Hypotheses about decision processes are often formulated qualitatively and remain silent about the interplay of decision, memorial, and other cognitive processes. At the same time, existing decision models are specified at varying levels of detail, making it difficult to compare them. We provide a methodological primer on how detailed cognitive architectures such as ACT-R allow remedying these problems. To make our point, we address a controversy, namely, whether noncompensatory or compensatory processes better describe how people make decisions from the accessibility of memories. We specify 39 models of accessibility-based decision processes in ACT-R, including the noncompensatory recognition heuristic and various other popular noncompensatory and compensatory decision models. Additionally, to illustrate how such models can be tested, we conduct a model comparison, fitting the models to one experiment and letting them generalize to another. Behavioral data are best accounted for by race models. These race models embody the noncompensatory recognition heuristic and compensatory models as a race between competing processes, dissolving the dichotomy between existing decision models.

Three experiments investigated the relations between buyers’ wealth or ability to pay (ATP) and sellers’ first offers. Study 1 demonstrated a positive correlation between sellers’ first offers and their perceptions of the buyer’s ATP as well as its real economic power (indicated by the company’s market value). In Study 2, sellers in a field experiment made higher offers to potential buyers of higher ATP. Study 3 examined the relations between buyer’s ATP, the perception of its ability to obtain alternatives to a specific deal, and sellers’ first offers. We found a positive correlation between sellers’ perception of buyers’ ATP, real ATP (as indicated by market value), and sellers’ perception of buyers’ availability of alternatives. As in Study 1, here too, the unit of analysis was the behavior of the individual participant. However, when sellers were primed to concentrate on buyers’ alternatives, their first offers were negatively related to perceived buyer’s alternatives.

Similar to research on risky choice, the traditional analysis of intertemporal choice takes the view that an individual behaves so as to maximize the discounted sum of all future utilities. The well-known Allais paradox contradicts the fundamental postulates of maximizing the expected value or utility of a risky option. We describe a violation of the law of diminishing marginal utility as well as an intertemporal version of the Allais paradox.

Recent research has emphasized emotion’s role in non-utilitarian judgments, but has not focused much on characteristics of subjects contributing to those judgments. The present article relates utilitarian judgment to individual disposition to experience various emotions. Study 1 first investigated the relationship among state emotions and utilitarian judgment. Diverse emotions were elicited during judgment: guilt, sadness, disgust, empathy, anger, and anxiety, etc. Using psychological scales, Study 2 found that trait emotions predict the extent of utilitarian judgments, especially trait anger, trait disgust, and trait empathy. Unlike previous research that designated emotions only as factors mitigating utilitarian judgment, this research shows that trait anger correlates positively with utilitarian judgment. On the other hand, disgust and empathy correlated negatively. Guilt and shame—though previous research argued that their absence increased utilitarian judgment—appear unrelated to the extent of utilitarian judgment. These results suggest that people’s emotional dispositions can affect their judgment. This finding might contribute to untangling the complex mechanisms of utilitarian judgments.

In this introduction to the special issue on methodology, we provide background on its original motivation and a systematic overview of the contributions. The latter are discussed with correspondence to the phase of the scientific process they (most strongly) refer to: Theory construction, design, data analysis, and cumulative development of scientific knowledge. Several contributions propose novel measurement techniques and paradigms that will allow for new insights and can thus avail researchers in JDM and beyond. Another set of contributions centers around how models can best be tested and/or compared. Especially when viewed in combination, the papers on this topic spell out vital necessities for model comparisons and provide approaches that solve noteworthy problems prior work has been faced with.

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.

The dual process model of moral judgment (DPM; Greene et al., 2004) argues that such judgments are influenced by both emotion-laden intuition and controlled reasoning. These influences are associated with distinct neural circuitries and different response tendencies. After reanalyzing data from an earlier study, McGuire et al. (2009) questioned the level of support for the dual process model and asserted that the distinction between emotion evoking moral dilemmas (personal dilemmas) and those that do not trigger such intuitions (impersonal dilemmas) is spurious. Using similar reanalysis methods on data reported by Moore, Clark, & Kane (2008), we show that the personal/impersonal distinction is reliable. Furthermore, new data show that this distinction is fundamental to moral judgment across widely different cultures (U.S. and China) and supports claims made by the DPM.

