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You bet: How personality differences affect risk-taking preferences
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Personality and Individual Differences 44 (2008) 1484–1494 www.elsevier.com/locate/paid
You bet: How personality differences affect risk-taking preferences Heath A. Demaree a,*, Michael A. DeDonno a, Kevin J. Burns b, D. Erik Everhart c a
Department of Psychology, Case Western Reserve University, Cleveland, OH, USA b The MITRE Corporation, Bedford, MA 01730, USA c Department of Psychology and Program in Neuroscience, East Carolina University, Greenville, NC, USA Received 8 October 2007; received in revised form 10 December 2007; accepted 8 January 2008
Abstract Individuals exhibit personal preferences in gambling games, like slot machines, even when their options are economically equivalent. Here we explore how personality differences affect risk-taking preferences in slot-like games that vary along two dimensions of a risk space, namely the wager amount or ‘‘utility” (in a ‘‘W-game”) and the winning chances or ‘‘probability” (in a ‘‘P-game”). The independent variables are personality measurements made by three scales: the Behavioral Inhibition/Activation Scales (BIS/ BAS), the Barratt Impulsiveness Scale, and the Zuckerman Sensation-Seeking Scale. Our results suggest that risk-taking is governed more by concern for a loss (measured by BIS) than desire for a win (measured by BAS), although both variables impact risk-taking preferences. We also find that Sensation-Seeking relates more to the chances (probability) of a win than the amount (utility) of a win. Impulsivity did not affect players’ choices in either game, presumably because it affects the choice to play or not play in the first place. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Risk-taking; Personality; Gambling; Assessment
*
Corresponding author. Tel.: +1 216 368 6468; fax: +1 216 368 4891. E-mail address: [email protected] (H.A. Demaree).
0191-8869/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2008.01.005
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1. Introduction Mathematically and economically, risky choices are governed by the product of probability and utility, called expected utility (Bernoulli, 1738; Edwards, 1954). Psychologically, however, experiments have shown that participants often prefer a sure-thing of $W (paid at probability P = 1) over a gamble for a larger amount $J (paid at probability P < 1, while $0 is paid at probability 1 P) even when the expected utility (U) of each option is equal (W = P J) (Tversky & Kahneman, 1992). The same experiments have also shown that preferences can change with the magnitude of W or P, such that sometimes participants prefer the gamble over the economically equivalent sure-thing. These findings suggest that risky choices are driven by more than just expected utility. This paper explores the basis for choices in a ‘‘risk space” (Fig. 1) where subjective expected utility (U0 ) may vary with W and P but objective expected utility (U) is constant. To do so we use a simplified slot machine in which each trial involves a wager (W) to win a jackpot (J), where the jackpot is paid at probability P such that expected utility is equal to zero (U = P J W = 0). In this case, any preference for W or P implies a cognitive-subjective U0 that deviates from the normative-objective U. Numerous theories have been proposed to explain such preferences in terms of subjective utilities and/or subjective probabilities. For example, in Prospect Theory (Kahneman & Tversky, 1979), subjective utilities are modeled with a non-linear ‘‘value function” while subjective probability is modeled with a non-linear ‘‘weighting function”. These non-linear functions are parametrically fit to data across a population of participants, thereby modeling average biases in subjective utility and probability. The present study instead focuses on individual differences to determine how risk-taking preferences are affected by perceptual-personality traits. Our method uses two slot-like games, called the ‘‘W-game” and the ‘‘P-game,” where all parameters of each trial are fixed except for one P
P-game
avgP
avgW W-game
0.13
W U
$4
Fig. 1. Illustration of a risk space (W, P, U) for slot games. Lines at W = $4 and P = 0.13 in the plane U = 0 show the portion of risk space tested in the present study, using a W-game and a P-game. In the W-game, a player chooses W along the horizontal line P = 0.13. In the P-game, a player chooses P along the vertical line W = $4. Average values of W for the W-game and P for the P-game, chosen by players, are indicated at points avg W = $5.21 and avg P = 0.45. These points reflect the average peaks in subjective U0 for the two games (W and P) where objective U = 0.
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parameter in each game that a participant adjusts in a series of trials. In both games, U = 0. In the W-game, P is fixed while a participant adjusts W and J is automatically adjusted (J = W/P) to keep U = 0. In the P-game, W is fixed while a participant adjusts P and J is automatically adjusted (J = W/P) to keep U = 0. In each game a participant will presumably select the dependent variable (W or P) in order to optimize his or her subjective expected utility (U0 ). A participant’s selections will presumably be affected by aspects of his or her personality, which are the independent variables. Thus the purpose of our experiment is to relate risk-taking preferences (for W and P, with U = 0) to perceptual-personality variables. With respect to perceptual-personality traits (our independent variables), the first two constructs we employ are the Behavioral Inhibition System and Behavioral Activation Systems (BIS/BAS) – measured by Carver and White’s (1994) BIS/BAS scales. BIS and BAS are two components to Gray’s Reinforcement Sensitivity Theory (RST) (e.g., Gray, 1994). High BIS predicts increased feelings of anxiety and withdrawal behavior to perceived threat, which is controlled by serotonergic pathways from the raphe nucleus to septo-hippocampal systems (e.g., Gray, 1994). High BAS is associated with greater positive affective experience and increased goal-directed behavior to appetitive stimuli; BAS appears to be the product of the brain’s dopaminergic reward pathways emanating from the ventral tegmental area to the nucleus accumbens and ventral striatum (e.g., Gray, 1990). Although no known data exist relating BIS and BAS to gambling-related behavior, we note that high BAS and BIS have been found to mediate emotional reactions and drives towards/away from appetitive and threatening behavior, respectively (e.g., Carver, Meyer, & Antoni, 2000; Gable, Reis, & Elliot, 2000). In risk-taking situations like slot games, where the outcome could be a net loss or a net win, we expect that the combination (ratio) of BIS/BAS will govern risk-taking as inhibition competes with activation. More specifically, we expect that risktaking will decrease as the ratio of BIS/BAS is increased, for both the W-game and P-game. Impulsivity and Sensation-Seeking are two other perceptual-personality variables that may affect risk-taking behavior. Impulsivity has been characterized by cognitive deficits on neuropsychological measures tapping the ability to plan, shift attention, and inhibit prepotent responses (e.g., Cavedini, Riboldi, Keller, D’Annucci, & Bellodi, 2002; Petry, 2001), which are abilities that appear to be associated with frontal cortex functioning (e.g., Chao & Knight, 1995). Impulsivity may be assessed using self-report measures such as the Barratt Impulsiveness Scale, Version 11 (BIS-11, Patton, Stanford, & Barratt, 1995). Because impulsivity has been found to predict increased risk-taking in real-world gambling (e.g., Clarke, 2006; Fuentes, Tavares, Artes, & Gorenstein, 2006), we expect it to do so in our W-game and P-game as well. Sensation-Seeking, originally proposed by Zuckerman, Kolin, Price, and Zoob (1964), has been defined as a trait whereby one seeks ‘‘varied, novel, complex sensations and experiences” (p. 27, Zuckerman, 1994). Sensation-Seeking is perhaps most frequently assessed via the Sensation-Seeking Scale, Form 5 (SSS-V, Zuckerman, 1994). Because Sensation-Seeking has been found to predict increased risk-taking in real-world gambling (e.g., Gupta, Derevensky, & Ellenbogen, 2006), and because both large rewards (Wgame) and low probability events (P-game) are rare (by definition, in the case of the P-game), we expect Sensation-Seeking to predict increased risk-taking on both W- and P-games. Throughout this study we use the term ‘‘increased risk-taking” to mean higher W in the W-game or lower P in the P-game. This usage reflects the fact that there would be no risk (i.e., no uncertainty in the outcomes of choices) if W = 0 or P = 1, hence risk-taking might be said
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to increase as W is increased (W > 0) or P is decreased (P < 1). More formally, these measures of ‘‘risk-taking” (March & Shapira, 1987) can be seen as reflecting variability in the set {win, loss} of expected utilities {P (J W), (1 P) W} for possible outcomes of each trial. Using J = W/P (because our case has U = 0), the set can be re-written as {W (1 P), W (1 P)}, which shows that W (1 P) is a measure of variability around the average expect utility (U = 0). This measure of variability will increase as W increases and P decreases (1 P increases).
