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In the control condition, participants had to solve three simple arithmetic problems (e.g., ��17 ? 5 = ?��) instead of visualizing negative risk consequences. Each risky situation was followed by six questions [http://www.selleckchem.com/products/MK-1775.html MK-1775 ic50] concerning: (1) emotions evoked by negative risk consequences (three questions), (2) risk perception (two questions), and (3) the intensity of mental images of risk (one question; by asking about intensity, we intended to measure the vividness of mental images of risk and the strength of mental representation; words ��intense�� and ��vivid�� are synonyms in the Polish language, so we might have assumed that when participants rated images of risk as more intense, they also rated them as more clear and vivid at the same time). Responses to each question [http://www.selleckchem.com/products/KU-55933.html KU55933] were provided on a 10-point scale (the exact wording of these questions and descriptive statistics are shown in Table ?Table1).1). At the final stage of the experiment, participants were asked to recall risky situations presented in the study to check whether they were similarly involved in the processing of risky situations regardless of the condition (imagery vs. solving arithmetic problems). In this task, five target and five distractor risky situations were displayed on the computer screen in a randomized order, and participants indicated which risky situation was actually shown to them in the study. Table 1 Descriptive statistics for measures used in Experiment 1. Statistical analysis In each experiment, we fitted a linear mixed model using the [http://en.wikipedia.org/wiki/PSCD4 CYTH4] lme4 (Bates et al., 2014) and the lmerTest packages implemented in the R statistical environment (R Core Team, 2014). In each model, risk perception was predicted by the experimental manipulation (i.e., images of negative risk consequences vs. control) and measures of evoked emotions. We also treated participants and a risk domain as random-intercept effects, whereas emotions were random-slope effects allowed to vary across participants and different risk domains. To test the indirect effect between the experimental manipulation and risk perception through changes in emotions we constructed lower and upper limits of the 95% confidence interval for the indirect path using the Monte Carlo simulation method (Preacher and Selig, 2012) based on 10,000 random samples. If the confidence interval for the indirect effect did not contain zero, we could conclude that the mediation effect was significant (Hayes, 2013). Results Manipulation check We found that participants in the experimental condition rated their mental images of risky situations as more intense than controls, b = 1.23, p