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.

We present the Concordant-Ranks (CR) strategy that decision makers use to quickly find an alternative that is proximate to an ideal alternative in a multi-attribute decision space. CR implies that decision makers prefer alternatives that exhibit concordant ranks between attribute values and attribute weights. We show that, in situations where the alternatives are equal in multi-attribute utility (MAU), minimization of the weighted Euclidean distance (WED) to an ideal alternative implies the choice of a CR alternative. In two experiments, participants chose among, as well as evaluated, alternatives that were constructed to be equal in MAU. In Experiment 1, four alternatives were designed in such a way that the choice of each alternative would be consistent with one particular choice strategy, one of which was the CR strategy. In Experiment 2, participants were presented with a CR alternative and a number of arbitrary alternatives. In both experiments, participants tended to choose the CR alternative. The CR alternative was on average evaluated as more attractive than other alternatives. In addition, measures of WED, between given alternatives and the ideal alternative, by and large agreed with the preference order for choices and attractiveness evaluations of the different types of alternatives. These findings indicate that both choices and attractiveness evaluations are guided by proximity of alternatives to an ideal alternative.

Would you redirect a trolley to save five people even if it means that the trolley will run over a person on the side track? Most people say they would. Would you push that same person into the path of the trolley in order to save the five? Most people say they would not. These sorts of intuitive moral judgments are made rapidly and seem almost automatic. Now imagine a combined choice context where one can redirect a trolley, push a person in its path or do nothing. The number of lives lost from intervening can be varied. The most straightforward interpretations of current theories of moral judgment predict either no effect or that the combined context will lead to greater focus on lives lost. In contrast, we observe a similarity effect such that utilitarian choice may become less attractive in the combined choice context.

According to the recognition heuristic (RH) theory, decisions follow the recognition principle: Given a high validity of the recognition cue, people should prefer recognized choice options compared to unrecognized ones. Assuming that the memory strength of choice options is strongly correlated with both the choice criterion and recognition judgments, the RH is a reasonable strategy that approximates optimal decisions with a minimum of cognitive effort (Davis-Stober, Dana, & Budescu, 2010). However, theories of recognition memory are not generally compatible with this assumption. For example, some threshold models of recognition presume that recognition judgments can arise from two types of cognitive states: (1) certainty states in which judgments are almost perfectly correlated with memory strength and (2) uncertainty states in which recognition judgments reflect guessing rather than differences in memory strength. We report an experiment designed to test the prediction that the RH applies to certainty states only. Our results show that memory states rather than recognition judgments affect use of recognition information in binary decisions.

We make hundreds of decisions every day, many of them extremely quickly and without much explicit deliberation. This motivates two important open questions: What is the minimum time required to make choices with above chance accuracy? What is the impact of additional decision-making time on choice accuracy? We investigated these questions in four experiments in which subjects made binary food choices using saccadic or manual responses, under either “speed” or “accuracy” instructions. Subjects were able to make above chance decisions in as little as 313 ms, and choose their preferred food item in over 70% of trials at average speeds of 404 ms. Further, slowing down their responses by either asking them explicitly to be confident about their choices, or to respond with hand movements, generated about a 10% increase in accuracy. Together, these results suggest that consumers can make accurate every-day choices, akin to those made in a grocery store, at significantly faster speeds than previously reported.

This paper investigates the boundaries of the recent result that eliciting more than one estimate from the same person and averaging these can lead to accuracy gains in judgment tasks. It first examines its generality, analysing whether the kind of question being asked has an effect on the size of potential gains. Experimental results show that the question type matters. Previous results reporting potential accuracy gains are reproduced for year-estimation questions, and extended to questions about percentage shares. On the other hand, no gains are found for general numerical questions. The second part of the paper tests repeated judgment sampling’s practical applicability by asking judges to provide a third and final answer on the basis of their first two estimates. In an experiment, the majority of judges do not consistently average their first two answers. As a result, they do not realise the potential accuracy gains from averaging.