2. Methods Fifty-nine undergraduate students from a private, Midwestern University participated in the present study. They received course credit for their participation. 2.1. Procedure Participants were first informed about the procedures of the experiment and provided written consent if they agreed to participate. They were then asked to complete a number of self-report questionnaires and subsequently asked to perform two versions of a repeated gambling task created in-house (described below). Following completion of these tasks, subjects were debriefed and thanked for their participation. 2.2. Questionnaires
(1) The BIS/BAS Scales (Carver & White, 1994) consist of 20 questions which assess BIS and BAS sensitivity. The BIS Scale (7 items) measures one’s general propensity to avoid negative, threatening stimuli. The BAS Scale (13 items) assesses the degree to which one approaches appetitive/rewarding stimuli, and may be subdivided into the following subscales: Reward Responsiveness (RR, 5 items tapping how emotionally reactive one is to positive events), Drive (D, 4 items measuring the degree to which one pursues rewards), and Fun Seeking (FS, 4 items assessing one’s propensity to seek and engage in fun activities). The internal consistency of BIS and the three BAS subscales range from .66 to .76 (Carver & White, 1994). (2) The Barratt Impulsiveness Scale, Version 11 (BIS-11, Patton et al., 1995) consists of 30 questions designed to measure impulsivity in such a way that the construct is orthogonally related to anxiety. The BIS-11 can be divided into three subscales – Attentional Impulsiveness (AI, measuring the propensity for racing thoughts and the inability to maintain focus), Motor Impulsiveness (MI, assessing one’s propensity to act in the ‘‘spur of the moment” and have a varied life-style), and Non-Planning Impulsiveness (NPI, measuring lack of planning and need for mental challenge). The internal consistency of the subscales range from .79 to .83 (Patton et al., 1995). (3) The Zuckerman Sensation-Seeking Scale, Form 5 (SSS-V, Zuckerman, 1994) consists of 40 forced-choice questions which may be divided into four different subscales (10 questions each) – Boredom Susceptibility (BS; assessing intolerance of monotony), Thrill and Adventure Seeking (TAS; measuring propensity towards physically dangerous pursuits), Experi-
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ence Seeking (ES; assessing life-style changes and need for cognitive stimulation), and Disinhibition (D; measuring outgoing social behavior). Because of Institutional Review Board concerns regarding questions involving drug use and sexual conduct, the D subscale was not administered (thus, only 30 questions of the SSS-V were included). The internal reliability of the SSS-V is .72 (Zuckerman, 1994). 2.3. Risk-taking measure To investigate how these perceptual-personality variables relate to different dimensions of risktaking, we designed and used the Cognitive-Affective Slot Experiments (CASE)1 computer program. CASE allows parameters (U, P, J, or W) of slot machines to be varied by experimenters and/or participants in order to suit experimental purposes. The present research was performed with two versions of CASE, one called the ‘‘W-game” and the other called the ‘‘P-game”. Participants played both versions of the game, which were counter-balanced across subjects. In both games, participants were given 50 fictitious dollars and asked to play the game for 25 trials (or until they ran out of money). In the W-game, participants varied their wager amount (W, in dollars and cents) on each trial while U and P were fixed by experimenters (U = 0 and P = 0.13). This is similar to a real slot machine, where U and P are fixed by the casino and a player can (on many machines) adjust W to his/her liking by inserting more or less money into the machine. In the W-game we set P = 0.13 because this is the actual aggregate P (over all possible payoffs) of a typical casino slot machine (Scarne, 1961). In the P-game, participants instead varied the probability of winning (P, in percent from 1% to 100%) as desired while W was fixed at $4. Although the W-game is more similar to familiar slot machines, the ecological validity of both games is uncertain because our CASE does not capture potential stimulators, distracters, and social features of gambling present in real-world venues. Participants were informed that, for every dollar they had in their stack following completion of trial #25, they would receive 1 ticket for a $50 raffle at the end of the semester. To keep both games independent from one another, participants were informed that there would be two $50 raffles – one for the W-game and one for the P-game. In the W-game, our dependent variable measuring risk-taking preference was the average wager (avg W) across a participant’s total number of trials. In the P-game, our dependent variable measuring risk-taking preference was the average probability (avg P) across a participant’s total number of trials.
3. Results 3.1. Participants Fifty-nine participants (27 women, 32 men) with a mean age of 19.34 (SD = 1.41, range: 18–23) volunteered for the experiment. For the W-game and P-game, 46/59 (total # of trials M = 22.19, 1
The CASE program and instructions for its use may be acquired by e-mailing HAD.
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SD = 6.27) and 58/59 (total # of trials M = 24.95, SD = 0.39) participants played all 25 trials (i.e., they did not run out of money), respectively. 3.2. Risk-taking behavior The overall mean for avg W was $5.21 (SD = $5.99) and for avg P was 45.20 (SD = 18.19). Men (avg W: M = $5.72, SD = $5.70; avg P: M = 44.54, SD = 19.07) and women (avg W: M = $4.61, SD = $6.37; avg P: M = 45.98, SD = 17.42) were statistically equivalent in their risk-taking behavior (W-game: F(1, 57) = 0.50, p > .05; P-game: F(1, 57) = 0.09, p > .05), and counter-balancing did not impact risk-taking behavior in either game, Fs < 1. 3.3. Personality and risk-taking behavior To assess the relationship between perceptual-personality variables and risk-taking behavior, we first report the correlation matrix between variables. Next, to determine whether the correlational relationships were due to the independent effects of the perceptual-personality measures as opposed to shared variance between measures, we report follow-up regressions. The avg W in the W-game trended to be inversely related to the avg P selected in the P-game, r = .25, p = .06. That is, people who wagered more in the W-game trended to select lower probability in the P-game. This is consistent with the notion that ‘‘risk-taking” can indeed be characterized as variability in the expected utility of the set {win, loss} of possible outcomes, which increases as W increases in the W-game (W > 0, where W = 0 is ‘‘no risk”) and as P decreases in the P-game (P < 1, where P = 1 is ‘‘no risk”). Descriptive data for the perceptual-personality measures, as well as the correlations between these variables and risk-taking behavior in the W-game and P-game are presented in Table 1. With regard to perceptual-personality variables, the first major finding was that BAS was significantly associated with risk-taking in the W-game only. That is, people with higher BAS-Total scores wagered a greater amount in the W-game. This finding appears to be largely driven by BAS-D and BAS-FS, which were highly correlated with avg W. Tests of differences between Pearson correlations revealed that risk-taking in the W-game was significantly more sensitive to BAS-Total than was risk-taking in the P-game, Z = 1.71, p < .05, BAS-D, Z = 1.83, p < .05, and BAS-FS, Z = 2.34, p < .01. The second major finding was that BIS significantly correlated with risk-taking in both games, as did the ratio of BIS/BAS. The third major finding was that Impulsivity was not significantly associated with risk-taking behavior in either game. The fourth major finding was that Sensation-Seeking2 significantly correlated with risk-taking in the P-game only. Specifically, people with higher Sensation-Seeking scores (the Zuckerman-Total score [which, again, excluded the D subscale], and on the Zuckerman-BS and -ES subscales) had lower avg P in the P-game. Results for the P-game were more sensitive than those of the
2
The internal reliability of the SSS-V in our sample (which excluded the D subscale) was .70.
