regions similar to those mediating loss aversion. Therefore, we hypothesized a stronger loss aversion in a monetary gamble task if gambles were associated with an unpleasant as opposed to pleasant odor. In thirty human subjects, unpleasant (methylmercaptan), pleasant (jasmine), and neutral (clean air) odors were presented for 4 s. At the same time, uncertain gambles offering an equal chance of gain or loss of a variable amount of money, or a prospect of an assured win were displayed. One hundred different gambles were presented three times, each time paired with a different odor. Loss aversion, risk aversion, and logit sensitivity were evaluated using non-linear fitting of individual gamble decisions. Loss aversion was larger when prospects were displayed in the presence of methylmercaptan compared to jasmine or clean air. Moreover, individual differences in changes in loss aversion to the unpleasant as compared to pleasant odor correlated with odor pleasantness but not with odor intensity. Skin conductance responses to losses during the outcome period were larger when gambles were associated with methylmercaptan compared to jasmine. Increased loss aversion while perceiving an unpleasant odor suggests a dynamic adjustment of loss aversion toward greater sensitivity to losses. Given that odors are biological signals of hazards, such adjustment of loss aversion may have adaptive value in situations entailing threat or danger. © 2015 Elsevier B.V. All rights reserved.

1. Introduction People assign different weights to gains as compared to losses of equivalent nominal values (Kahneman & Tversky, 1979). Loss aversion refers to the tendency to prefer avoiding losses over acquiring gains. Typically, in a trading situation, the value of a gain needs to be about twice as large as the value of loss to be accepted (Tversky & Kahneman, 1991). Loss aversion affects a large scale of behaviors, such as decision making in monetary gamble tasks (Lerner & Keltner, 2001; Sokol-Hessner et al., 2009; Takahashi et al., 2013; Tom, Fox, & Poldrack, 2007), willingness to part with a mug in one’s possession (Kahneman, Knetsch, & Thaler, 1990), greater sensitivity to price increases than price decreases (Hardie, Johnson, & Fader,

∗ Corresponding author at: Department of Psychological Sciences, University of Liverpool, Liverpool L79 7ZA, United Kingdom. Tel.: +44 151 794 6951. E-mail address: [email protected] (A. Stancak). http://dx.doi.org/10.1016/j.biopsycho.2015.02.006 0301-0511/© 2015 Elsevier B.V. All rights reserved.

1993; Putler, 1992), or the style of playing golf (Pope & Schweitzer, 2011). Loss aversion has been reported in capuchin monkeys (Chen, Lakshminarayanan, & Santos, 2006), although an alternative explanation has been also suggested (Silberberg et al., 2008). A study of patients with bilateral amygdala lesions (De Martino, Camerer, & Adolphs, 2010) and brain imaging studies showed that amygdala (Sokol-Hessner, Camerer, & Phelps, 2013), ventral striatum and other brain regions (Tom et al., 2007) mediated loss aversion. Although loss aversion can be viewed as an individual’s stable trait, possibly linked with monoaminergic systems in thalamus (Takahashi et al., 2013), aversion to potentially unfavorable outcomes has also been shown to vary under the influence of emotions (Lerner & Keltner, 2001; Rottenstreich & Hsee, 2001) or cognitive-emotional appraisals applied during decision making (Sokol-Hessner et al., 2009). Information about occurrence of adverse events has been reported to increase perceived likelihood of other adverse events (Johnson & Tversky, 1983). These studies suggest that loss aversion may be dynamically adapted in response to instantaneous situational and affective influences.

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The sense of smell informs about the presence of both adverse cues such as fire, poisons, contaminated food, or water, and appetitive cues such as food, group members, or a safe, nurturing environment. Unpleasant odors have been shown to increase the aversive startle reflex (Ehrlichman, Kuhl, Zhu, & Warrenburg, 1997; Miltner, Matjak, Braun, Diekmann, & Brody, 1994). Detection of unpleasant odors compared to pleasant odors occurs faster (Bensafi, Rouby, Farget, Vigoroux, & Holley, 2002; Boesveldt, Frasnelli, Gordon, & Lundstrom, 2010; Jacob & Wang, 2006), and unpleasant odors are associated with a stronger autonomic arousal than pleasant odors (Aoui-Ismaïli, Vernet-Maury, Dittmar, Delhomme, & Chanel, 1997; Brauchli, Ruegg, Etzweiler, & Zeir, 1995). Odors have also been shown to shift hedonic evaluations of previously neutral visual stimuli toward negative or positive depending on the hedonic quality of the odor (Bone & Jantrania, 1992; Todrank, Byrnes, Wrzesniewski, & Rozin, 1995). Further, odors activate a number of regions known to participate in decision making including among others the orbitofrontal cortex (Gottfried & Zald, 2005; Rolls, Critchley, & Treves, 1996), anterior cingulate cortex (Ciumas, Lindstrom, Aoun, & Savic, 2008; Rolls, Grabenhorst, & Parris, 2010; Savic, Gulyas, Larsson, & Roland, 2000), amygdala (Cerf-Ducastel & Murphy, 2003; Gottfried & Dolan, 2004; Royet et al., 2000; Savic & Gulyas, 2000; Savic et al., 2000; Zald & Pardo, 1997), and anterior insula (Bensafi, Sobel, & Khan, 2007; Heining et al., 2003; Plailly, Radnovich, Sabri, Royet, & Kareken, 2007; Rolls, 2005; Wicker et al., 2003). The present study aimed to investigate the role of odors on loss aversion in a monetary gamble task. We hypothesized that an unpleasant odor would increase loss aversion relative to presentation of a pleasant or neutral odor. Low-intensity odors were administered during presentation of two prospects, one offering an uncertain gain and loss, and the other an assured zero or non-zero win. Risk aversion, representing the distaste for chance resulting from diminishing marginal sensitivity to value, results in the tendency to avoid uncertain gambles. To account for this process, the risk aversion parameter was also analyzed. Skin conductance response was analyzed to explore whether odors modulated autonomic responses to losses. 2. Methods 2.1. Participants Thirty-two healthy participants (18 females, 14 males), aged 25.7 ± 3.55 years (mean ± SD), took part in the study. All participants showed normal sensitivity to odors according to the Sniffin’ Stick test battery (Hummel, Sekinger, Wolf, Pauli, & Kobal, 1997). Further, none of the participants reported any history of a neurological or respiratory disorder, or any acute or chronic inflammation of the respiratory pathways. Participants gave their written consent prior to the study. The procedures of the experiment were approved by the Research Ethics Committee of the University of Liverpool. Participants received £8 to compensate for their travel expenses and time. Two subjects were removed from the sample due to erratic choices and unusually high values of loss aversion ( > 15 and >4), assessed statistically as outliers. Thus, the final sample consisted of 30 participants (16 females, 14 males). 2.2. Procedure Subjects sat in a dimly lit, sound attenuated room. The air was continuously cleaned using a Blueair 203 Heppasilent Particle Filter system (Blueair AB, Sweden) to prevent accumulation of any odor residuals in ambient air. Subjects viewed stimuli on a 19-inch cathode ray tube monitor and rested their right hand on a computer mouse. Odors were delivered using a flow olfactometer (OL2, DancerDesign, UK) at a rate of 2.2 l/min. The olfactometer delivers a constant flow of clean air or an odor using two polytetraflouroethylene tubes of 2 mm diameter ending about 2 cm below the nostrils. The air flowed continuously through bottles containing either about 20 ml of propylene glycol (1,2-propanediol 99%, Sigma-Aldrich Co., USA), jasmine (Jasmin Flavor 10794272/2, Symrise GmbH, Germany), or methylmercaptan (Methylmercaptan 10786168/2, Symrise GmbH, Germany) which was diluted in propylene glycol at 1% concentration. The propylene glycol condition is labeled as clean air condition further in the text. These odors, tested in a pilot experiment (N = 45, unpublished), yielded distinct pleasant (jasmine) or unpleasant (rotten cabbage) sensations of comparable subjective intensities without provoking any