According to Karl Popper, we can tell good theories from poor ones by assessing their empirical content (empirischer Gehalt), which basically reflects how much information they convey concerning the world. “The empirical content of a statement increases with its degree of falsifiability: the more a statement forbids, the more it says about the world of experience.” Two criteria to evaluate the empirical content of a theory are their level of universality (Allgemeinheit) and their degree of precision (Bestimmtheit). The former specifies how many situations it can be applied to. The latter refers to the specificity in prediction, that is, how many subclasses of realizations it allows. We conduct an analysis of the empirical content of theories in Judgment and Decision Making (JDM) and identify the challenges in theory formulation for different classes of models. Elaborating on classic Popperian ideas, we suggest some guidelines for publication of theoretical work.

The current field study compares the time preferences of young adults of similar ages but in two very different environments, one more dangerous and uncertain than the other. Soldiers, college students and a control group of teenagers answered questionnaires about their time preferences. During mandatory service, soldiers live in a violent atmosphere where they face great uncertainty about the near future and high risk of mortality (measured by probability of survival). University students and teenagers live in much calmer environment and are tested for performance only periodically. The soldier-subjects show relatively high subjective discount rates when compared to the other two groups. We suggest that the higher subjective discount rate among soldiers can be the result of high perceived risk in the army as an institution, or higher mortality risk.

Binary sequences are characterized by various features. Two of these characteristics—alternation rate and run length—have repeatedly been shown to influence judgments of randomness. The two characteristics, however, have usually been investigated separately, without controlling for the other feature. Because the two features are correlated but not identical, it seems critical to analyze their unique impact, as well as their interaction, so as to understand more clearly what influences judgments of randomness. To this end, two experiments on the perception of binary sequences orthogonally manipulated alternation rate and maximum run length (i.e., length of the longest run within the sequence). Results show that alternation rate consistently exerts a unique effect on judgments of randomness, but that the effect of alternation rate is contingent on the length of the longest run within the sequence. The effect of maximum run length was found to be small and less consistent. Together, these findings extend prior randomness research by integrating literature from the realms of perception, categorization, and prediction, as well as by showing the unique and joint effects of alternation rate and maximum run length on judgments of randomness.

Fiedler et al. (2009), reviewed evidence for the utilization of a contingency inference strategy termed pseudocontingencies (PCs). In PCs, the more frequent levels (and, by implication, the less frequent levels) are assumed to be associated. PCs have been obtained using a wide range of task settings and dependent measures. Yet, the readiness with which decision makers rely on PCs is poorly understood. A computer simulation explored two potential sources of subjective validity of PCs. First, PCs are shown to perform above chance level when the task is to infer the sign of moderate to strong population contingencies from a sample of observations. Second, contingency inferences based on PCs and inferences based on cell frequencies are shown to partially agree across samples. Intriguingly, this criterion and convergent validity are by-products of random sampling error, highlighting the inductive nature of contingency inferences.

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.

How many judgment and decision making (JDM) researchers have not claimed to be building on Herbert Simon’s work? We identify two of Simon’s goals for JDM research: He sought to understand people’s decision processes—the descriptive goal—and studied whether the same processes lead to good decisions—the prescriptive goal. To investigate how recent JDM research relates to these goals, we analyzed the articles published in the Journal of Behavioral Decision Making and in Judgment and Decision Making from 2006 to 2010. Out of 377 articles, 91 cite Simon or we judged them as directly relating to his goals. We asked whether these articles are integrative, in the following sense: For a descriptive article we asked if it contributes to building a theory that reconciles different conceptualizations of cognition such as neural networks and heuristics. For a prescriptive article we asked if it contributes to building a method that combines ideas of other methods such as heuristics and optimization models. Based on our subjective judgments we found that the proportion of integrative articles was 67% of the prescriptive and 52% of the descriptive articles. We offer suggestions for achieving more integration of JDM theories. The article concludes with the thesis that although JDM researchers work under Simon’s spell, no one really knows what that spell is.