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Table 1 Descriptive statistics of perceptual-personality measures, as well as Pearson product-moment correlations between these variables and the average wager selected in W-game (avg W) and the average probability selected in P-game (avg P) Mean (SD) [range] BIS BAS-Total BAS-RR BAS-D BAS-FS BIS/BAS ratio Barratt-Total AI MI NPI Zuckerman-Total BS TAS ES
19.86 39.41 14.12 13.76 11.53 .51 66.97 18.66 23.90 24.41 15.92 3.83 6.93 5.15
(4.04) [7–29] (5.08) [29–52] (1.94) [7–16] (2.69) [9–20] (2.64) [7–16] (.12) [.13–.83] (10.65) [41–93] (3.48) [9–27] (4.91 [13–36] (4.92) [13–36] (5.07) [5–26] (1.66) [1–8] (2.80) [0–10] (1.78) [2–9]
Avg W *
0.303 .359** .004 .380** .301* .392** .078 .123 .156 .100 .123 .067 .171 .020
Avg P .401** .053 .240 .054 .131 .325* .070 .011 .110 .050 .372** .422*** .191 .366**
*
p < .05. p < .01. *** p < .001. **
W-game to the Zuckerman-Total score, Z = 2.72, p < .01, the Zuckerman-BS Scale, Z = 2.74, p < .01, and the Zuckerman-ES Scale, Z = 2.14, p < .05.3 To determine whether the above results were due to the independent effects of the perceptualpersonality measures, two separate standard multiple regressions were performed. Specifically, avg W and avg P were independently regressed onto the four total perceptual-personality scores (i.e., BIS, BAS-Total, Barratt-Total, and Zuckerman-Total [excluding the D subscale]). Both avg W, F(4, 54) = 4.06, p < .01, r2 = 23.1%, and avg P, F(4, 54) = 4.15, p < .01, r2 = 23.5%, were significantly predicted by the perceptual-personality measures4. Of note, higher BAS-Total predicted increased risk-taking in the W-game only (W-game: t[54] = 3.08, p < .01, unstandardized b = .342, sr2 = 13.5%; P-game: t[54] = 1.37, p > .05, b = .672, sr2 = 2.6%) and higher Zuckerman-Total scores predicted increased risk-taking in the P-game only (W-game: t[54] = 0.97, p > .05, b = .116; P-game: t[54] = 2.07, p < .05, b = 1.096, sr2 = 6.0%). Higher BIS predicted decreased risk-taking on both measures (W-game: t[54] = 2.55, p < .05, b = .348, sr2 = 9.2%; P-game: t[54] = 2.05, p < .05, b = 1.241, sr2 = 5.9%) and Barratt-Total score did not predict risk-taking behavior in either game (W-game: t[54] = 0.64, p > .05, b = .003; P-game: t[54] = 0.13, p > .05, b = 0.031). These results strengthen our finding that higher BIS predicts decreased risk-taking in both games, whereas higher BAS and Sensation-Seeking predict increased risk-taking in the W-game and P-game, respectively. 3
Post-hoc power analyses revealed that the correlations between BAS-Total/avg P (power = .11), Impulsivity-Total/ avg W (.15), Impulsivity-Total/avg P (.13), and Zuckerman-Total [excluding D subscale]/avg W (.24) were insufficiently powered. It was pragmatically impossible to recruit an N large enough to achieve sufficient power. 4 Tolerance statistics for these regressions ranged from .65 to .79, indicating that multicollinearity issues were not of practical concern.
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4. Discussion The major findings can be summarized as follows: (1) Lower levels of the BIS/BAS ratio, as well as lower BIS alone, are associated with increased risk-taking in both the W-game and the P-game. (2) Higher levels of Impulsivity do not significantly correlate with risk-taking in either the W-game or the P-game. (3) Higher levels of Sensation-Seeking are associated with increased risk-taking in the P-game but not the W-game. With respect to BIS/BAS, our finding was expected given that risky choices involve a tradeoff between the potential for a net win and the potential for a net loss. In particular, the equation for U can be written as a sum of two terms, each of which involves the product of ‘‘chances” (P or 1 P) and ‘‘amount” (J W or W): U = P (J W) (1 P) W, which reduces to U = P J W. Since U is a combination of chances amount for reward (win) and punishment (loss), the choice of U should be governed by the interaction between activation (BAS) and inhibition (BIS). Because the BIS/BAS ratio predicts risk-taking in both the W-game and P-game, our results suggest that this is indeed the case. With respect to BIS and BAS individually, there is a fundamental asymmetry between the Wgame and P-game, as evidenced by the equation: U = P (J W) (1 P) W. For BIS, notice that the W-game varies the amount of a loss (W) and controls for the chances (1 P) of a loss, while the P-game varies the chances of a loss (1 P) and controls for the amount (W) of a loss. Thus the amount and chances of potential loss are isolated in the W-game and P-game, respectively. Assuming that higher BIS favors lower amounts and lower chances of a loss, higher BIS should cause increased risk-taking in both games – as we see in Table 1. For BAS, notice that the W-game varies the amount of a win (J W) and controls for the chances (P) of a win, while the P-game varies both the chances of a win and the amount of a win (because J is adjusted along with P, per J = W/P, to ensure that U = 0). As such, the amount and chances of a potential win are confounded in the P-game, whereby higher amounts won occur at a lower probability, and vice versa. Assuming that higher BAS favors higher amounts and higher chances of a win, it is not surprising that BAS would cause increased risk-taking in the W-game only (where chances and amount are not confounded) – as we see in Table 1. Also with respect to BIS and BAS, the fundamental asymmetry between games (noted above) allows us to infer the relative contributions of BIS and BAS to the combined effects of BIS/BAS. In particular, Table 1 shows that BIS (unlike BAS) produces risk-taking similar to BIS/BAS for both avg W in the W-game and avg P in the P-game. This suggests that the competition between inhibition (BIS) and activation (BAS) is governed more by BIS than BAS. In other words, it appears that risky choices in both games are driven more by concern for a loss (inhibition) than desire for a win (activation). This is consistent with the well-known insight from experiments on Prospect Theory, which is that even under economic equivalence it appears that ‘‘losses loom larger than gains” (Kahneman & Tversky, 1984). Neurophysiologically, it is also consistent with McNaughton and Gray’s (2000) revision of RST, which suggests that BIS does not mediate reactions to threatening stimuli per se but, rather, aids in the resolution of conflict by propelling the organism towards a non-conflict state. In the present research, this notion was supported because people with higher BIS opted for less conflict by selecting lower Ws in the W-game (with W = 0 representing zero conflict) and higher Ps in the P-game (with P = 1 representing no conflict).
