Some studies have found that choices become more risk averse after gains and more risk seeking after losses, although other studies have found the opposite. The latter tend to use hypothetical cases that encourage deliberation. In the current study, we examined the effects of prior gains and losses on a task designed to encourage less reflective decision making, the Iowa Gambling Task (IGT). Fifty participants conducted a manipulated decision-making task in which one group gained money, whereas the other group lost money, followed by the IGT. Participants who experienced a prior monetary loss displayed more risky choice behavior on the IGT than subjects who experienced a prior gain. These effects were not mediated by a positive or negative affect, although the sample size may have been too small to detect a small effect.

The concept of “passive risk taking”, which refers to the risk brought on or magnified by inaction, has recently appeared in the literature on risk taking. Keinan and Bereby-Meyer (2012) have developed a scale to measure the personal tendency for passive risk taking (PRT); the scale has criterion validity and high test-retest reliability; it correlates with reported passive risk taking in everyday life and with the DOSPERT scale. Furthermore, it presents divergent validity from classic risk-taking constructs such as sensation seeking, and convergent validity with procrastination and avoidance. In this paper we propose a validation of the PRT scale in Italian. We performed the linguistic adaptation to Italian via the five steps suggested by Guillemin and colleagues (1993) and Beaton and colleagues (2000); we then submitted the derived questionnaire to a 297-adult sample. Results show that two out of three factors from the original scale were confirmed. However the third factor, originally composed of 6 items, was not consistent. We present the scale derived from such results, and discuss the differences with the original scale.

We compare four different risk-taking propensity measures on their ability to describe and to predict actual risky behavior in the domain of health. The risk-taking propensity measures we compare are: (1) a general measure of risk-taking propensity derived from a one-item survey question (Dohmen et al., 2011), (2) a risk aversion index calculated from a set of incentivized monetary gambles (Holt & Laury, 2002), (3) a measure of risk taking derived from an incentive compatible behavioral task—the Balloon Analog Risk Task (Lejuez et al., 2002), and (4) a composite score of risk-taking likelihood in the health domain from the Domain-Specific Risk Taking (DOSPERT) scale (Weber et al., 2002). Study participants are 351 clients of health centers around Witbank, South Africa. Our findings suggest that the one-item general measure is the best predictor of risky health behavior in our population, predicting two out of four behaviors at the 5% level and the remaining two behaviors at the 10% level. The DOSPERT score in the health domain performs well, predicting one out of four behaviors at the 1% significance level and two out of four behaviors at the 10% level, but only if the DOSPERT instrument contains a hypothetical risk-taking item that is similar to the actual risky behavior being predicted. Incentivized monetary gambles and the behavioral task were unrelated to actual health behaviors; they were unable to predict any of the risky health behaviors at the 10% level. We provide evidence that this is not because the participants had trouble understanding the monetary trade-off questions or performed poorly in the behavioral task. We conclude by urging researchers to further test the usefulness of the one-item general measure, both in explaining health related risk-taking behavior and in other contexts.

Research carried out in different cultural contexts shows that the use of exclusively coercive disciplinary measures does not improve the behavior of those punished, and may even increase the risks underpinning those behaviors. The aim of this research was to study whether there is a link between repeatedly suffering punishment at school and psychosocial risks in adolescence. A non-experimental design was implemented with selected groups. The participants were 507 adolescents from four groups with different risk levels: in social protection (n = 189); subject to court measures (n = 104); in treatment for drug abuse (n = 25); and comparison group (n = 189). A questionnaire was applied collectively. The variables measured were school punishments, violence, drug consumption and commission of crimes. The mild punishments variable predicted and increased the probability of consuming alcohol, tobacco and cannabis by 34% (95% CI [1.1, 1.5]), and increased the probability of using illegal drugs by 11% (95% CI [1.11, 1.30]). Te severe punishments variable increased the probability of using illegal drugs by 86% (95% CI [1.41, 2.49]) and increased the probability of committing crimes by 40% (95% CI [1.13, 1.73]). School punishments, particularly if severe, stand as a visible indicator of psychosocial risk. Behaviors subjected to punishment should alert us to the need to intervene with individuals who manifest them for which the use of exclusively coercive measures is ineffective. A wider educational intervention is required to help them find their place in school instead of excluding them from it.

The purpose of this paper is to study the differential and complementary role played by the theory of planned behavior (TPB) variables and by participants' emotions when recalling and describing previous experiences of such risk behavior in the prediction of the intention to repeat a risk behavior in the immediate future. We chose the behavior of occasional excessive drinking, a risk behavior characterized by evoking attitudinal ambivalence and eliciting mixed emotions, joy and sadness. The results show that emotional ambivalence is not equivalent to attitudinal ambivalence (whose indexes include that of the affective component), and that this emotional information is relevant for predicting the intention to repeat the risk behavior in the near future, enhancing the prediction of the TPB model.

We tested how anticipated emotions interact with personal experience in risk behavior to improve predictions from TPB on behavioral intention (BI) and behavioral expectation (BE) for sex without condom (Study 1) and excessive drinking (Study 2). In the moderate-high experience group, anticipated emotional profiles (AEPs) improve TPB prediction from 28% to 45% in the case of BI and from 19% to 40% in that of BE in relation to sexual risk behavior (Study 1), and from 23% to 36% in the case of BI and from 17% to 31% in that of BE in relation to binge drinking (Study 2). However, in the low-experience group (Study 2) AEPs improve TPB predictions for BI (12% to 34%) but not for BE, showing that in less experienced people BI and BE are not equivalent: anticipated emotions have different relevance in their prediction. These results were replicated using a general negative anticipated emotion index (averaging emotional categories).