irritation to the nasal mucosa. To prevent droplets of solution propelling through the tubes of the olfactometer, a cellulose foam was inserted into the bottles. Odors were delivered in pulses of 4-s duration and in pseudo-random order such that an identical odor could not occur twice in a row. The randomization procedure also maintained intervals between two presentations of the same odor long enough (>30 s) to prevent habituation (Jehl, Royet, & Holley, 1994). Respiratory movements were monitored using a PneumoTrace II piezo-electric sensor (ADInstruments Pty Ltd., Australia) placed at the level of epigastrium or chest. The respiratory signal was continuously recorded with 400 Hz sampling rate and displayed on a 14-inch laptop screen. Monitoring respiratory movements allowed a manual triggering of odor onsets at the end of expiration, usually during the post-expiratory pause just preceding onset of inspiration. Respiratory movement signals were not calibrated against tidal volume data in the present study. Since the proportion of thoracic and abdominal excursions may vary from cycle to cycle, our amplitude estimates offer only approximate and indirect information about tidal volume changes associated with inhaling different odors and this represents a certain methodological limitation of the present study. Onsets of odor pulses occurred shortly before onsets of inspirations at an average latency of −0.13 ± 0.45 s, mean ± SD. Skin conductance was recorded continuously using the same ADI instruments amplifiers. Two rounded silver electrodes with a surface area of 1.5 × 1.5 cm2 were placed to the distal phalanges of the left 3rd and 4th fingers. The experiment started with acquisition of subjective ratings for pleasantness, intensity, and familiarity of each odor. Odor stimuli were presented for 4 s, and subjects rated each odor using three visual analog scales shown on a computer screen. The intensity scale was anchored with labels “no odor”, and “very strong odor”. The pleasantness scale ranged from “very unpleasant” to “very pleasant”, and the familiarity scale from “not familiar at all” to “very familiar”. All ratings were measured on a scale ranging from 0 to 100. 2.3. Monetary gamble task The monetary gamble task was similar to the task used in previous studies (Sokol-Hessner et al., 2009; Tom et al., 2007). Participants received an initial endowment of £25 and were informed that this amount of money is theirs to gamble and that they can either increase or decrease their initial endowment depending on their luck during the experiment. They were also informed that 10% of gambles will be randomly selected from all trials at the end of experiment, and the difference between sum of wins and losses on those select trials will be added to or subtracted from their initial endowment of £25. Participants’ earnings ranged from £31 to 40. The experiment consisted of 240 gambles with an alternative assured win of zero and 60 gambles with a non-zero assured win. Eighty gambles with assured zero win showed any combination of 8 possible gains (£1.0, 2.0, 3.0, 3.5, 4.5, 5.5, 5.0, 6.0), and 10 possible losses for each gain. The losses were calculated by multiplying a gain value by one of 10 coefficients in the range of 0.2–2.0 in steps of 0.2. These coefficients yielded 10 gain/loss ratios as follows: 5.0, 2.5, 1.67, 1.25, 1.0, 0.83, 0.71, 0.63, 0.56, and 0.5. All permutations (n = 80) of gains and losses were presented three times in random order, and each of three presentations of identical gambles was associated with a different odor (240 trials). Twenty trials with a non-zero assured win were also presented three times (60 trials), each time with a different odor. The assured win trials offered a risky prospect (P = 0.5) of winning a larger amount of money and a prospect of a smaller assured win. The list of 20 pairs of assured wins and risky gains is given in Table 1. Trials were presented in random order for each participant. Due to the large number of trials, the experiment was split into

Table 1 List of 20 pairs of uncertain gains and assured wins used in the experiment. Pair

£ Gain (P = 0.5)

£ Assured win (P = 1.0)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1.0 1.5 2.0 2.5 3.5 4.0 6.0 6.0 6.0 7.5 7.5 9.5 11.0 11.5 12.5 12.5 13.0 13.0 14.0 15.0

0.5 0.5 1.0 1.0 1.5 1.5 3.0 2.5 2.0 2.5 3.0 4.0 5.0 5.0 4.5 5.0 5.0 6.0 7.5 6.0

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Fig. 1. Flowchart of the experiment. (A) Declined gambles. Each trial started at around onset of inspiration. Two prospects have been displayed for 4 s along with one of three odors. One-half of the screen showed a gamble entailing 50% chance of winning or losing the displayed amount of money. The other half of the screen showed an assured win. In the case of choosing an assured win of £0, participants would neither lose nor win anything. In the next 2.5-s period, the prospects continued to be displayed, and two yellow rectangles appeared at the bottom part of the screen prompting the subject to reveal his/her decision by pressing the left or right button. If subjects declined gamble and chose the assured win, a fixation cross appeared on the computer screen and the next trials started in few seconds depending on arrival time of a next inspiration. (B) Accepted gambles. If subjects accepted a gamble, a black screen was shown for 1 s after the 2.5 response period elapsed, and the outcome of the last gamble has been displayed (“You won” or “You lost”) for 1 s. A resting period lasting 4 s was inserted to allow the skin conductance response to the outcome to evolve. The next trial started few seconds after the 4-s resting period as soon an inspiration has been detected. The intervals used in analysis of skin conductance response (SCR) data are indicated with white rectangles. The baseline interval of 1 s duration preceded onset of feedback presentation. The amplitude of SCR was evaluated in a 4.5 s window starting 0.5 s after onset of feedback.

three equal blocks of 100 trials lasting about 22 min each. Blocks were separated by resting periods of approximately 3 min. The stimuli were controlled using Cogent 2000 (UCL, London, UK) program running in Matlab 7.8 (Mathworks, Inc., USA) environment. The trial structure is shown in Fig. 1A and B. Each trial began with a fixation cross which was displayed for a variable time interval of one or two full respiratory cycles to allow synchronization of the trial onset with onset of inspiration. Next, two prospects were displayed on the computer screen for 4 s. The left or right part of the screen showed two yellow text lines on a black background, e.g., “You win £3.0”, “You lose £3.0”. The other half of the screen showed the value of an assured gain. While prospects were still displayed on the screen, two yellow rectangles were displayed in the absence of an odor for another 2.5 s. Participants were instructed to use this period to indicate their decision about the prospects by pressing the left or right mouse button to select which option they preferred and they were also informed that if they would not press any button within the 2.5 s interval, that particular trial would be invalid. After indicating their choice using a computer mouse, the yellow rectangle situated below the selected prospect turned to green. If the participant selected the risky gamble option (Fig. 1B), a 1-s resting interval displaying a black screen was inserted, and feedback in the form “You won” or “You lost” was shown for 1 s. The feedback was followed by a 4 s resting interval before a fixation cross appeared and the next trial began. 2.4. Eliciting loss aversion Prospect theory (Kahneman & Tversky, 1979) describes how people decide between different risky alternatives with known outcome probabilities. The theory postulates that decisions are based on the potential value of losses and gains rather than absolute levels of wealth and people are more sensitive to losses than to gains of the same magnitude. We employed a parametric method to estimate the level of loss aversion using a piecewise function:

 U(x) =

+

xv ,

x ≥ 0, v−

−(−x) ,

The elicitation process is based on the logit-function which gives the probability of acceptance of a risky gamble. Formally, the function can be written as F(p, xg , xl , xc ) = (1 + exp{−(U(p, xg , xl ) − U(xc ))})

−1

,

(1)

where xg and xl refer to the monetary amount that participants could win or lose and xc represents the gains for an assured win. The probability to win the uncertain gamble is represented by p. In the present study, probabilities of wins and losses were equal throughout the experiment at p = (1 − p) = 0.5. We further assume that participants combine their utility and probability in a linear manner, which implies pU(x) = U(px). The logit parameter  denotes the sensitivity to utility deviations. A greater  suggests a greater consistency in applying a mathematical model to individual decision-making behavior. On the other hand, smaller  suggests that the probability of accepting a gamble is more sensitive to the difference between the gamble and the graduated amount. Three hundred choices were collected for each participant. Choice data were clustered based on odor type resulting in three sets, counting 100 choices each. Denote Zi as the choice related to the gamble i, where Zi equals 1 if the participant proceeds with the uncertain gamble, otherwise Zi will remain zero. The log likelihood function for each odor condition is given by 100 

Zi log(F(p, xg , xl , xc )) + (1 − Zi ) log(1 − F(p, xg , xl , xc ))

(2)

i=1

The values of , v, and  are obtained by finding a proper set of estimates to maximize Eq. (2). Specifically, since this process involves a nonlinear optimization, a numerical approximation method has been applied using the Nelder–Mead simplex algorithm (see Nocedal & Wright, 2006), implemented in Mathematica 9.0 (Wolfram Research, Inc., USA). 2.5. Skin conductance response

x < 0,

where v is the risk aversion parameter that controls the diminishing sensitivity, x represents the actual outcome from each trail, and  > 1 is the loss aversion coefficient to overstate utility from losses. Because the whole utility is referencedependent, outcomes are regarded as gains when x ≥ 0 or losses when x < 0. Empirical data suggest that the utility function is concave over gains and convex over losses (Abdellaoui, Bleichrodt, & Paraschiv, 2007; Wakker, Köbberling, & Schwieren, 2007). As curvature parameters for gains and losses in subsequent studies have been largely assumed to be equal (Barberis, Huang, & Santos, 2001; Fielding & Stracca, 2007; Hwang & Satchell, 2010; Sokol-Hessner et al., 2009; Tversky & Kahneman, 1992; Wu & Gonzalez, 1996), the present study also employed the assumption of equality of curvature parameters v+ = v− = v.

To evaluate effects of odors on subject’s reactions to losses and wins when learning the outcomes of gambles, skin conductance responses (SCR) were analyzed in loss–win gambles. Due to technical problems, skin conductance data in four subjects were unavailable for analysis. The continuous skin conductance signal was bandpass filtered (0.001–5 Hz) and segmented into epochs ranging from −4.0 to 12.5 s relative to the onset of the odor pulse; these long epochs were visually checked for presence of artifacts. To analyze SCR changes associated with outcomes, the SCR amplitude was evaluated as the maximum skin conductance value during a 4.5-s period starting 0.5 s after onset of feedback, highlighted in Fig. 1B. This time window accords with previously reported latencies of SCR peaks falling into an interval ranging from 1.5 to 4.0 s relative to onset of stimulus (Boucsein, 2012). The baseline SCR values, computed in a 1-s epoch preceding feedback (Fig. 1B), were subtracted

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Fig. 2. Loss aversion, risk aversion, logit sensitivity, odor ratings, and skin conductance responses in three odor conditions. (A) Mean values and standard errors of the mean (SEMs) of odor pleasantness in jasmine, clean air, and methylmercaptan conditions. (B) Mean values and SEMs of odor intensity. (C) Mean and SEM for loss aversion . (D) Individual cumulative  coefficients in jasmine, clean air, and methylmercaptan conditions. Individuals were ordered according to their mean  computed from all three odor conditions· (E). Mean values and SEMs of risk aversion v. (F) Mean values and SEMs of logit sensitivity . (G) The statistically significant interaction seen in amplitude of skin conductance response between three odors and win (white squares) vs. loss (black squares) outcomes. Vertical bars indicate SEMs. (H) Mean values and SEMs of time of inspiration Ti in the respiratory cycle preceding (white squares) and following (black squares) onset of jasmine, clean air, and methylmercaptan. This pattern corresponded to the statistically significant interaction between odor cycles and types of odors.

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from SCR values in the outcome period. The threshold for SCR amplitude was set to 0.25 ␮S. The maximum value was the average of three data samples centered at the local SCR maximum. To normalize SCR values for amounts of losses and wins and to improve the statistical properties of SCR data, SCR values (in ␮S) were transformed √ by the square-root transform and divided by £ values of win or loss ( ␮S/£), similar to Sokol-Hessner et al. (2009). 2.6. Respiratory parameters To evaluate effects of odors on amplitude of respiratory movements and durations of inspiration and expiration, the onsets of inspiration, peaks of inspiration, and ends of expirations were identified, using a semi-automated program, in two respiratory cycles, one preceding and one starting just before or after onset of an odor pulse. Mean values of inspiration time (Ti ) and expiration time (Te ) were computed from all trials falling into a particular odor type. The amplitude of respiratory movements was computed by subtracting the average voltage measured at the beginning of inspiration and at end of expiration from the peak inspiration voltage. As respiratory movements have not been calibrated against tidal volume, we used standardized amplitude values. The standardized amplitudes of respiratory movements were computed by converting in every subject the peak volume values (in millivolts) of all respiratory cycles (300 trials, 2 respiratory cycles per trial) to Z values, and computing mean Z values for the cycle preceding and following onset of trial and for each of three odors. Finally, the duration of the post-expiratory pause occurring between two respiratory cycles was evaluated as the time interval between the end of expiration and the onset of the next inspiration. 2.7. Statistical analysis Parameters of loss aversion , risk aversion v, and logit sensitivity  were evaluated using one-way analysis of variance (ANOVA) for repeated measures with the three odor conditions as the within-subject factor. The degrees of freedom were corrected using Greenhouse–Geisser ε correction to overcome any violation of the sphericity assumption. Respiratory parameters were analyzed using a twoway ANOVA for repeated measures with odors (three levels) and respiratory cycles (two levels, pre-odor and odor cycles) as within-subject factors. Skin conductance response values were analyzed using a two-way ANOVA for repeated measures using odors (three levels) and outcomes (two levels, wins vs. losses). To analyze associations between loss aversion under three odors and odor pleasantness and intensity, a one-way analysis of covariance (ANCOVA) for repeated measures was computed in BMDP 2 V program (Biomedical Data Package, Cork, Ireland). Pearson’s correlation coefficients were used to analyze linear associations between variables. A 95% confidence level was employed throughout.

Fig. 3. The scatter plots, linear regression lines, and the 95% confidence lines representing correlations between methylmercaptan-jasmine differences in loss aversion  and odor pleasantness (A) and intensity (B).