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With respect to Impulsivity, we found that Barratt scores did not significantly affect choices in either the W-game or P-game, even though previous studies have associated Impulsivity with gambling frequency/severity (e.g., Clarke, 2006; Fuentes et al., 2006). One explanation may be that participants in our W-game and P-game were forced to gamble. That is, Impulsivity may be more associated with the choice to gamble in the first place rather than choosing between various gambles. With respect to Sensation-Seeking (excluding the D subscale), it is interesting that this variable predicted risk-taking in the P-game but not the W-game, especially since the BIS/BAS ratio predicted risk-taking in both games. The finding suggests that there is some aesthetic utility (i.e., ‘‘sensation”) beyond economic utility, and that aesthetic utility varies more in the P-dimension than the W-dimension of risk space. In other words, it appears that the novelty of a win or loss (Burns, 2007) may matter more than the amount of money that is won or lost – at least when U = 0. Thus in our ‘‘fair” games, it appears that players select P (and perhaps W as well) in order to optimize a subjective utility that includes more than just economic utility. Looking beyond our case of U = 0, Sensation-Seeking may also help explain why people accept so-called ‘‘irrational” gambles in real slot machines (where U < 0 because of the ‘‘house rake”), i.e., presumably because the aesthetic fun outweighs the economic cost. Our findings raise both concerns and opportunities for those interested in the assessment of risk-taking behavior. Of concern, previous studies using neuroimaging technologies have used similar tasks to determine (in part) the brain areas underlying decision-making in cases involving financial risk (e.g., Beer, Knight, & D’Esposito, 2006; Kuhnen & Knutson, 2005). Overlapping findings might be understood in terms of BIS/BAS (and especially BIS), whereas differential findings may be attributable to mechanisms underlying reward (BAS) and Sensation-Seeking preferences. In such research, the joint use of games that measure preferences along both dimensions (W and P) of a risk space may be beneficial to the identification of double-dissociations. On the opportunity front, the same approach may help assess the causal basis for risk-taking in individuals with extreme levels of BAS or Sensation-Seeking. In particular, risk-taking in the two games might be used to establish whether an individual’s preferences relate more to BAS (which appears to drive risk-taking in the W-game) or Sensation-Seeking (which appears to drive risk-taking in the P-game), as well as the types of risks members of specific populations avoid/seek. Populations with low BAS, high BAS, and high Sensation-Seeking may include the clinically depressed, people evidencing mania, and pathological gamblers, respectively (e.g., Depue & Zald, 1993; Fowles, 1994; Gupta et al., 2006). We cautiously suggest that the CASE protocol may be useful in identifying individuals within such clinical populations who may be most responsive to therapeutic intervention and, in addition, could perhaps be a useful adjunct when measuring treatment response. Here, we should point out that risk-taking is neither inherently ‘‘good” nor ‘‘bad”; although risk without regard to safety can clearly be self-destructive, risk-taking may also be important to the creation of well-being (e.g., Hosen, Stern, & Solovey-Hosen, 2004). The present research had some notable limitations. First, it focused only on financial risk-taking (e.g., not drug-related, sexual, etc., although associations with such forms of risk may exist) among healthy undergraduates within a limited age range, and failed to counter-balance questionnaire administration before and after the CASE. In the future, researchers may want to expand on this work by determining how risk-taking changes with demographics, or in various clinical populations, or how genetics may impact specific risk-taking behaviors (Kreek, Nielsen, Butelman, &
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LaForge, 2005). Future work may also explore the impact of emotional responses to wins/losses on subsequent risk-taking behavior, and how such relationships are moderated by personality, demographics, genetics, and the like. Moreover, we explored only a small portion of a risk space (Fig. 1), along the lines of W = $4 (P-game) and P = 0.13 (W-game) in the plane where U = 0. The effects of perceptual-personality differences on risk-taking preferences may be quite different in other portions of the W–P risk space, especially at more extreme values of W or P. Finally, our study was limited to just two dimensions (W and P) of risk space when in fact the third dimension (U) may be the most important of all – since differences in U are the only cases for which people should (from a normative-economic perspective) exhibit preferences in their choices.
Acknowledgements The authors thank Daria Babeshko and Mariya Topolyanskaya for their assistance on this project.
References Beer, J. S., Knight, R. T., & D’Esposito, M. (2006). Controlling the integration of emotion and cognition: The role of frontal cortex in distinguishing helpful from hurtful emotional information. Psychological Science, 17(5), 448–453. Bernoulli, D. (1738). Exposition of a new theory of the measurement of risk (Sommer, L. Trans. (1954)). Econometrica, 22, 23–26. Burns, K. (2007). EVE’s entropy: A formal gauge of fun in games. Studies in Computational Intelligence, 71, 153–173. Carver, C. S., Meyer, B., & Antoni, M. H. (2000). Responsiveness to threats and incentives, expectancy of recurrence, and distress and disengagement. Journal of Consulting and Clinical Psychology, 68(6), 965–975. Carver, C. S., & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS Scales. Journal of Personality and Social Psychology, 67(2), 319–333. Cavedini, P., Riboldi, G., Keller, R., D’Annucci, A., & Bellodi, L. (2002). Frontal lobe dysfunction in pathological gambling patients. Biological Psychiatry, 15, 334–341. Chao, L. L., & Knight, R. T. (1995). Human prefrontal lesions increase distractibility to irrelevant sensory inputs. NeuroReport, 6, 1605–1610. Clarke, D. (2006). Impulsivity as a mediator in the relationship between depression and problem gambling. Personality and Individual Differences, 40, 5–15. Depue, R. A., & Zald, D. H. (1993). Biological and environmental processes in nonpsychotic psychopathology: A neurobehavioral perspective. In C. G. Costello (Ed.), Basic issues in psychopathology (pp. 127–237). New York: Guilford Press. Edwards, W. (1954). The theory of decision making. Psychological Bulletin, 51(4), 380–417. Fowles, D. C. (1994). A motivational theory of psychopathology. In W. D. Spaulding (Ed.), Integrative views of motivation, cognition, and emotion (pp. 181–238). Iowa City, IA: University of Nebraska Press. Fuentes, D., Tavares, H., Artes, R., & Gorenstein, C. (2006). Self-reported and neuropsychological measures of impulsivity in pathological gambling. Journal of the International Neuropsychological Society, 12, 907–912. Gable, S. L., Reis, H. T., & Elliot, A. J. (2000). Behavioral activation and inhibition in everyday life. Journal of Personality and Social Psychology, 78(6), 1135–1149. Gray, J. A. (1990). Brain systems that mediate both emotion and cognition. Special issue: Development of relationships between emotion and cognition. Cognition and Emotion, 4, 269–288. Gray, J. A. (1994). Framework on taxonomy of psychiatric disorder. In H. M. Van Goozen, N. E. Van De Poll, & J. A. Sergeant (Eds.), Emotions: Essays on emotion theory (pp. 29–59). Hillsdale, NJ: Erlbaum.
