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In the wake of the COVID-19 pandemic, many journals swiftly changed their editorial policies and peer-review processes to accelerate the provision of knowledge about COVID-related issues to a wide audience. These changes may have favoured speed at the cost of accuracy and methodological rigour. In this study, we compare 100 COVID-related articles published in four major psychological journals between 2020 and 2022 with 100 non-COVID articles from the same journal issues and 100 pre-COVID articles published between 2017 and 2019. Articles were coded with regard to design features, sampling and recruitment features, and openness and transparency practices. Even though COVID research was, by and large, more ‘observational’ in nature and less experimentally controlled than non- or pre-COVID research, we found that COVID-related studies were more likely to use ‘stronger’ (i.e. more longitudinal and fewer cross-sectional) designs, larger samples, justify their sample sizes based on a priori power analysis, pre-register their hypotheses and analysis plans and make their data, materials and code openly available. Thus, COVID-related psychological research does not appear to be less rigorous in these regards than non-COVID research.

Abstract When someone violates a social norm, others may think that some sanction would be appropriate. We examine how the experience of emotions like anger and disgust relate to the judged appropriateness of sanctions, in a pre-registered analysis of data from a large-scale study in 56 societies. Across the world, we find that individuals who experience anger and disgust over a norm violation are more likely to endorse confrontation, ostracism and, to a smaller extent, gossip. Moreover, we find that the experience of anger is consistently the strongest predictor of judgments of confrontation, compared to other emotions. Although the link between state-based emotions and judgments may seem universal, its strength varies across countries. Aligned with theoretical predictions, this link is stronger in societies, and among individuals, that place higher value on individual autonomy. Thus, autonomy values may increase the role that emotions play in guiding judgments of social sanctions.

Abstract The emergence of COVID-19 dramatically changed social behavior across societies and contexts. Here we study whether social norms also changed. Specifically, we study this question for cultural tightness (the degree to which societies generally have strong norms), specific social norms (e.g. stealing, hand washing), and norms about enforcement, using survey data from 30,431 respondents in 43 countries recorded before and in the early stages following the emergence of COVID-19. Using variation in disease intensity, we shed light on the mechanisms predicting changes in social norm measures. We find evidence that, after the emergence of the COVID-19 pandemic, hand washing norms increased while tightness and punishing frequency slightly decreased but observe no evidence for a robust change in most other norms. Thus, at least in the short term, our findings suggest that cultures are largely stable to pandemic threats except in those norms, hand washing in this case, that are perceived to be directly relevant to dealing with the collective threat.

Little is known about people’s preferred responses to norm violations across countries. Here, in a study of 57 countries, the authors highlight cultural similarities and differences in people’s perception of the appropriateness of norm violations.

In two pre-registered and fully incentivized studies (N = 501), we investigate prosocial behavior during the COVID-19 pandemic in Germany. Based on a comparison with pre-pandemic data, Study 1 shows that individuals’ general prosociality measured with a (context-independent) Social Value Orientation Slider changed significantly before vs. during the early stage of the pandemic towards increased prosociality. In addition, we identified pandemic-specific context factors for prosocial behavior measured by a series of Dictator games with different recipients. Allocations in the Dictator game increased with the giver's responsibility and the recipients’ vulnerability to the virus. Study 2 replicates and extends this finding in a sample representative for the adult German population in terms of age and gender. We show that, for different actors (self, recipient, politicians), different dimensions of responsibility (responsibility to help, responsibility for the problem cause, and for the solution) are associated with prosocial behavior. Contrary to what could be expected from diffusion of responsibility, prosocial behavior increased not only when individuals themselves felt responsible to help but also when they perceived politicians to be responsible to help. Assigning responsibility for the cause of the COVID-19 crisis to recipients and politicians was associated with a decrease in prosocial behavior. However, responsibility for the solution had no influence. We discuss implications for public policies.