3. Results 3.1. Odor ratings Odors differed significantly in their pleasantness according to a one-way ANOVA for repeated measures (F(2,58) = 517.1, P < 0.001, ε = 0.951) (Fig. 2A). Jasmine (77.8 ± 1.95, mean ± SEM) was evaluated as more pleasant than both clean air (52.2 ± 0.9; t(29) = 12.7, P < 0.001) and methylmercaptan (13.2 ± 1.8; t(29) = 24.8, P < 0.001), whilst methylmercaptan was evaluated as more unpleasant than clean air (t(29) = 18.6, P < 0.001). Odors also differed in their intensities (F(2,58) = 440.9, P < 0.001, ε = 0.828) (Fig. 2B). Both jasmine (64.6 ± 2.8, mean ± SEM) and methylmercaptan (77.7 ± 1.97) were perceived as being stronger than clean air (7.8 ± 1.7, P < 0.001), and methylmercaptan was also perceived to be more intense than jasmine (t(29) = 6.1, P < 0.001). Odors did not differ in their familiarity (F(2,58) = 0.51, P = 0.60, ε = 0.828). 3.2. Odors and loss aversion Mean loss aversion  was 1.40 ± 0.07 (mean ± SEM), matching well the mean  in a previous study (Sokol-Hessner et al., 2009). Loss aversion coefficients showed normal distributions for all odor conditions according to Kolmogorov–Smirnov distribution fitting test (P > 0.05). A one-way ANOVA for repeated measures showed that  was influenced by the odor type (F(2,58) = 4.2, P = 0.032, ε = 0.733) (Fig. 2C). This effect was due to a larger  in unpleasant odor condition (1.60 ± 0.09, mean ± SEM) relative to both pleasant (1.34 ± 0.08, mean ± SEM; t(29) = 2.21, P = 0.022) and neutral

odor conditions (1.38 ± 0.07; t(29) = 2.41, P = 0.035). Individual  coefficients for each subject and odor are shown in Fig. 2D. Neither risk aversion (Fig. 2E) nor logit sensitivity (Fig. 2F) was affected by the type of odor (P > 0.05). To analyze whether the differences between jasmine and methylmercaptan in loss aversion would be related to variations in odor pleasantness or odor intensity, Pearson’s correlation coefficients were computed between the difference in  and the difference in odor pleasantness or intensity between methylmercaptan and jasmine conditions. The scatter plots and linear regression lines for odor pleasantness and odor intensity are shown in Fig. 3A and B, respectively. We found a statistically significant correlation between the difference values of  and odor pleasantness (r(28) = −0.364, P = 0.048) pointing to a linear increase in  with increased unpleasantness of methylmercaptan over jasmine. The correlation computed between  and odor intensity was not statistically significant (r(28) = −0.02, P = 0.91). The association between  and odor pleasantness was further supported by a oneway ANCOVA for repeated measures using  as the dependent variable and both odor pleasantness and intensity as covariates. The covariate effect of odor pleasantness was statistically significant (F(1,57) = 4.25, P = 0.044), and the main effect of odors changed to be statistically not significant after inclusion of odor pleasantness as a covariate (F(2,57) = 0.88, P = 0.42). This suggests that the odor pleasantness largely accounted for changes of · The covariate effect of odor intensity was not statistically significant (F(1,57) = 0.35, P = 0.57), and the main effect of odors proved to be statistically significant even after inclusion the odor intensity as a covariate (F(2,57) = 3.40, P = 0.040).

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To evaluate effects of habituation on loss aversion associated with long duration of experiment and repeated exposure to odors, loss aversion  was also estimated in three blocks of trials irrespective of their association with a particular odor. Effects of blocks in repeated-measures ANOVA was not statistically significant (F(2,58) = 0.31, P = 0.66, ε = 0.725) suggesting a stable level of loss aversion across blocks of trials. Taken together, the data suggest an increased loss aversion in a monetary gamble task if prospects were displayed in the presence of an unpleasant odor, which increase was related to variations in hedonic evaluation of odors.

in Ti in the odor-related respiratory cycle were not associated with  according to one-way ANCOVA for repeated measures (P > 0.05). Neither Te nor the duration of the post-expiratory pause occurring between the respiratory cycle co-occurring with onset of odor and the preceding respiratory cycle showed any interaction between the type of odor and respiratory cycles (P > 0.05). Data suggest that jasmine and methylmercaptan were followed by a prolonged inspiration which was of similar duration in both these odors, and bore no association with loss aversion changes.

4. Discussion 3.3. Skin conductance response The skin conductance responses to gamble outcomes were analyzed using a 3 × 2 ANOVA for repeated measures (three odors, √ two levels of gamble outcomes). Losses (0.28 ± 0.045 ␮S/£, mean ± SEM) were associated with greater amplitudes of SCR than √ wins (0.09 ± 0.018 ␮S/£) (F(1,25) = 6.57, P = 0.017). Further, odors affected SCR amplitudes differently in trials entailing losses or wins (F(2,50) = 3.36, P < 0.047, ε = 0.921) (Fig. 2G). Tests of simple effects showed that the interaction effect was due to a larger amplitude of SCR in the methylmercaptan condition during loss than win trials compared to jasmine condition (F(1,25) = 5.69, P = 0.025), whilst neither the contrast between jasmine and clean air, or methylmercaptan and clean air showed any difference in the preponderance of losses over wins (P > 0.05). To analyze associations between odor-related variations in skin conductance responses to gamble outcomes and loss aversion and odor pleasantness, the amplitude differences between losses and wins were calculated in each of three odors, and used as a dependent variable in a one-way ANCOVA for repeated measures with loss aversion and odor pleasantness as covariates. Although the effect of odors on loss-win SCR differences proved to be statistically not significant after inclusion of loss aversion and pleasantness as covariates, none of the covariate effects reached the statistical significance threshold (P > 0.05). The correlation coefficient computed using differential loss–win SCR amplitude and loss aversion in methylmercaptan condition was not statistically significant (r(24) = 0.023, P = 0.91). 3.4. Effects of odors on respiratory pattern The amplitude of respiratory movements was evaluated in two respiratory cycles, one preceding and one following onset of an odor pulse. This data were analyzed using a 3 × 2 ANOVA for repeated measures (three odors, two respiratory cycles). Neither the main effect of odors nor the interaction between odors and respiratory cycles were statistically significant (P > 0.05). The inspiration time Ti , evaluated using ANOVA for repeated measures showed statistically significant main effects of cycles (F(1,29) = 21.2, P < 0.0001), odors (F(2,58) = 5.66, P = 0.07, ε = 0.909), and the interaction of cycles and odors (F(2,58) = 9.84, P = 0.0002, ε = 0.764) (Fig. 2H). Ti was longer in the cycle co-occurring with odor pulse (1.43 ± 0.050 s, mean SEM) than in the preceding cycle (1.35 ± 0.043 s), and longer for jasmine (1.41 ± 0.047) than both clean air (1.37 ± 0.047 s; F(1,29) = 8.7, P = 0.006) and methylmercaptan (1.39 ± 0.045 s; F(1,29) = 4.79, P = 0.036). Tests of simple effects showed that odors differed only in the respiratory cycle coinciding with odors (F(1,29) = 13.2, P = 0.001) but not in the preceding cycle (P > 0.05). The effects of odors in the cycle commencing with odor pulse was due to longer Ti in jasmine than clean air (t(29) = 4.39, P < 0.001), and methylmercaptan than clean air (t(29) = 3.63, P = 0.001); however, Ti was not statistically different in jasmine and methylmercaptan conditions (P > 0.05). The changes

