This special issue on recognition processes in inferential decision making represents an adversarial collaboration among the three guest editors. This introductory article to the special issue’s third and final part comes in three sections. In Section 1, we summarize the six papers that appear in this part. In Section 2, we give a wrap-up of the lessons learned. Specifically, we discuss (i) why studying the recognition heuristic has led to so much controversy, making it difficult to settle on mutually accepted empirically grounded assumptions, (ii) whether the development of the recognition heuristic and its theoretical descriptions could explain some of the past controversies and misconceptions, (iii) how additional cue knowledge about unrecognized objects could enter the decision process, (iv) why recognition heuristic theory should be complemented by a probabilistic model of strategy selection, and (v) how recognition information might be related to other information, especially when considering real-world applications. In Section 3, we present an outlook on the thorny but fruitful road to cumulative theory integration. Future research on recognition-based inferences should (i) converge on overcoming past controversies, taking an integrative approach to theory building, and considering theories and findings from neighboring fields (such as marketing science and artificial intelligence), (ii) build detailed computational process models of decision strategies, grounded in cognitive architectures, (iii) test existing models of such strategies competitively, (iv) design computational models of the mechanisms of strategy selection, and (v) effectively extend its scope to decision making in the wild, outside controlled laboratory situations.

We compared Turkish and English students’ soccer forecasting for English soccer matches. Although the Turkish students knew very little about English soccer, they selected teams on the basis of familiarity with the team (or its identified city); their prediction success was surprisingly similar to knowledgeable English students—consistent with Goldstein and Gigerenzer’s (1999; 2002) characterization of the recognition heuristic. The Turkish students made forecasts for some of the matches with additional information—the half-time scores. In this and a further study, where British students predicting matches for foreign teams could choose whether or not to use half-time information, we found that predictions that could be made by recognition alone were influenced by the half-time information. We consider the implications of these findings in the context of Goldstein and Gigerenzer’s (2002, p. 82) suggestion that “… no other information can reverse the choice determined by recognition” and a recent more qualified statement (Gigerenzer & Goldstein, 2011) indicating that two processes, recognition and evaluation guide the adaptive selection of the recognition heuristic.

The advantage of models that do not use flexible parameters is that one can precisely show to what degree they predict behavior, and in what situations. In three issues of this journal, the recognition heuristic has been examined carefully from many points of view. We comment here on four themes, the use of optimization models to understand the rationality of heuristics, the generalization of the recognition input beyond a binary judgment, new conditions for less-is-more effects, and the importance of specifying boundary conditions for cognitive heuristics.

Marketing science seeks to prescribe better marketing strategies (advertising, product development, pricing, etc.). To do so we rely on models of consumer decisions grounded in empirical observations. Field experience suggests that recognition-based heuristics help consumers to choose which brands to consider and purchase in frequently-purchased categories, but other heuristics are more relevant in durable-goods categories. Screening with recognition is a rational screening rule when advertising is a signal of product quality, when observing other consumers makes it easy to learn decision rules, and when firms react to engineering-design constraints by offering brands such that a high-level on one product feature implies a low level on another product feature. Experience with applications and field experiments suggests four fruitful research topics: deciding how to decide (endogeneity), learning decision rules by self-reflection, risk reduction, and the difference between utility functions and decision rules. These challenges also pose methodological cautions.

The diversity, ingenuity and differences of opinion displayed in the articles of the recent special issues on the recognition heuristic are testament to the power and theoretical fertility of a simple idea about the role of recognition in decision making. In this brief comment I mention a number of these papers, but my focus is on points of agreement and disagreement with the conclusions drawn by Gigerenzer and Goldstein (2011) in their review of a decade’s worth of research on the recognition heuristic.

The recognition heuristic models the adaptive use and dominant role of recognition knowledge in judgment under uncertainty. Of the several predictions that the heuristic makes, empirical tests have predominantly focused on the proposed noncompensatory processing of recognition. Some authors have emphasized that the heuristic needs to be scrutinized based on precise tests of the exclusive use of recognition. Although precise tests have clear merits, I critically evaluate the value of such tests as they are currently employed. First, I argue that using precise measures of the exclusive use of recognition has to go beyond showing that the recognition heuristic—like every model—cannot capture reality completely. Second, I illustrate how precise tests based on response times can lead to unsubstantiated conclusions if the fact that the recognition heuristic does not model the recognition judgment itself is ignored. Finally, I highlight two key but so far neglected aspects of the recognition heuristic: (a) the connection between recognition memory and the recognition heuristic; and (b) the mechanisms underlying the adaptive use of recognition.

I describe and discuss the sometimes heated controversy surrounding the recognition heuristic (RH) as a model of inferential decision making. After briefly recapitulating the history of the RH up to its current version, I critically evaluate several specific assumptions and predictions of the RH and its surrounding framework: recognition as a memory-based process; the RH as a cognitive process model; proper conditions of testing the RH; measures of using the RH; reasons for not using the RH; the RH as a non-compensatory strategy; evidence for a Less-is-more effect (LIME); and the RH as part of the toolbox. The collection of these controversial issues may help to better understand the debate, to further sharpen the RH theory, and to develop ideas for future research.