We conducted two studies using a sample of students (Experiment 1, N=84) and the general public (Experiment 2, N=412) to assess the relative and unique effects of factors suggested by three major theories of law obedience: a utility-theoretic deterrence theory (Becker, 1968), the general theory of crime (Gottfredson and Hirschi, 1990), and the legitimacy model (Tyler, 1990). Six different types of low-level crime were considered. The probability of breaking the law increases with factors predicted by each of these theories, namely detection probability, expected fine, self-control, and legitimacy. All four factors uniquely contribute to predicting law obedience, effects are mainly additive, and no stable interaction effects are observed. The relative influence of the investigated factors varies between types of low-level crimes. This indicates that an integrative theory of why people obey the law needs to consider factors from various theories and allow for the relative influence of factors to differ among crimes. We observe systematic deviations from a basic utility-theoretic approach to law breaking. Individuals’ tendency to obey the law is much higher than predicted by an approach taking into account detection probability, expected fines, and benefits only. The robust effects of interindividual differences concerning legitimacy and self-control as well as the finding that the tendency to break the law decreases with increasing benefit of the crime also conflict with a basic utility-theoretic approach to law-obedience.

Over the past decade, the world has faced an unprecedented refugee crisis. The large number of incoming refugees represents a challenge for host societies and its citizens triggering reactions from a supportive welcome to brusque rejection and hostile behavior toward refugees. In a pre-registered study, we investigated factors that could promote altruistic behavior in fully incentivized one-shot Dictator Game toward various receiver groups including refugees. We find that host citizens behave more altruistically toward refugees and other receiver groups if they (a) share a local identity with them (i.e., live in the same city), and (b) perceive them to be close (to the self) and warm-hearted. Moreover, citizens that are (c) generally more prosocial and hold a more left-wing political orientation are more willing to give. Unexpectedly, from a theoretical point of view, altruistic giving toward refugees was not influenced in the predicted direction by a shared student identity, competition and perceived income differences (although the latter effect was significant when considering all receiver groups). For shared student identity we even observe a reduction of altruistic behavior, while the opposite effect was predicted. We discuss implications for public policies for successful refugee helping and integration.

Following the outbreak of COVID-19 pandemic, governments around the globe coerced their citizens to adhere to preventive health behaviours, aiming to reduce the effective reproduction numbers of the virus. Driven by game theoretic considerations and inspired by the work of US National Research Council's Committee on Food Habits (1943) during WWII, and the post-WWII Yale Communication Research Program, the present research shows how to achieve enhanced adherence to health regulations without coercion. To this aim, we combine three elements: (i) indirect measurements, (ii) personalized interventions, and (iii) attitude changing treatments (IMPACT). We find that a cluster of short interventions, such as elaboration on possible consequences, induction of cognitive dissonance, addressing next of kin and similar others and receiving advice following severity judgements, improves individuals' health-preserving attitudes. We propose extending the use of IMPACT under closure periods and during the resumption of social and economic activities under COVID-19 pandemic, since efficient and lasting adherence should rely on personal attitudes rather than on coercion alone. Finally, we point to the opportunity of international cooperation generated by the pandemic.

In a sample of 599 participants (60% female, 18–81 years), we tested the hypotheses that cognitive ability and the big-six personality traits suffice to explain the individual-difference component of paranormal beliefs (belief in magic, astrology, esoterism, supernatural beings, and spirituality). Additionally, we measured 14 other potential predictors that were found to correlate with paranormal beliefs in prior research (e.g., ontological confusion). Although cognitive ability and the big-six explained 10% of the variance in individual belief, ontological confusion and causality understanding also were significant predictors in regression analyses. The resulting model, explaining 19 % of variance, consists of ontological confusion, cognitive ability (negative correlation), openness to new experiences, emotionality, conscientiousness (neg. corr.) and causality understanding (neg. corr.). We discuss the findings with reference to two hypothetical factors that drive individuals' acceptance of paranormal beliefs, inclination for story-telling, and tendency to evaluate belief content in terms of reason and conscientious evaluation.

Adaptive actors must be able to use probabilities as decision weights. In a computerized multi-attribute task, the authors examined the decisions of children (5--6 years, n = 44; 9--10 y., n = 39) and adults (21--22 y., n = 31) in an environment that fosters the application of a weighted-additive strategy that uses probabilities as weights (WADD: choose option with highest sum of probability-value products). Applying a Bayesian outcome-based strategy classification procedure from adult research, we identified the utilization of WADD and several other strategies (lexicographic, equal weight, naïve Bayes, guessing, and saturated model) on the individual level. As expected based on theory, the prevalence of WADD-users in adults was high. In contrast, no preschoolers could be classified as users of probability-sensitive strategies. Nearly one-third of third-graders used probability-sensitive strategies.