Our study shows that unpleasant odors increase loss aversion in a monetary gamble task. Odor-related individual variations in loss aversion were associated with hedonic evaluations of odors but not with odor intensity. Further, the unpleasant odor increased amplitudes of skin conductance response to losses relative to gains during learning outcomes of gambles. To the best of our knowledge, the present study is the first to demonstrate increases in loss aversion while perceiving an aversive odor, emphasizing evolutionarily based, biological roots of decision making. Furthermore, our findings highlight the role of unpleasant odors as behavioral signals of threat or danger. Thus, unpleasant odors alter hedonic evaluations of previously neutral stimuli toward less pleasant (Todrank et al., 1995; van Reekum, van den Berg, & Frijda, 1999). Unpleasant odors have also been shown to augment defensive reflexes (Ehrlichman et al., 1997; Miltner et al., 1994) and to increase motor readiness (Bensafi et al., 2002; Boesveldt et al., 2010; Jacob & Wang, 2006), and autonomic arousal (Aoui-Ismaïli et al., 1997; Brauchli et al., 1995). In addition, increases in loss aversion whilst smelling an unpleasant odor are in line with previous studies reporting that negative emotional states increase pessimistic outlooks (Lerner & Keltner, 2001), perceived likelihood for adverse life events (Johnson & Tversky, 1983), or perceived likelihood of occurrence of subsequent negative emotional states (DeSteno, Petty, Wegener, & Rucker, 2000). We conjecture that unpleasant odors increase the negative hedonic value of a potential loss by the mechanisms of evaluative priming (Fazio, Sanbonmatsu, Powell, & Kardes, 1986; Herring et al., 2013) resulting in a shift in the hedonic evaluation of a target stimulus preceded or co-occurring with a positively or negatively valenced priming stimulus. Evaluative priming in the olfactory domain operates according to hedonic congruency between an olfactory prime and a target stimulus (Bone & Jantrania, 1992; Hermans, Van den Broeck, & Eelen, 1998; Todrank et al., 1995; van Reekum, van den Berg, & Frijda, 1999). For instance, hedonic evaluations of non-figurative paintings (van Reekum et al., 1999) or neutral faces (Todrank et al., 1995) have been shown to change when presented simultaneously with pleasant odors. While unpleasant odor has increased loss aversion relative to clean air, pleasant odor failed to attenuate loss aversion in a statistically significant manner. Suppressing effects of positively valenced primes on hedonic evaluations of negatively valenced, aversive targets are not conclusive. For instance, pain intensity during noxious laser stimulation has been shown to increase in the presence of unpleasant pictures or sounds, whilst positive emotional stimuli failed to decrease pain compared to hedonically neutral stimuli (Stancak & Fallon, 2013; Stancak, Ward, & Fallon, 2013). However, other studies reported pain-suppressing effects of positively valenced emotional stimuli compared to neutral stimuli (Kenntner-Mabiala & Pauli, 2005; Roy, Piché, Chen, & Rainville, 2009). Similarly, pleasant and unpleasant odors showed variable effects on pain intensity ranging from lack of odor effect (Marchand & Arsenault, 2002), greater pain in unpleasant than pleasant

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odors (Villemure, Slotnick, & Bushnell, 2003), greater pain in both pleasant and unpleasant odor conditions compared to clean air (Martin, 2006), to an almost linear relationship between odor pleasantness and pain intensity including decreased pain during smelling pleasant odor (Bartolo et al., 2013). Thus, further studies should address effects of pleasant odors on loss aversion as such effects might only be present in specific odors having direct impact on brain structures participating in hedonic evaluations of aversive stimuli and decision making. Although from a theoretical standpoint loss aversion is viewed as part of a risk aversion attitude (Köbberling & Wakker, 2005), our empirical study shows that unpleasant odor selectively increased loss aversion sparing risk aversion. As risk aversion refers to avoidance of risky gambles irrespective of their hedonic values, the association between loss aversion and unpleasantness of odor suggests that hedonic congruency between anticipated losses and unpleasantness of odor was essential. Notably, implementation of parametric estimation of loss aversion and risk aversion, similar to Sokol-Hessner et al. (2009), Sokol-Hessner, et al. (2013), and SokolHessner, Hartley, Hamilton, and Phelps (2014) was instrumental in highlighting the effect of unpleasant odor on loss aversion. Germane to our findings is also brain-imaging data which suggests an overlap of brain circuitry involved in both decision making and olfaction. Amygdala has been shown to be active when smelling odors (Anderson & Sobel, 2003; Savic et al., 2000; Zald & Pardo, 1997). Specifically, amygdala is thought to differentially encode odor intensity and pleasantness (Anderson et al., 2003) and shows an increased functional connectivity with orbitofrontal cortex particularly when smelling unpleasant odors (Zald, Donndelinger, & Pardo, 1998). Bilateral amygdala lesions have been associated with reduced loss aversion (De Martino et al., 2010). Amygdala activation during outcome periods of a gamble task has been shown to correlate with loss aversion (Sokol-Hessneret al., 2013), which task was almost identical to the one employed in the present study. Thus, amygdala appears to be the likely candidate structure mediating increased loss aversion during smelling of an unpleasant odor. Besides amygdala, anterior insula and ventral striatum may have also contributed to odor-related increases of loss aversion. Anterior insula is one of secondary olfactory regions (Gottfried & Zald, 2005; Royet & Plailly, 2004) and frequently shows activation clusters during olfactory stimulation (Gottfried & Zald, 2005; Seubert, Freiherr, Djordjevic, & Lundstrom, 2013), specifically during smelling of unpleasant odors (Heining et al., 2003; Wicker et al., 2003). Anterior insula has also been shown to be associated with risk-seeking decision mistakes (Kuhnen & Knutson, 2005), anticipated or perceived losses (Paulus, Rogalsky, Simmons, Feinstein, & Stein, 2003), and negative reward skewing (Burke & Tobler, 2011). Involvement of the ventral striatum, owing to its role in risky decision making (Hsu, Krajbich, Zhao, & Camerer, 2009) and loss aversion (Tom et al., 2007), and its activation by unpleasant odors (Heining et al., 2003) may also explain our behavioral data. Future brain imaging studies should address the exact neural mechanism underlying increased loss aversion during smelling of unpleasant odors. Skin conductance response data showed that losses were associated with a stronger autonomic response than wins when both outcomes were normalized for the amount of money. This finding replicates a previous study (Sokol-Hessner et al., 2009) and adds to the recent finding of loss aversion relating to the activity of the mono-aminergic transporter system in thalamus (Takahashi et al., 2013) in showing that losses are associated with a greater arousal than equivalent wins. Greater arousal associated with losses than wins also accords with the asymmetry in cognitive and emotional processing of negative and positive events with greater autonomic and behavioral consequences being associated with negative than positive events (Taylor, 1991). The present study extends these