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Gupta, R., Derevensky, J. L., & Ellenbogen, S. (2006). Personality characteristics and risk-taking tendencies among adolescent gamblers. Canadian Journal of Behavioural Science, 38(3), 201–213. Hosen, R., Stern, L., & Solovey-Hosen, D. (2004). Proactive risk-taking promotes happiness. Psychology and Education: An Interdisciplinary Journal, 41(1), 1–21. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. Kahneman, D., & Tversky, A. (1984). Choices, values and frames. American Psychologist, 39(4), 341–350. Kreek, M. J., Nielsen, D. A., Butelman, E. R., & LaForge, K. S. (2005). Genetic influences on impulsivity, risk-taking, stress responsivity and vulnerability to drug abuse and addiction. Nature Neuroscience, 8(11), 1450–1457. Kuhnen, C. M., & Knutson, B. (2005). The neural basis of financial risk-taking. Neuron, 47, 763–770. March, J. G., & Shapira, Z. (1987). Managerial perspectives on risk and risk-taking. Management Science, 33(11), 1404–1418. McNaughton, N., & Gray, J. A. (2000). Anxiolytic action on the behavioural inhibition system implies multiple types of arousal contribute to anxiety. Journal of Affective Disorders, 61, 161–176. Patton, J. H., Stanford, M. S., & Barratt, E. S. (1995). Factor structure of the Barratt impulsiveness scale. Journal of Clinical Psychology, 51, 769–774. Petry, N. M. (2001). Substance abuse, pathological gambling, and impulsiveness. Drug and Alcohol Dependence, 63, 29–38. Scarne, J. (1961). Scarne’s new complete guide to gambling. New York, NY: Simon & Schuster. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323. Zuckerman, M. (1994). Behavioral expressions and biosocial bases of sensation seeking. New York: Cambridge University Press. Zuckerman, M., Kolin, E. A., Price, L., & Zoob, I. (1964). Development of a sensation-seeking scale. Journal of Consulting Psychology, 28, 477–482.
Personality and Individual Differences 44 (2008) 1484–1494 www.elsevier.com/locate/paid
You bet: How personality differences affect risk-taking preferences Heath A. Demaree a,*, Michael A. DeDonno a, Kevin J. Burns b, D. Erik Everhart c a
Department of Psychology, Case Western Reserve University, Cleveland, OH, USA b The MITRE Corporation, Bedford, MA 01730, USA c Department of Psychology and Program in Neuroscience, East Carolina University, Greenville, NC, USA Received 8 October 2007; received in revised form 10 December 2007; accepted 8 January 2008
Abstract Individuals exhibit personal preferences in gambling games, like slot machines, even when their options are economically equivalent. Here we explore how personality differences affect risk-taking preferences in slot-like games that vary along two dimensions of a risk space, namely the wager amount or ‘‘utility” (in a ‘‘W-game”) and the winning chances or ‘‘probability” (in a ‘‘P-game”). The independent variables are personality measurements made by three scales: the Behavioral Inhibition/Activation Scales (BIS/ BAS), the Barratt Impulsiveness Scale, and the Zuckerman Sensation-Seeking Scale. Our results suggest that risk-taking is governed more by concern for a loss (measured by BIS) than desire for a win (measured by BAS), although both variables impact risk-taking preferences. We also find that Sensation-Seeking relates more to the chances (probability) of a win than the amount (utility) of a win. Impulsivity did not affect players’ choices in either game, presumably because it affects the choice to play or not play in the first place. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Risk-taking; Personality; Gambling; Assessment
*
Corresponding author. Tel.: +1 216 368 6468; fax: +1 216 368 4891. E-mail address: [email protected] (H.A. Demaree).
0191-8869/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2008.01.005
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1. Introduction Mathematically and economically, risky choices are governed by the product of probability and utility, called expected utility (Bernoulli, 1738; Edwards, 1954). Psychologically, however, experiments have shown that participants often prefer a sure-thing of $W (paid at probability P = 1) over a gamble for a larger amount $J (paid at probability P < 1, while $0 is paid at probability 1 P) even when the expected utility (U) of each option is equal (W = P J) (Tversky & Kahneman, 1992). The same experiments have also shown that preferences can change with the magnitude of W or P, such that sometimes participants prefer the gamble over the economically equivalent sure-thing. These findings suggest that risky choices are driven by more than just expected utility. This paper explores the basis for choices in a ‘‘risk space” (Fig. 1) where subjective expected utility (U0 ) may vary with W and P but objective expected utility (U) is constant. To do so we use a simplified slot machine in which each trial involves a wager (W) to win a jackpot (J), where the jackpot is paid at probability P such that expected utility is equal to zero (U = P J W = 0). In this case, any preference for W or P implies a cognitive-subjective U0 that deviates from the normative-objective U. Numerous theories have been proposed to explain such preferences in terms of subjective utilities and/or subjective probabilities. For example, in Prospect Theory (Kahneman & Tversky, 1979), subjective utilities are modeled with a non-linear ‘‘value function” while subjective probability is modeled with a non-linear ‘‘weighting function”. These non-linear functions are parametrically fit to data across a population of participants, thereby modeling average biases in subjective utility and probability. The present study instead focuses on individual differences to determine how risk-taking preferences are affected by perceptual-personality traits. Our method uses two slot-like games, called the ‘‘W-game” and the ‘‘P-game,” where all parameters of each trial are fixed except for one P
P-game
avgP
avgW W-game
0.13
W U
$4
Fig. 1. Illustration of a risk space (W, P, U) for slot games. Lines at W = $4 and P = 0.13 in the plane U = 0 show the portion of risk space tested in the present study, using a W-game and a P-game. In the W-game, a player chooses W along the horizontal line P = 0.13. In the P-game, a player chooses P along the vertical line W = $4. Average values of W for the W-game and P for the P-game, chosen by players, are indicated at points avg W = $5.21 and avg P = 0.45. These points reflect the average peaks in subjective U0 for the two games (W and P) where objective U = 0.