As individuals from different nations increasingly interact with each other, research on national in-group favoritism becomes particularly vital. In a cross-national, large-scale study (N = 915) including representative samples from four Latin American nations (Chile, Peru, Colombia, Venezuela) and the USA, we explore differences regarding nationality-based in-group favoritism. In-group favoritism is assessed through differences in prosocial behavior towards persons from the own nation as compared to persons from other nations in fully incentivized one-shot dictator games. We find strong evidence for national in-group favoritism for the overall sample, but also significant differences among national subsamples. Latin Americans show more national in-group favoritism compared to US Americans (interacting with Latin Americans). While US Americans mainly follow an equal split norm (for both in- and out-group interactions), Latin Americans do so only in in-group interactions. The magnitude of in-group favoritism increases with social distance towards the out-group.

Danziger, Levav and Avnaim-Pesso (2011) analyzed legal rulings of Israeli parole boards concerning the effect of serial order in which cases are presented within ruling sessions. They found that the probability of a favorable decision drops from about 65% to almost 0% from the first ruling to the last ruling within each session and that the rate of favorable rulings returns to 65% in a session following a food break. The authors argue that these findings provide support for extraneous factors influencing judicial decisions and cautiously speculate that the effect might be driven by mental depletion. A simulation shows that the observed influence of order can be alternatively explained by a statistical artifact resulting from favorable rulings taking longer than unfavorable ones. An effect of similar magnitude would be produced by a (hypothetical) rational judge who plans ahead minimally and ends a session instead of starting cases that he or she assumes will take longer directly before the break. One methodological detail further increased the magnitude of the artifact and generates it even without assuming any foresight concerning the upcoming case. Implications for this article are discussed and the increased application of simulations to identify nonobvious rational explanations is recommended.

Online marketplaces allow consumers to leave reviews about the products they purchase, which are visible to potential customers and competitors. While the impact of reviews on valuations of worth and purchasing decisions has been intensively studied, little is known about how the reviews themselves are attended to, and the relation between attention and valuations. In three studies we use eye-tracking methodologies to investigate attention in subjective monetary valuations of consumer goods. We find that, when evaluating consumer goods, individuals' attention to ratings are related to their frequencies, attention to positive or negative information is related to subjective valuations, and that perspective (owner vs. non-owner) influences the type of information attended to. These findings extend previous research regarding the valuations of risky prospects as implemented in abstract monetary gambles and suggest that similar cognitive processes might underlie both types of tasks.

In a highly uncertain world, individuals often have to make decisions in situations with incomplete information. We investigated in three experiments how partial cue information is treated in complex probabilistic inference tasks. Specifically, we test a mechanism to infer missing cue values that is based on the discrimination rate of cues (i.e., how often a cue makes distinct predictions for choice options). We show analytically that inferring missing cue values based on discrimination rate maximizes the probability for a correct inference in many decision environments and that it is therefore adaptive to use it. Results from three experiments show that individuals are sensitive to the discrimination rate and use it when it is a valid inference mechanism but rely on other inference mechanisms, such as the cues’ base-rate of positive information, when it is not. We find adaptive inferences for incomplete information in environments in which participants are explicitly provided with information concerning the base-rate and discrimination rate of cues (Exp. 1) as well as in environments in which they learn these properties by experience (Exp. 2). Results also hold in environments of further increased complexity (Exp. 3). In all studies, participants show a high ability to adaptively infer incomplete information and to integrate this inferred information with other available cues to approximate the naïve Bayesian solution.

Glöckner and Bröder (2011) have shown that for 77.5% of their participants’ decision making behavior in decisions involving recognition information and explicitly provided additional cues could be better described by weighted-compensatory Parallel Constraint Satisfaction (PCS) Models than by non-compensatory strategies such as recognition heuristic (RH) or Take the Best (TTB). We investigate whether this predominance of PCS models also holds in memory-based decisions in which information retrieval is effortful and cognitively demanding. Decision strategies were analyzed using a maximum-likelihood strategy classification method, taking into account choices, response times and confidence ratings simultaneously. In contrast to the memory-based-RH hypothesis, results show that also in memory-based decisions for 62% of the participants behavior is best explained by a compensatory PCS model. There is, however, a slight increase in participants classified as users of the non-compensatory strategies RH and TTB (32%) compared to the previous study, mirroring other studies suggesting effects of costly retrieval.

