Volz and Gigerenzer Decisions under risk versus uncertainty In the actual decision making task, participants were pre- sented with only the names of two companies and then had to infer, by using the TTB heuristic, which company will be more successful in the next year. To do so, participants had to retrieve all the relevant attribute information from long-term memory. The attributes with which the two companies were described consisted of visual information known to be represented in dif- ferent parts of the posterior cortex, e.g., in a face-speciﬁc and in a house-speciﬁc region of interest. That allowed the authors to examine activation within these regions of interest as a function of the number of the to-be-retrieved attributes. Given the cog- nitive process model of the TTB heuristic, Khader et al. (2011) expected the activation in the regions of interest to be systemati- cally modulated by the relative importance of the information for making a decision. Their speciﬁc analyses revealed a controlled retrieval shown by a selective boosting of activation, speciﬁcally in those regions that represent the attributes that were relevant for the decision. For example, activation strongly increased in the face-speciﬁc region solely in those trials in which faces were rel- evant for the decision. Furthermore, a prolonged response to an attribute was found only when it was relevant late in the deci- sion process, when the attribute was low in importance. All in all, the data showed a “selective modulation of neural activation that follows the retrieval order according to TTB” (p. 11), which the authors take to support the notion of controlled retrieval processes. Thus, by using fMRI the authors could provide evidence in favor of the cognitive process model’s prediction for the decision phase, i.e., using the TTB heuristic is indeed associated with a con- trolled activation of decision-relevant attribute representations. As in the case of the RH, the imaging study was used to compare two competing models, exhaustive search as assumed in standard weighting-and-adding models and limited search as deﬁned by take-the-best. STUDY THE NEURAL CORRELATES OF PROCESS MODELS, NOT AS-IF MODELS Why are experimental studies with situations of known risk, such as lotteries, so popular? This is especially puzzling given that lotter- ies, roulette, and other tasks with known risks are quite a recent in human history. One answer is that they facilitate application of sta- tistical optimization models, such as expected utility and Bayesian updating, which are also quite recent achievements in human his- tory. However, studies of cognitive processes have provided little evidence that the mind engages in expected utility calculations during decision making; instead, there is reliable evidence for the use of heuristics (see Ford et al., 1989; Payne et al., 1993; Friedman and Sunder, 2011). For instance, Friedman and Sunder (p. 1) con- cluded in their review of the literature on risky choice from 1950 to 2010: No such functions [utility or similar Bernoulli functions] have yet been found that are useful for out-of-sample pre- dictions. Nor do we ﬁnd practical applications of Bernoulli functions in major risk-based industries such as ﬁnance, insurance, and gambling. The important methodological concept is “out-of-sample pre- diction.” Expected utility theory or its variants such as prospect theory can easily ﬁt their parameters to the data after the fact, but the real test is in prediction, not ﬁtting. “Out-of-sample” means that the parameters of a model, heuristic, or optimiz- ing, are ﬁtted to one part (e.g., half) of the sample and the other part is tested. This is an elementary form of uncertainty, where not all data are known. Most importantly, neoclassical economists have never claimed that the brain computes expected utilities but explicitly emphasize that optimization models do not describe the cognitive process. Following Friedman’s (1953) as-if methodology, economists consider these models only as tools for prediction, making deliberately “wrong” assumptions that are mathematically convenient. Unfortunately many cog- nitive neuroscience studies appear to be unaware of this con- ceptual problem and search for the neural correlates of “as-if ” models. CONCLUSION We distinguished between two kinds of problems humans face: worlds of risk or worlds of uncertainty. In a world of risk (small world), all relevant alternatives, their probabilities, and their con- sequences are known for sure and the future is certain. In contrast, in a world of uncertainty (large world) part of the information is unknown or has to be estimated from small samples, and surprises can happen. The second distinction we introduced is between what decisions people make (the outcome) and how they make them (the process). Answering the ﬁrst question leads to as-if models; answering both questions leads to process models. We argue that the two distinctions are correlated: As-if models tend to match small world studies, whereas process models tend to match large world studies. We pointed out the strong focus on decision making under risk in neuroscientiﬁc studies, which pay little attention to how the brain makes adaptive decisions in an uncertain world. That becomes problematic when the normative and descriptive results are generalized to how the brain deals with an uncertain world. In addition, we provided evidence that the normative solution under risk is not the best one under uncertainty. We also pro- vided evidence that the cognitive processes for decisions in a world of risk are not the same as in a world of uncertainty. The study of behavior in lotteries – and other small world tasks – does not address the question of how humans make decisions when the conditions for rationality postulated by the model of neoclassical economics are not met, a question empha- sized by Simon (1989). In large worlds, people cannot opti- mize but instead “satisﬁce” by relying on the brain’s adaptive toolbox. In sum, the current focus of cognitive neuroscience studies on situations where all risks are known and optimization is possible imposes limits on the understanding of adaptive brain processes, both normatively and descriptively. The neural correlations of cognitive processes such as heuristic search, stopping rules, and aspiration levels have little chance of being detected and may even be taken for correlates of expected utility and other as-if theories. www.frontiersin.org July 2012 | Volume 6 | Article 105 | 5