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parameter in each game that a participant adjusts in a series of trials. In both games, U = 0. In the W-game, P is fixed while a participant adjusts W and J is automatically adjusted (J = W/P) to keep U = 0. In the P-game, W is fixed while a participant adjusts P and J is automatically adjusted (J = W/P) to keep U = 0. In each game a participant will presumably select the dependent variable (W or P) in order to optimize his or her subjective expected utility (U0 ). A participant’s selections will presumably be affected by aspects of his or her personality, which are the independent variables. Thus the purpose of our experiment is to relate risk-taking preferences (for W and P, with U = 0) to perceptual-personality variables. With respect to perceptual-personality traits (our independent variables), the first two constructs we employ are the Behavioral Inhibition System and Behavioral Activation Systems (BIS/BAS) – measured by Carver and White’s (1994) BIS/BAS scales. BIS and BAS are two components to Gray’s Reinforcement Sensitivity Theory (RST) (e.g., Gray, 1994). High BIS predicts increased feelings of anxiety and withdrawal behavior to perceived threat, which is controlled by serotonergic pathways from the raphe nucleus to septo-hippocampal systems (e.g., Gray, 1994). High BAS is associated with greater positive affective experience and increased goal-directed behavior to appetitive stimuli; BAS appears to be the product of the brain’s dopaminergic reward pathways emanating from the ventral tegmental area to the nucleus accumbens and ventral striatum (e.g., Gray, 1990). Although no known data exist relating BIS and BAS to gambling-related behavior, we note that high BAS and BIS have been found to mediate emotional reactions and drives towards/away from appetitive and threatening behavior, respectively (e.g., Carver, Meyer, & Antoni, 2000; Gable, Reis, & Elliot, 2000). In risk-taking situations like slot games, where the outcome could be a net loss or a net win, we expect that the combination (ratio) of BIS/BAS will govern risk-taking as inhibition competes with activation. More specifically, we expect that risktaking will decrease as the ratio of BIS/BAS is increased, for both the W-game and P-game. Impulsivity and Sensation-Seeking are two other perceptual-personality variables that may affect risk-taking behavior. Impulsivity has been characterized by cognitive deficits on neuropsychological measures tapping the ability to plan, shift attention, and inhibit prepotent responses (e.g., Cavedini, Riboldi, Keller, D’Annucci, & Bellodi, 2002; Petry, 2001), which are abilities that appear to be associated with frontal cortex functioning (e.g., Chao & Knight, 1995). Impulsivity may be assessed using self-report measures such as the Barratt Impulsiveness Scale, Version 11 (BIS-11, Patton, Stanford, & Barratt, 1995). Because impulsivity has been found to predict increased risk-taking in real-world gambling (e.g., Clarke, 2006; Fuentes, Tavares, Artes, & Gorenstein, 2006), we expect it to do so in our W-game and P-game as well. Sensation-Seeking, originally proposed by Zuckerman, Kolin, Price, and Zoob (1964), has been defined as a trait whereby one seeks ‘‘varied, novel, complex sensations and experiences” (p. 27, Zuckerman, 1994). Sensation-Seeking is perhaps most frequently assessed via the Sensation-Seeking Scale, Form 5 (SSS-V, Zuckerman, 1994). Because Sensation-Seeking has been found to predict increased risk-taking in real-world gambling (e.g., Gupta, Derevensky, & Ellenbogen, 2006), and because both large rewards (Wgame) and low probability events (P-game) are rare (by definition, in the case of the P-game), we expect Sensation-Seeking to predict increased risk-taking on both W- and P-games. Throughout this study we use the term ‘‘increased risk-taking” to mean higher W in the W-game or lower P in the P-game. This usage reflects the fact that there would be no risk (i.e., no uncertainty in the outcomes of choices) if W = 0 or P = 1, hence risk-taking might be said
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to increase as W is increased (W > 0) or P is decreased (P < 1). More formally, these measures of ‘‘risk-taking” (March & Shapira, 1987) can be seen as reflecting variability in the set {win, loss} of expected utilities {P (J W), (1 P) W} for possible outcomes of each trial. Using J = W/P (because our case has U = 0), the set can be re-written as {W (1 P), W (1 P)}, which shows that W (1 P) is a measure of variability around the average expect utility (U = 0). This measure of variability will increase as W increases and P decreases (1 P increases).
2. Methods Fifty-nine undergraduate students from a private, Midwestern University participated in the present study. They received course credit for their participation. 2.1. Procedure Participants were first informed about the procedures of the experiment and provided written consent if they agreed to participate. They were then asked to complete a number of self-report questionnaires and subsequently asked to perform two versions of a repeated gambling task created in-house (described below). Following completion of these tasks, subjects were debriefed and thanked for their participation. 2.2. Questionnaires
(1) The BIS/BAS Scales (Carver & White, 1994) consist of 20 questions which assess BIS and BAS sensitivity. The BIS Scale (7 items) measures one’s general propensity to avoid negative, threatening stimuli. The BAS Scale (13 items) assesses the degree to which one approaches appetitive/rewarding stimuli, and may be subdivided into the following subscales: Reward Responsiveness (RR, 5 items tapping how emotionally reactive one is to positive events), Drive (D, 4 items measuring the degree to which one pursues rewards), and Fun Seeking (FS, 4 items assessing one’s propensity to seek and engage in fun activities). The internal consistency of BIS and the three BAS subscales range from .66 to .76 (Carver & White, 1994). (2) The Barratt Impulsiveness Scale, Version 11 (BIS-11, Patton et al., 1995) consists of 30 questions designed to measure impulsivity in such a way that the construct is orthogonally related to anxiety. The BIS-11 can be divided into three subscales – Attentional Impulsiveness (AI, measuring the propensity for racing thoughts and the inability to maintain focus), Motor Impulsiveness (MI, assessing one’s propensity to act in the ‘‘spur of the moment” and have a varied life-style), and Non-Planning Impulsiveness (NPI, measuring lack of planning and need for mental challenge). The internal consistency of the subscales range from .79 to .83 (Patton et al., 1995). (3) The Zuckerman Sensation-Seeking Scale, Form 5 (SSS-V, Zuckerman, 1994) consists of 40 forced-choice questions which may be divided into four different subscales (10 questions each) – Boredom Susceptibility (BS; assessing intolerance of monotony), Thrill and Adventure Seeking (TAS; measuring propensity towards physically dangerous pursuits), Experi-
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ence Seeking (ES; assessing life-style changes and need for cognitive stimulation), and Disinhibition (D; measuring outgoing social behavior). Because of Institutional Review Board concerns regarding questions involving drug use and sexual conduct, the D subscale was not administered (thus, only 30 questions of the SSS-V were included). The internal reliability of the SSS-V is .72 (Zuckerman, 1994). 2.3. Risk-taking measure To investigate how these perceptual-personality variables relate to different dimensions of risktaking, we designed and used the Cognitive-Affective Slot Experiments (CASE)1 computer program. CASE allows parameters (U, P, J, or W) of slot machines to be varied by experimenters and/or participants in order to suit experimental purposes. The present research was performed with two versions of CASE, one called the ‘‘W-game” and the other called the ‘‘P-game”. Participants played both versions of the game, which were counter-balanced across subjects. In both games, participants were given 50 fictitious dollars and asked to play the game for 25 trials (or until they ran out of money). In the W-game, participants varied their wager amount (W, in dollars and cents) on each trial while U and P were fixed by experimenters (U = 0 and P = 0.13). This is similar to a real slot machine, where U and P are fixed by the casino and a player can (on many machines) adjust W to his/her liking by inserting more or less money into the machine. In the W-game we set P = 0.13 because this is the actual aggregate P (over all possible payoffs) of a typical casino slot machine (Scarne, 1961). In the P-game, participants instead varied the probability of winning (P, in percent from 1% to 100%) as desired while W was fixed at $4. Although the W-game is more similar to familiar slot machines, the ecological validity of both games is uncertain because our CASE does not capture potential stimulators, distracters, and social features of gambling present in real-world venues. Participants were informed that, for every dollar they had in their stack following completion of trial #25, they would receive 1 ticket for a $50 raffle at the end of the semester. To keep both games independent from one another, participants were informed that there would be two $50 raffles – one for the W-game and one for the P-game. In the W-game, our dependent variable measuring risk-taking preference was the average wager (avg W) across a participant’s total number of trials. In the P-game, our dependent variable measuring risk-taking preference was the average probability (avg P) across a participant’s total number of trials.