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findings by showing a fractionation of SCR for losses and wins in the unpleasant odor compared to pleasant odor condition. The odor-related modulation of arousal during the outcome period may also be attributed to evaluative priming (Bone & Ellen, 1999; Herz & Cupchik, 1993; Todrank et al., 1995; van Reekum et al., 1999) due to the hedonic congruency between a loss and an unpleasant odor. The differential effect of unpleasant and pleasant odors on autonomic responses to wins and losses was not related to individual variations in loss aversion. Although unpleasant odors also affected responses to outcomes, decisions appeared to be affected by odors and loss aversion only during the period of evaluation of prospects, in line with the preventative function of loss aversion which primarily guards against potential losses yet to occur. Increase in loss aversion during unpleasant odor condition occurred irrespective of lack of a normative relation between odor and decision making as odors had no direct or consequential associations with individual prospects of the decision task itself. Odors in the present study represented an indirect, incidental factor (Loewenstein & Lerner, 2009; Peters, Västfjäll, Gärling, & Slovic, 2006) in decision making. Incidental factors, such as emotional states not having a direct association with a particular decision situation, have been shown to affect judgments and decisions (Lerner & Keltner, 2001; Lerner, Small, & Loewenstein, 2004; Raghunathan & Pham, 1999). For instance, induced anger as opposed to induced fear diminishes perceived risk during decision making (Lerner & Keltner, 2001). Disgust has been shown to affect endowment effect in an economic decision task (Lerner et al., 2004). Emotions are closely associated with consciously aware or unaware behavioral tendencies, such as approach or avoidance, or even more specific behavioral patterns (Frijda, 1988). A recent theoretical model of emotions postulates a rapid categorization process which shapes underlying core affect and primes adequate motor actions and/or behavioral patterns (Barrett, 2006, 2009). Thus, a disgusting odor in the present study likely primed avoidance behavior consequently boosting avoidance of losses. Our findings are limited by the small selection of odors employed. This limitation relates to the complexity and duration of odor experiments that make it difficult to administer a range of different positive and negative odors. Therefore, further studies should address the effects of other pleasant or unpleasant odors on loss aversion as we cannot exclude that a select positive odor would decrease loss aversion. For instance, one earlier study reported increase in the use of slot machines in a casino if ambient air was scented with a specific pleasant odor, whilst other pleasant odor failed to affect the use of slot machines (Hirsch, 1995). Bearing this limitation in mind, we conclude that unpleasant odors have the capacity to increase loss aversion in a monetary gamble task. The congruency between odor unpleasantness and the negative hedonic value of loss appears to be the dominant component in this effect. This novel phenomenon exemplifies the evolutionarily based, biological roots of economic decision making in humans.

Role of the funding source Dr. N. Fallon’s post-doctoral fellowship was sponsored by Unilever.

Disclosure statement Dr. A. Thomas, Dr. T. Giesbrecht, and Dr. P. Bulsing are employed by Unilever. Unilever is interested in behavioral and cognitive effects of odors as odors are natural ingredients of food and personal care products.

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References Abdellaoui, M., Bleichrodt, H., & Paraschiv, C. (2007). Measuring loss aversion under prospect theory: A parameter-free approach. Management Science, 53, 1659–1674. http://dx.doi.org/10.1287/mnsc.1070.0711 Anderson, A. K., Christoff, K., Stappen, I., Panitz, D., Ghahremani, D. G., Glover, G. H., et al. (2003). Dissociated neural representations of intensity and valence in human olfaction. Nature Neurosciene, 6, 196–202. http://dx.doi.org/ 10.1038/nn1001 Anderson, A. K., & Sobel, N. (2003). Dissociating intensity from valence as sensory input to emotion. Neuron, 39, 581–583. http://dx.doi.org/ 10.1016/S0896-6273(03)00504-X Aoui-Ismaïli, E., Vernet-Maury, E., Dittmar, A., Delhomme, G., & Chanel, J. (1997). Odor hedonics: Connection with emotional response estimated by autonomic parameters. Chemical Senses, 22, 237–248. http://dx.doi.org/ 10.1093/chemse/22.3.237 Barberis, N., Huang, M., & Santos, T. (2001). Prospect theory and asset prices. Quarterly Journal of Economics, 116, 1–53. http://dx.doi.org/10.1162/ 003355301556310 Barrett, L. F. (2006). Solving the emotion paradox: Categorization and the experience of emotion. Journal of Personality and Social Psychology Review, 10, 20–46. http://dx.doi.org/10.1207/s15327957pspr1001 2 Barrett, L. F. (2009). Variety is the spice of life: A psychological construction approach to understanding variability in emotion. Cognition & Emotion, 23, 1284–1306. http://dx.doi.org/10.1080/02699930902985894 Bartolo, M., Serrao, M., Gamgebeli, Z., Alpaidze, M., Perrotta, A., Padua, L., et al. (2013). Modulation of the human nociceptive flexion reflex by pleasant and unpleasant odors. Pain, 154, 2054–2059. http://dx.doi.org/10.1016/j.pain.2013.06.032 Bensafi, M., Rouby, C., Farget, V., Vigoroux, M., & Holley, A. (2002). Asymmetry of plesant vs. unnpleasant odor processing during affective judgment in humans. Neuroscience Letters, 328, 309–313. Bensafi, M., Sobel, N., & Khan, R. (2007). Hedonic-specific activity in piriform cortex during odor imagery mimics that during odor perception. Journal of Neurophysiology, 98, 3254–3262. http://dx.doi.org/10.1152/ jn.00349.2007 Boesveldt, S., Frasnelli, J., Gordon, A. R., & Lundstrom, J. N. (2010). The fish is bad: Negative food odors elicit faster and more accurate reactions than other odors. Biological Psychology, 84, 313–317. http://dx.doi.org/ 10.1016/j.biopsycho.2010.03.006 Bone, P. F., & Ellen, P. S. (1999). Scents in the marketplace: Explaining a fraction of olfaction. Journal of Retailing, 75, 243–262. http://dx.doi.org/10.1016/ S0022-4359(99)00007-X Bone, P. F., & Jantrania, S. (1992). Olfaction as a cue to product quality. Marketing Letters, 3, 289–296. http://dx.doi.org/10.1007/BF00994136 Boucsein, W. (2012). Electrodermal activity. New York: Springer. Brauchli, P., Ruegg, P. B., Etzweiler, F., & Zeir, H. (1995). Electrocortical and autonomic alteration by administration of a pleasant and an unpleasant odor. Chemical Senses, 20, 505–515. http://dx.doi.org/10.1093/chemse/20.5.505 Burke, C. J., & Tobler, P. N. (2011). Reward skewness coding in the insula independent of probability and loss. Journal of Neurophysiology, 106, 2415–2422. http://dx.doi.org/10.1152/jn.00471.2011 Cerf-Ducastel, B., & Murphy, C. (2003). FMRI brain activation in response to odors is reduced in primary olfactory areas of elderly subjects. Brain Research, 986, 39–53. http://dx.doi.org/10.1016/S0006-8993(03)03168-8 Ciumas, C., Lindstrom, P., Aoun, B., & Savic, I. (2008). Imaging of odor perception delineates functional disintegration of the limbic circuits in mesial temporal lobe epilepsy. NeuroImage, 39, 578–592. http://dx.doi.org/ 10.1016/j.neuroimage.2007.09.004 Chen, M. K., Lakshminarayanan, V., & Santos, L. R. (2006). How basic are behavioral biases? Evidence from capuchin monkey trading behavior. Journal of Political Economics, 114, 517–537. http://dx.doi.org/10.1086/503550 De Martino, B., Camerer, C. F., & Adolphs, R. (2010). Amygdala damage eliminates monetary loss aversion. Proceedings of the National Academy of Sciences of the United States of America, 107, 3788–3792. http://dx.doi.org/10.1073/ pnas.0910230107 DeSteno, D., Petty, R. E., Wegener, D. T., & Rucker, D. D. (2000). Beyond valence in the perception of likelihood: The role of emotion specificity. Journal of Personality and Social Psychology, 78, 397–416. http://dx.doi.org/ 10.1037/0022-3514.78.3.397 Ehrlichman, H., Kuhl, S. B., Zhu, J., & Warrenburg, S. (1997). Startle reflex modulation by pleasant and unpleasant odors in a between-subjects design. Psychophysiology, 34, 726–729. http://dx.doi.org/10.1111/j.1469-8986.1997.tb02149.x Fazio, R. H., Sanbonmatsu, F. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attributes. Journal of Personality and Social Psychology, 50, 229–238. http://dx.doi.org/10.1037/0022-3514.50.2.229 Fielding, D., & Stracca, L. (2007). Myopic loss aversion, disappointment aversion, and the equity premium puzzle. Journal of Economic Behavior & Organization, 64, 250–268. http://dx.doi.org/10.1016/j.jebo.2005.07.004 Frijda, N. H. (1988). The laws of emotions. American Psychologist, 43, 349–358. http://dx.doi.org/10.1037/0003-066X.43.5.349 Gottfried, J. A., & Dolan, R. J. (2004). Human orbitofrontal cortex mediates extinction learning while accessing conditioned representations of value. Nature Neuroscience, 7, 1144–1152. http://dx.doi.org/10.1038/nn1314 Gottfried, J. A., & Zald, D. H. (2005). On the scent of human olfactory orbitofrontal cortex: Meta-analysis and comparison to non-human primates. Brain Research Reviews, 50, 287–304. http://dx.doi.org/10.1016/j.brainresrev.2005.08.004

