Previous research shows that individuals who tend to get bored frequently and intensely—the highly boredom prone—are more likely to engage in risky behaviors. However, these studies are based largely on self-reports. Here we address this gap and suggest that noisy decision-making (DM) is a potential driver for this relationship between boredom proneness and risk-taking. In Study 1, eighty-six participants completed the Balloon Analogue Risk Task (BART) while EEG was recorded. We found blunted feedback processing with higher boredom proneness, as indexed by reduced feedback-P3 amplitudes. Risk taking, as indexed by the BART, was not higher in the highly boredom prone. In Study 2a ( N = 404) we directly tested the noisy DM hypothesis in an online sample using a binary choice task, and found that with higher boredom proneness, participants were more likely to alternate between choices on a trial-to-trial basis, but were not more likely to choose the risky alternative. These findings were replicated in a new sample (Study 2b), and extended to the Iowa Gambling Task (IGT; Study 3). In the IGT we found increased choice switching and reduced feedback sensitivity with higher boredom proneness. Once again, higher risk taking as indexed by the IGT was not evident in the highly boredom prone. Overall, our findings suggest that boredom proneness is associated with noisy decision-making (i.e., a tendency to alternate more between choice options regardless of risk level), and not risk-seeking per se. That is, the highly boredom prone are not necessarily attracted to risks, but rather, may be insensitive to risks due to reduced feedback sensitivity.

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Boredom, the unfulfilled desire to be engaged in a satisfying mental activity, is an aversive state characterized by poor self-regulation. There is ample evidence that both state and trait boredom are associated with failures of attention in both experimental and everyday settings. The neural correlates of boredom, however, remain underexplored. We recorded electroencephalographic signal from 83 participants during a resting state and while performing a go/no-go task. We found a negative correlation between trait boredom proneness and power in the alpha and theta bands during the resting state. Furthermore, higher levels of task-induced boredom were associated with reduced amplitudes for the P3 and error-related negativity eventrelated potentials. Increased commission error rates were also associated with higher levels of task-induced boredom. We conclude that state and trait boredom are associated with inadequate engagement of attentional resources.

New technology can be used to enhance safety by imposing costs, or taxes, on certain reckless behaviors. The current paper presents two pre-registered experiments that clarify the impact of taxation of this type on decisions from experience between three alternatives. Experiment 1 focuses on an environment in which safe choices maximize expected returns and examines the impact of taxing the more attractive of two risky options. The results reveal a U-shaped effect of taxation: some taxation improves safety, but too much taxation impairs safety. Experiment 2 shows a clear negative effect of high taxation even when the taxation eliminates the expected benefit from risk-taking. Comparison of alternative models suggests that taxing reckless behaviors backfires when it significantly increases the proportion of experiences in which a more dangerous behavior leads to better outcomes than the taxed behavior. Qualitative hypotheses derived from naïve sampling models assuming small samples were only partially supported by the data.

Experience is the best teacher. Yet, in the context of repeated decisions, experience was found to trigger deviations from maximization in the direction of underweighting of rare events. Evaluations of alternative explanations for this bias led to contradicting conclusions. Studies that focused on the aggregate choice rates, including a series of choice prediction competitions, favored the assumption that this bias reflects reliance on small samples. In contrast, studies that focused on individual decisions suggest that the bias reflects a strong myopic tendency by a significant minority of participants. The current analysis clarifies the apparent inconsistency by reanalyzing a data set that previously led to contradicting conclusions. Our analysis suggests that the apparent inconsistency reflects the differing focus of the cognitive models. Specifically, sequential adjustment models (that assume sensitivity to the payoffs’ weighted averages) tend to find support for the hypothesis that the deviations from maximization are a product of strong positive recency (a form of myopia). Conversely, models assuming random sampling of past experiences tend to find support to the hypothesis that the deviations reflect reliance on small samples. We propose that the debate should be resolved by using the assumptions that provide better predictions. Applying this solution to the data set we analyzed shows that the random sampling assumption outperforms the weighted average assumption both when predicting the aggregate choice rates and when predicting the individual decisions.