3. Results 3.1. Participants Fifty-nine participants (27 women, 32 men) with a mean age of 19.34 (SD = 1.41, range: 18–23) volunteered for the experiment. For the W-game and P-game, 46/59 (total # of trials M = 22.19, 1
The CASE program and instructions for its use may be acquired by e-mailing HAD.
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SD = 6.27) and 58/59 (total # of trials M = 24.95, SD = 0.39) participants played all 25 trials (i.e., they did not run out of money), respectively. 3.2. Risk-taking behavior The overall mean for avg W was $5.21 (SD = $5.99) and for avg P was 45.20 (SD = 18.19). Men (avg W: M = $5.72, SD = $5.70; avg P: M = 44.54, SD = 19.07) and women (avg W: M = $4.61, SD = $6.37; avg P: M = 45.98, SD = 17.42) were statistically equivalent in their risk-taking behavior (W-game: F(1, 57) = 0.50, p > .05; P-game: F(1, 57) = 0.09, p > .05), and counter-balancing did not impact risk-taking behavior in either game, Fs < 1. 3.3. Personality and risk-taking behavior To assess the relationship between perceptual-personality variables and risk-taking behavior, we first report the correlation matrix between variables. Next, to determine whether the correlational relationships were due to the independent effects of the perceptual-personality measures as opposed to shared variance between measures, we report follow-up regressions. The avg W in the W-game trended to be inversely related to the avg P selected in the P-game, r = .25, p = .06. That is, people who wagered more in the W-game trended to select lower probability in the P-game. This is consistent with the notion that ‘‘risk-taking” can indeed be characterized as variability in the expected utility of the set {win, loss} of possible outcomes, which increases as W increases in the W-game (W > 0, where W = 0 is ‘‘no risk”) and as P decreases in the P-game (P < 1, where P = 1 is ‘‘no risk”). Descriptive data for the perceptual-personality measures, as well as the correlations between these variables and risk-taking behavior in the W-game and P-game are presented in Table 1. With regard to perceptual-personality variables, the first major finding was that BAS was significantly associated with risk-taking in the W-game only. That is, people with higher BAS-Total scores wagered a greater amount in the W-game. This finding appears to be largely driven by BAS-D and BAS-FS, which were highly correlated with avg W. Tests of differences between Pearson correlations revealed that risk-taking in the W-game was significantly more sensitive to BAS-Total than was risk-taking in the P-game, Z = 1.71, p < .05, BAS-D, Z = 1.83, p < .05, and BAS-FS, Z = 2.34, p < .01. The second major finding was that BIS significantly correlated with risk-taking in both games, as did the ratio of BIS/BAS. The third major finding was that Impulsivity was not significantly associated with risk-taking behavior in either game. The fourth major finding was that Sensation-Seeking2 significantly correlated with risk-taking in the P-game only. Specifically, people with higher Sensation-Seeking scores (the Zuckerman-Total score [which, again, excluded the D subscale], and on the Zuckerman-BS and -ES subscales) had lower avg P in the P-game. Results for the P-game were more sensitive than those of the
2
The internal reliability of the SSS-V in our sample (which excluded the D subscale) was .70.
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Table 1 Descriptive statistics of perceptual-personality measures, as well as Pearson product-moment correlations between these variables and the average wager selected in W-game (avg W) and the average probability selected in P-game (avg P) Mean (SD) [range] BIS BAS-Total BAS-RR BAS-D BAS-FS BIS/BAS ratio Barratt-Total AI MI NPI Zuckerman-Total BS TAS ES
19.86 39.41 14.12 13.76 11.53 .51 66.97 18.66 23.90 24.41 15.92 3.83 6.93 5.15
(4.04) [7–29] (5.08) [29–52] (1.94) [7–16] (2.69) [9–20] (2.64) [7–16] (.12) [.13–.83] (10.65) [41–93] (3.48) [9–27] (4.91 [13–36] (4.92) [13–36] (5.07) [5–26] (1.66) [1–8] (2.80) [0–10] (1.78) [2–9]
Avg W *
0.303 .359** .004 .380** .301* .392** .078 .123 .156 .100 .123 .067 .171 .020
Avg P .401** .053 .240 .054 .131 .325* .070 .011 .110 .050 .372** .422*** .191 .366**
*
p < .05. p < .01. *** p < .001. **
W-game to the Zuckerman-Total score, Z = 2.72, p < .01, the Zuckerman-BS Scale, Z = 2.74, p < .01, and the Zuckerman-ES Scale, Z = 2.14, p < .05.3 To determine whether the above results were due to the independent effects of the perceptualpersonality measures, two separate standard multiple regressions were performed. Specifically, avg W and avg P were independently regressed onto the four total perceptual-personality scores (i.e., BIS, BAS-Total, Barratt-Total, and Zuckerman-Total [excluding the D subscale]). Both avg W, F(4, 54) = 4.06, p < .01, r2 = 23.1%, and avg P, F(4, 54) = 4.15, p < .01, r2 = 23.5%, were significantly predicted by the perceptual-personality measures4. Of note, higher BAS-Total predicted increased risk-taking in the W-game only (W-game: t[54] = 3.08, p < .01, unstandardized b = .342, sr2 = 13.5%; P-game: t[54] = 1.37, p > .05, b = .672, sr2 = 2.6%) and higher Zuckerman-Total scores predicted increased risk-taking in the P-game only (W-game: t[54] = 0.97, p > .05, b = .116; P-game: t[54] = 2.07, p < .05, b = 1.096, sr2 = 6.0%). Higher BIS predicted decreased risk-taking on both measures (W-game: t[54] = 2.55, p < .05, b = .348, sr2 = 9.2%; P-game: t[54] = 2.05, p < .05, b = 1.241, sr2 = 5.9%) and Barratt-Total score did not predict risk-taking behavior in either game (W-game: t[54] = 0.64, p > .05, b = .003; P-game: t[54] = 0.13, p > .05, b = 0.031). These results strengthen our finding that higher BIS predicts decreased risk-taking in both games, whereas higher BAS and Sensation-Seeking predict increased risk-taking in the W-game and P-game, respectively. 3
Post-hoc power analyses revealed that the correlations between BAS-Total/avg P (power = .11), Impulsivity-Total/ avg W (.15), Impulsivity-Total/avg P (.13), and Zuckerman-Total [excluding D subscale]/avg W (.24) were insufficiently powered. It was pragmatically impossible to recruit an N large enough to achieve sufficient power. 4 Tolerance statistics for these regressions ranged from .65 to .79, indicating that multicollinearity issues were not of practical concern.
