Volz and Gigerenzer Decisions under risk versus uncertainty for these predictions (Hertwig et al., 2002). In many situations, fairness und justice are achieved by distributing resources equally. Our normative argument has fundamental consequences for the neuroscience of decision making: Claims that the rational brain always works by Bayesian calculations are founded on the assump- tion that what is rational in a world of risk is also rational in an uncertain world – the world our brain has to deal with most of the time. These claims are also incompatible with three well-known restrictions: Bayesian optimization is not feasible if (i) the choice alternatives are not known for sure, (ii) the mind has more than one goal, and (iii) even if all alternatives were known and the mind had only one goal, the calculations can quickly become compu- tationally intractable, that is, no mind can actually perform them in a lifetime (Gigerenzer, 2004). Bayesian inference works in small worlds where there are reliable data for probabilities and only a few alternatives and cues. COGNITIVE PROCESSES IN SITUATIONS OF RISK 6D PROCESSES IN SITUATIONS OF UNCERTAINTY In the previous section, we argued that what is optimal in a world of risk is typically not the best in a world of uncertainty. Conse- quently, an adapted brain relies on different processes according to the situation. When faced with risk, using heuristics is of little value, unless the computations become too difﬁcult. When faced with uncertainty, using logic and statistics is of little value, unless the part of the problem that is known is being calculated. We would like to emphasize the importance of the distinction between risky and uncertain worlds for the neuroscientiﬁc investi- gation of decision making. So far, its focus has been on small world problems. But just as normative results from studying cognition in small worlds do not automatically generalize to what people should do in uncertain worlds, we cannot be sure that descrip- tive results generalize either. Inﬂuenced by small-word theories of decision making, neuroscientists, and neuroeconomists have nev- ertheless relied heavily on the “gambling paradigm” as a model for exploring the neural correlates. In a typical neuroeconomic paradigm, participants are presented with the choice between two options, Option A and Option B, which differ with respect to objective dimensions such as the magnitude and the probability of reward (as assigned by the experimenter). Reward is largely deﬁned as monetary value, which the participant will receive after the functional session. These problems require entirely different skills and strategies than decisions under uncertainty. For example, although calculating the expected value might sufﬁce for a lot- tery, it will not be sufﬁcient for deciding whether to be vaccinated against swine ﬂu, which share to buy, or whom to marry. Results such as the ﬁnding that “activity in the ventral striatum during the evaluation of monetary gambles is non-linear in prob- abilities in the pattern predicted by prospect theory” (Hsu et al., 2009, p. 2231) may capture the neural activation pattern when comparing gambles. Concluding that this activity pattern will also be observed when searching for jobs or mates, however, is not war- ranted. The pattern predicted by prospect theory in fact disappears and even reverses when the probabilities are not provided by the experimenter but the participant instead has to learn these from experience, a phenomenon known as the description-experience gap (Hertwig and Erev, 2009). Nor do ﬁndings from small worlds easily translate into a cognitive process model, that is, testing for the neural correlates of some form of utility model cannot elu- cidate the cognitive mechanisms in large world problems. In the words of Colombo and Seriès (2012): “that the brain is a Bayesian machine does not follow from the fact that Bayesian models are used to study the brain and the behavior it generates” (p. 2). In an uncertain world, there is broad experimental evidence that humans and other animals rely on a toolbox of heuristics. These are based on evolved and learned core capacities and include (for details, see Gigerenzer and Gaissmaier, 2011): – Recognition-based heuristics: Recognition heuristic (RH; see below); ﬂuency heuristic1. – Equality-based heuristics: 1/N (see above); tallying (weight reasons equally). – One-good-reason heuristics: take-the-best (see below); fast-and- frugal trees2. – Social heuristics: tit-for-tat; imitate-the-majority. What would a neuroscientiﬁc investigation of heuristic decision making look like? One approach is to study the neural correlates of heuristic processes, such as search rules, stopping rules, and deci- sion rules (e.g., Volz et al., 2006, 2010; Khader et al., 2011; Rosburg et al., 2011). In what follows, we provide two illustrations for how to go beyond lotteries and study the neural correlates of the use of cognitive heuristics in an uncertain world. NEURAL CORRELATES OF HEURISTIC DECISIONS IN UNCERTAIN WORLDS Note that studying decision making under uncertainty (as opposed to risk) does not require squeezing the complexity of the large world into the laboratory. It simply requires studying tasks where not all alternatives, consequences, and probabilities are known for sure or provided by the experimenter. RECOGNITION HEURISTIC Consider a simple heuristic that humans and other animals use to make inferences about an uncertain world (Goldstein and Gigerenzer, 2002): Recognition heuristic: If one of two objects is recognized and the other is not, then infer that the recognized object has the higher value with respect to the criterion. 1The ﬂuency heuristic is a simple heuristic that can be used to exploit recognition memory and is deﬁned in the following way: If two objects are recognized, and one of objects is more ﬂuently retrieved, then infer that this object has the higher value with respect to criterion; where retrieval ﬂuency is deﬁned as how long it takes to retrieve a trace from long-term memory (c.p. Schooler and Hertwig, 2005). 2Fast-and-frugal decision trees are simple rules for categorization; they are fast- and-frugal since they allow a classiﬁcation decision at each level of the tree (c.p. Martignon et al., 2011). For binary predictors, a fast-and-frugal tree has n C 1 exists, while a full tree has 2n exits. An example is the Simple Triage and Rapid Treatment (START) procedure, which is used to categorize patients into those who need imme- diate medical treatment and those whose treatment can be delayed (Super, 1984). By using the START, “a paramedic sequentially checks up to ﬁve diagnostic cues to decide which category a person falls into; a decision can be made after each cue is checked” (Luan et al., 2011, p. 316). By using such a simple and transparent decision tree, the decision maker/paramedic does not need to search for and integrate all the relevant cues so as to reach a sound decision. www.frontiersin.org July 2012 | Volume 6 | Article 105 | 3