Hardie, B. G. S., Johnson, A. L., & Fader, P. S. (1993). Modeling loss-aversion and reference dependence effects on brand choice. Marketing Science, 378–394. http://dx.doi.org/10.1287/mksc.12.4.37 Heining, M., Young, A., Ioannou, G., Andrew, C. M., Brammer, M. J., Gray, J. A., et al. (2003). Disgusting smells activate human anterior insula and ventral striatum. Annals of the New York Academy of Sciences, 1000, 380–384. http://dx.doi.org/10.1196/annals.1280.035 Hermans, D., Van den Broeck, A., & Eelen, P. (1998). Affective priming using a colornaming task: A test of an affective-motivational account of affective priming effects. Zeitschrift fur Experimentelle Psychologie, 45, 136–148. Herring, D. R., White, K. R., Jabeen, L. N., Hinojos, M., Terrazas, G., Reyes, S. M., et al. (2013). On the automatic activation of attitudes: A quarter century of evaluative priming research. Psychological Bulletin, 139, 1062–1089. http://dx.doi.org/10.1037/a0031309 Herz, R. S., & Cupchik, G. C. (1993). The effects of hedonic context on evaluations and experience of paintings. Empirical Studies of the Arts, 11, 147–166. http://dx.doi.org/10.2190/36RG-0V9J-4Y4G-7803 Hirsch, A. (1995). Effects of ambient odors on slot machine usage in a Las Vegas Casino. Psychology & Marketing, 12, 585–594. http://dx.doi.org/ 10.1002/mar.4220120703 Hsu, M., Krajbich, I., Zhao, C., & Camerer, C. F. (2009). Neural response to reward anticipation under risk is nonlinear in probabilities. Journal of Neuroscience, 29, 2231–2237. http://dx.doi.org/10.1523/JNEUROSCI.5296-08.2009 Hummel, T., Sekinger, B., Wolf, S. R., Pauli, E., & Kobal, G. (1997). ‘Sniffin’ sticks: Olfactory performance assessed by the combined testing of odor identification, odor discrimination and olfactory threshold. Chemical Senses, 22, 39–52. http://dx.doi.org/10.1093/chemse/22.1.39 Hwang, S., & Satchell, S. E. (2010). How Loss Averse are investors in financial markets. Journal of Banking & Finance, 34, 2425–2438. Jacob, T. J. C., & Wang, L. (2006). A new method for measuring reaction times for odour detection at iso-intensity: Comparison between an unpleasant and pleasant odour. Physiology & Behavior, 87, 500–505. http://dx.doi.org/ 10.1016/j.physbeh.2005.11.018 Jehl, C., Royet, J. P., & Holley, A. (1994). Very short term recognition memory for odors. Percepttion & Psychophysics, 56, 658–668. http://dx.doi.org/ 10.3758/BF03208359 Johnson, E., & Tversky, A. (1983). Affect, generalization, and the perception of risk. Journal of Personality & Social Psychology, 4, 200–206. http://dx.doi.org/ 10.1037/0022-3514.45.1.20 Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1990). Experimental tests of the endowement effect and the coase theorem. Journal of Political Economy, 98, 1325–1348. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–292. http://dx.doi.org/10.2307/1914185 Kenntner-Mabiala, R., & Pauli, P. (2005). Affective modulation of brain potentials to painful and non-painful stimuli. Psychophysiology, 42, 559–567. http://dx.doi.org/10.1111/j.1469-8986.2005.00310.x Köbberling, V., & Wakker, P. P. (2005). An index of loss aversion. Journal of Economic Theory, 122, 119–131. http://dx.doi.org/10.1016/j.jet.2004.03.009 Kuhnen, C. M., & Knutson, B. (2005). The neural basis of financial risk taking. Neuron, 47, 763–770. http://dx.doi.org/10.1016/j.neuron.2005.08.008 Lerner, J. S., & Keltner, D. (2001). Fear, anger, and risk. Journal of Personality & Social Psychology, 81, 146–159. http://dx.doi.org/10.1037/0022-3514.81.1.146 Lerner, J. S., Small, D. A., & Loewenstein, G. (2004). Heart strings and purse strings: Carryover effects of emotions on economic decisions. Psychological Science, 15, 337–341. http://dx.doi.org/10.1111/j.0956-7976.2004.00679.x Loewenstein, G., & Lerner, J. S. (2009). The role of affect in decision making. In R. J. Davidson, K. R. Scherer, & H. H. Goldsmith (Eds.), Handbook of affective sciences (pp. 619–642). Oxford: Oxford University Press. Marchand, S., & Arsenault, P. (2002). Odors modulate pain perception: A gender-specific effect. Physiology & Behavior, 76, 251–256. http://dx.doi.org/ 10.1016/S0031-9384(02)00703-5 Martin, G. N. (2006). The effect of exposure to odor on the perception of pain. Psychosomatic Medicine, 68, 613–616. http://dx.doi.org/10.1097/ 01.psy.0000227753.35200.3e Miltner, W., Matjak, M., Braun, C., Diekmann, H., & Brody, S. (1994). Emotional qualities of odors and their influence on the startle reflex in humans. Psychophysiology, 31, 107–110. http://dx.doi.org/10.1111/j.1469-8986.1994.tb01030.x Nocedal, J., & Wright, S. J. (2006). Numerical optimization. Berlin/New York: SpringerVerlag. Paulus, M. P., Rogalsky, C., Simmons, A., Feinstein, J. S., & Stein, M. B. (2003). Increased activation in the right insula during risk-taking decision making is related to harm avoidance and neuroticism. Neuroimage, 19, 1439–1448. http://dx.doi.org/10.1016/S1053-8119(03)00251-9 Peters, E., Västfjäll, D., Gärling, T., & Slovic, P. (2006). Affect and decision making. Journal of Behavioral Decision Making, 19, 79–85. http://dx.doi.org/10.1002/bdm. 528 Plailly, J., Radnovich, A. J., Sabri, M., Royet, J. P., & Kareken, D. A. (2007). Involvement of the left anterior insula and frontopolar gyrus in odor discrimination. Human Brain Mapping, 28, 363–372. http://dx.doi.org/10.1002/hbm.20290 Pope, D. G., & Schweitzer, M. E. (2011). Is Tiger Woods loss averse? Pesristent bias in the face of experience, competition, and high stakes. The American Economic Review, 101, 129–157. http://dx.doi.org/10.1257/aer.101.1.129 Putler, D. (1992). Incorporating reference price effects into a theory of consumer choice. Marketing & Science, 11, 287–309. Raghunathan, R., & Pham, M. T. (1999). All negative moods are not equal: Motivational influences of anxiety and sadness on decision making. Organizational