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4. Discussion The major findings can be summarized as follows: (1) Lower levels of the BIS/BAS ratio, as well as lower BIS alone, are associated with increased risk-taking in both the W-game and the P-game. (2) Higher levels of Impulsivity do not significantly correlate with risk-taking in either the W-game or the P-game. (3) Higher levels of Sensation-Seeking are associated with increased risk-taking in the P-game but not the W-game. With respect to BIS/BAS, our finding was expected given that risky choices involve a tradeoff between the potential for a net win and the potential for a net loss. In particular, the equation for U can be written as a sum of two terms, each of which involves the product of ‘‘chances” (P or 1 P) and ‘‘amount” (J W or W): U = P (J W) (1 P) W, which reduces to U = P J W. Since U is a combination of chances amount for reward (win) and punishment (loss), the choice of U should be governed by the interaction between activation (BAS) and inhibition (BIS). Because the BIS/BAS ratio predicts risk-taking in both the W-game and P-game, our results suggest that this is indeed the case. With respect to BIS and BAS individually, there is a fundamental asymmetry between the Wgame and P-game, as evidenced by the equation: U = P (J W) (1 P) W. For BIS, notice that the W-game varies the amount of a loss (W) and controls for the chances (1 P) of a loss, while the P-game varies the chances of a loss (1 P) and controls for the amount (W) of a loss. Thus the amount and chances of potential loss are isolated in the W-game and P-game, respectively. Assuming that higher BIS favors lower amounts and lower chances of a loss, higher BIS should cause increased risk-taking in both games – as we see in Table 1. For BAS, notice that the W-game varies the amount of a win (J W) and controls for the chances (P) of a win, while the P-game varies both the chances of a win and the amount of a win (because J is adjusted along with P, per J = W/P, to ensure that U = 0). As such, the amount and chances of a potential win are confounded in the P-game, whereby higher amounts won occur at a lower probability, and vice versa. Assuming that higher BAS favors higher amounts and higher chances of a win, it is not surprising that BAS would cause increased risk-taking in the W-game only (where chances and amount are not confounded) – as we see in Table 1. Also with respect to BIS and BAS, the fundamental asymmetry between games (noted above) allows us to infer the relative contributions of BIS and BAS to the combined effects of BIS/BAS. In particular, Table 1 shows that BIS (unlike BAS) produces risk-taking similar to BIS/BAS for both avg W in the W-game and avg P in the P-game. This suggests that the competition between inhibition (BIS) and activation (BAS) is governed more by BIS than BAS. In other words, it appears that risky choices in both games are driven more by concern for a loss (inhibition) than desire for a win (activation). This is consistent with the well-known insight from experiments on Prospect Theory, which is that even under economic equivalence it appears that ‘‘losses loom larger than gains” (Kahneman & Tversky, 1984). Neurophysiologically, it is also consistent with McNaughton and Gray’s (2000) revision of RST, which suggests that BIS does not mediate reactions to threatening stimuli per se but, rather, aids in the resolution of conflict by propelling the organism towards a non-conflict state. In the present research, this notion was supported because people with higher BIS opted for less conflict by selecting lower Ws in the W-game (with W = 0 representing zero conflict) and higher Ps in the P-game (with P = 1 representing no conflict).
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With respect to Impulsivity, we found that Barratt scores did not significantly affect choices in either the W-game or P-game, even though previous studies have associated Impulsivity with gambling frequency/severity (e.g., Clarke, 2006; Fuentes et al., 2006). One explanation may be that participants in our W-game and P-game were forced to gamble. That is, Impulsivity may be more associated with the choice to gamble in the first place rather than choosing between various gambles. With respect to Sensation-Seeking (excluding the D subscale), it is interesting that this variable predicted risk-taking in the P-game but not the W-game, especially since the BIS/BAS ratio predicted risk-taking in both games. The finding suggests that there is some aesthetic utility (i.e., ‘‘sensation”) beyond economic utility, and that aesthetic utility varies more in the P-dimension than the W-dimension of risk space. In other words, it appears that the novelty of a win or loss (Burns, 2007) may matter more than the amount of money that is won or lost – at least when U = 0. Thus in our ‘‘fair” games, it appears that players select P (and perhaps W as well) in order to optimize a subjective utility that includes more than just economic utility. Looking beyond our case of U = 0, Sensation-Seeking may also help explain why people accept so-called ‘‘irrational” gambles in real slot machines (where U < 0 because of the ‘‘house rake”), i.e., presumably because the aesthetic fun outweighs the economic cost. Our findings raise both concerns and opportunities for those interested in the assessment of risk-taking behavior. Of concern, previous studies using neuroimaging technologies have used similar tasks to determine (in part) the brain areas underlying decision-making in cases involving financial risk (e.g., Beer, Knight, & D’Esposito, 2006; Kuhnen & Knutson, 2005). Overlapping findings might be understood in terms of BIS/BAS (and especially BIS), whereas differential findings may be attributable to mechanisms underlying reward (BAS) and Sensation-Seeking preferences. In such research, the joint use of games that measure preferences along both dimensions (W and P) of a risk space may be beneficial to the identification of double-dissociations. On the opportunity front, the same approach may help assess the causal basis for risk-taking in individuals with extreme levels of BAS or Sensation-Seeking. In particular, risk-taking in the two games might be used to establish whether an individual’s preferences relate more to BAS (which appears to drive risk-taking in the W-game) or Sensation-Seeking (which appears to drive risk-taking in the P-game), as well as the types of risks members of specific populations avoid/seek. Populations with low BAS, high BAS, and high Sensation-Seeking may include the clinically depressed, people evidencing mania, and pathological gamblers, respectively (e.g., Depue & Zald, 1993; Fowles, 1994; Gupta et al., 2006). We cautiously suggest that the CASE protocol may be useful in identifying individuals within such clinical populations who may be most responsive to therapeutic intervention and, in addition, could perhaps be a useful adjunct when measuring treatment response. Here, we should point out that risk-taking is neither inherently ‘‘good” nor ‘‘bad”; although risk without regard to safety can clearly be self-destructive, risk-taking may also be important to the creation of well-being (e.g., Hosen, Stern, & Solovey-Hosen, 2004). The present research had some notable limitations. First, it focused only on financial risk-taking (e.g., not drug-related, sexual, etc., although associations with such forms of risk may exist) among healthy undergraduates within a limited age range, and failed to counter-balance questionnaire administration before and after the CASE. In the future, researchers may want to expand on this work by determining how risk-taking changes with demographics, or in various clinical populations, or how genetics may impact specific risk-taking behaviors (Kreek, Nielsen, Butelman, &
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LaForge, 2005). Future work may also explore the impact of emotional responses to wins/losses on subsequent risk-taking behavior, and how such relationships are moderated by personality, demographics, genetics, and the like. Moreover, we explored only a small portion of a risk space (Fig. 1), along the lines of W = $4 (P-game) and P = 0.13 (W-game) in the plane where U = 0. The effects of perceptual-personality differences on risk-taking preferences may be quite different in other portions of the W–P risk space, especially at more extreme values of W or P. Finally, our study was limited to just two dimensions (W and P) of risk space when in fact the third dimension (U) may be the most important of all – since differences in U are the only cases for which people should (from a normative-economic perspective) exhibit preferences in their choices.
Acknowledgements The authors thank Daria Babeshko and Mariya Topolyanskaya for their assistance on this project.
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