In our everyday life we often have to make decisions with uncertain consequences, for instance in the context of investment decisions. To successfully cope with these situations, the nervous system has to be able to estimate, represent, and eventually resolve uncertainty at various levels. That is, not only are there different forms of uncertainty with different consequences for behavior and learning but research indicates that the processing of uncertainty highly depends on situation and context. The present research topic includes both review and original research articles that seek to shed light on the neural processes underlying decision making under uncertainty with a particular focus on situational and contextual influences.First, Bland and Schaefer (2012) review the diverse (and often overlapping) definitions of uncertainty. They identify three main forms-expected uncertainty (including risk), unexpected uncertainty and volatility-and review theoretical and empirical evidence that supports this dissociation. Several original research articles then aim to either directly compare different forms of uncertainty or to identify further dissociations within these forms. Payzan-LeNestour and Bossaerts (2012) systematically vary unexpected and estimation uncertainty to study what drives exploration (as opposed to exploitation). They report that humans both seek out new reward opportunities ("curiosity motive") and avoid the unknown ("cautiousness motive"), resulting in exploration and exploitation, respectively. O'Reilly (2013) addresses the same forms of uncertainty in the context of learning with a particular focus on how an organism should adapt their rate of learning in changing environments. Hansen et al. (2012) on the other hand show that decisions made under perceptual vs. categorical uncertainty are differentially affected by prior knowledge such that prior knowledge increases visual cortical activity when uncertainty is driven by the sensory stimulus itself rather than at the cognitive level.The next set of papers explores situational and contextual aspects of expected uncertainty. First, Studer et al. (2012) demonstrate that neural responses in a distributed network of choice under risk increase when subjects actively choose a risky gamble as opposed to being passively exposed to risk when a computer chooses that gamble. Kim et al. (2012) study what information decision makers attend to when either choosing between two lotteries or betting on a single lottery. Using eye-tracking data they observe task-dependent attentional shifts from probabilities to amounts which may influence the (neural) computation of value. Consequently, individuals often chose options with higher probabilities but place higher bids on options with higher amounts. Schönberg et al. (2012) used the Balloon Analog Risk Taking task to study the neural network underlying naturalistic risk-taking. They find that brain activity in a network previously related to risk increases as individuals continue to inflate a balloonthus, increasing...