A. Stancak et al. / Biological Psychology 107 (2015) 1–9 Behavior and Human Decision Processes, 79, 56–77. http://dx.doi.org/10.1006/ obhd.1999.2838 Rolls, E. T. (2005). Taste, olfactory, and food texture processing in the brain, and the control of food intake. Physiology & Behavior, 85, 45–56. http://dx.doi.org/10.1016/j.physbeh.2005.04.012 Rolls, E. T., Critchley, H. D., & Treves, A. (1996). Representation of olfactory information in the primate orbitofrontal cortex. Journal of Neurophysiology, 75, 1982–1996. Rolls, E. T., Grabenhorst, F., & Parris, B. A. (2010). Neural systems underlying decisions about affective odors. Journal of Cognitive Neuroscience, 22, 1069–1082. Rottenstreich, Y., & Hsee, C. K. (2001). Money, kisses, and electric shocks: On the affective psychology of risk. Psychological Science, 12, 185–190. http://dx.doi.org/ 10.1111/1467-9280.00334 Roy, M., Piché, M., Chen, J.-I., & Rainville, P. (2009). Cerebral and spinal modulation of emotion by pain. Proceedings of the National Academcy of Sciences of the United States of America, 106, 20900–20905. http://dx.doi.org/ 10.1073/pnas.0904706106 Royet, J. P., & Plailly, J. (2004). Lateralization of olfactory processes. Chemical Senses, 29, 731–745. http://dx.doi.org/10.1093/chemse/bjh067 Royet, J. P., Zald, D., Versace, R., Costes, N., Lavenne, F., Koenig, O., et al. (2000). Emotional responses to pleasant and unpleasant olfactory, visual, and auditory stimuli: A positron emission tomography study. Journal of Neuroscience, 20(20), 7752–7759. Savic, I., & Gulyas, B. (2000). PET shows that odors are processed both ipsilaterally and contralaterally to the stimulated nostril. NeuroReport, 11, 2861–2866. Savic, I., Gulyas, B., Larsson, M., & Roland, P. (2000). Olfactory functions are mediated by parallel and hierarchical processing. Neuron, 26, 735–745. http://dx.doi.org/10.1016/S0896-6273(00)81209-X Seubert, J., Freiherr, J., Djordjevic, J., & Lundstrom, J. N. (2013). Statistical loxalization of human olfactory cortex. NeuroImage, 66, 333–342. http://dx.doi.org/ 10.1016/j.neuroimage.2012.10.030 Silberberg, A., Roma, P. G., Huntsberry, M. E., Warren-Boulton, F. R., Sakagami, T., Ruggiero, A. M., et al. (2008). On loss aversion in capuchin monkeys. Journal of the Experimental Analysis of Behavior, 89, 145–155. http://dx.doi.org/ 10.1901/jeab.2008-89-145 Sokol-Hessner, P., Camerer, C. F., & Phelps, E. A. (2013). Emotion regulation reduces loss aversion and decreases amygdala responses to losses. Social Cognitive and Affective Neuroscience, 8, 341–350. http://dx.doi.org/10.1093/scan/nss002 Sokol-Hessner, P., Hartley, C. A., Hamilton, J. R., & Phelps, E. A. (2014). Interoceptive ability predicts aversion to losses. Cognition & Emotion, 1–7. http://dx.doi.org/10.1080/02699931.2014.925426 Sokol-Hessner, P., Hsu, M., Curley, N. G., Delgado, M. R., Camerer, C. F., & Phelps, E. A. (2009). Thinking like a trader selectively reduces individuals’ loss aversion. Proceedings of the National Academy of Sciences of the United States of America, 106, 5035–5040. http://dx.doi.org/10.1073/pnas.0806761106 Stancak, A., & Fallon, N. (2013). Emotional modulation of experimental pain: A source imaging study of laser evoked potentials. Frontiers in Human Neuroscience, 7, 552. http://dx.doi.org/10.3389/fnhum.2013.00552

9

Stancak, A., Ward, H., & Fallon, N. (2013). Modulation of pain by emotional sounds: A laser-evoked potential study. European Journal of Pain, 17, 324–335. http://dx.doi.org/10.1002/j.1532-2149.2012.00206.x Takahashi, H., Fujie, S., Camerer, C., Arakawa, R., Takano, H., Kodaka, F., et al. (2013). Norepinephrine in the brain is associated with aversion to financial loss. Molocular Psychiatry, 18, 3–4. http://dx.doi.org/10.1038/mp.2012.7 Taylor, S. (1991). Asymmetrical effects of positive and negative events: The Mobilization-Minimization hypothesis. Psychological Bulletin, 110, 67–85. http://dx.doi.org/10.1037/0033-2909.110.1.67 Todrank, J., Byrnes, D., Wrzesniewski, A., & Rozin, P. (1995). Odors can change preferences for people in photographs: A cross-modal evaluative conditioning study with olfactory USs and CSs. Learning and Motivation, 26, 116–140. http://dx.doi.org/10.1016/0023-9690(95)90001-2 Tom, S., Fox, M., & Poldrack, C. R. R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315, 515–518. http://dx.doi.org/ 10.1126/science.1134239 Tversky, A., & Kahneman, D. (1991). Loss aversion and riskless choice: A reference dependent model. Quarterly Journal of Economics, 106, 1039–1061. http://dx.doi.org/10.2307/2937956 Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk Uncertainty, 5, 297–323. http://dx.doi.org/10.1007/BF00122574 van Reekum, C. M., van den Berg, H., & Frijda, N. H. (1999). Crossmodal preference acquisition: Evaluative conditioning of pictures by affective olfactory and auditory cues. Cognition & Emotion, 13, 831–836. http://dx.doi.org/10.1080/026999399379104 Villemure, C., Slotnick, B. M., & Bushnell, M. C. (2003). Effects of odors on pain perception: Deciphering the roles of emotion and attention. Pain, 106, 101–108. http://dx.doi.org/10.1016/S0304-3959(03)00297-5 Wakker, P. P., Köbberling, V., & Schwieren, C. (2007). Prospect theory’s diminishing sensitivity versus economic’s intrinsic utility: How the introduction of the Euro can be used to disentangle the two empirically. Theory and Decision, 63, 205–231. Wicker, B., Keysers, C., Plailly, J., Royet, J. P., Galleze, V., & Rizolatti, G. (2003). Both of disgusted in My insula: The common neural basis of seeing and feeling disgust. Neuron, 40, 644–655. Wu, G., & Gonzalez, R. (1996). Curvature of the probability weighting function. Management Science, 42, 1676–1690. Zald, D. H., Donndelinger, M. J., & Pardo, J. V. (1998). Elucidating dynamic brain interactions with across-subjects correlational analyses of positron emission tomographic data: The functional connectivity of the amygdala and orbitofrontal cortex during olfactory tasks. Journal of Cerebral Blood Flow & Metabolism, 18, 896–905. http://dx.doi.org/10.1097/00004647-199808000-00010 Zald, D. H., & Pardo, J. V. (1997). Emotion, olfaction, and the human amygdala: Amygdala activation during aversive olfactory stimulation. Proceedings of the National Academy of Sciences of the United States of America, 94(8), 4119– 4124.