The endowment effect has been debated for over 30 years. Recent research suggests that differential focus of attention might play a role in shaping preferences. In two studies we investigated the role of biased attention in the emergence of endowment effects. We thereby derive predictions from an extended version of evidence accumulation models by additionally assuming a bias in attentional allocation based on one’s endowment status. We test these predictions against an alternative account in which the endowment effect is the result of initial anchoring and adjustment differences (Sequential Value Matching model; Johnson & Busemeyer, 2005). In both studies we add deliberation time constraints to a standard Willingness-to-Accept/Willingness-to-Pay paradigm and consistently find that the endowment effect grows as deliberation time increases. In Study 2 we additionally use eye tracking and find that buyers focus more on value decreasing attributes than sellers (and vice versa for value increasing attributes). This shift in attention plays a pivotal role in the construction of value and partially mediates the endowment effect.

The calibration of probability or confidence judgments concerns the association between the judgments and some estimate of the correct probabilities of events. Researchers rely on estimates using relative frequencies computed by aggregating data over observations. We show that this approach creates conceptual problems, and may result in the confounding of explanatory variables or unstable estimates. To circumvent these problems we propose using probability estimates obtained from statistical models—specifically mixed models for binary data—in the analysis of calibration. We illustrate this methodology by re-analyzing data from a published study and comparing the results from this approach to those based on relative frequencies. The model-based estimates avoid problems with confounding variables and provided more precise estimates, resulting in better inferences.

It is a long known problem that the preferential publication of statistically significant results (publication bias) may lead to incorrect estimates of the true effects being investigated. Even though other research areas (e.g., medicine, biology) are aware of the problem, and have identified strong publication biases, researchers in judgment and decision making (JDM) largely ignore it. We reanalyzed two current meta-analyses in this area. Both showed evidence of publication biases that may have led to a substantial overestimation of the true effects they investigated. A review of additional JDM meta-analyses shows that most meta-analyses conducted no or insufficient analyses of publication bias. However, given our results and the rareness of non-significant effects in the literature, we suspect that biases occur quite often. These findings suggest that (a) conclusions based on meta-analyses without reported tests of publication bias should be interpreted with caution and (b) publication policies and standard research practices should be revised to overcome the problem.

The history of judgment and decision making is defined by a trend toward increasingly nuanced explanations of the decision making process. Recently, process models have become incredibly sophisticated, yet the tools available to directly test these models have not kept pace. These increasingly complex process models require increasingly complex process data by which they can be adequately tested. We propose a new class of data collection that will facilitate evaluation of sophisticated process models. Tracking mouse paths during a continuous response provides an implicit measure of the growth of preference that produces a choice—rather than the current practice of recording just the button press that indicates that choice itself. Recent research in cognitive science (Spivey & Dale, 2006) has shown that cognitive processing can be revealed in these dynamic motor responses. Unlike current process methodologies, these response dynamics studies can demonstrate continuous competition between choice options and even online preference reversals. Here, in order to demonstrate the mechanics and utility of the methodology, we present an example response dynamics experiment utilizing a common multi-alternative decision task.

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.

We present a classification methodology that jointly assigns to a decision maker a best-fitting decision strategy for a set of choice data as well as a best-fitting stochastic specification of that decision strategy. Our methodology utilizes normalized maximum likelihood as a model selection criterion to compare multiple, possibly non-nested, stochastic specifications of candidate strategies. In addition to single strategy with “error” stochastic specifications, we consider mixture specifications, i.e., strategies comprised of a probability distribution over multiple strategies. In this way, our approach generalizes the classification framework of Bröder and Schiffer (2003a). We apply our methodology to an existing dataset and find that some decision makers are best fit by a single strategy with varying levels of error, while others are best described as using a mixture specification over multiple strategies.

Currently, a disparity exists between the process-level models decision researchers use to describe and predict decision behavior and the methods implemented and metrics collected to test these models. The current work seeks to remedy this disparity by combining the advantages of work in decision research (mouse-tracing paradigms with contingent information display) and cognitive psychology (eye-tracking paradigms from reading and scene perception). In particular, we introduce a new decision moving-window paradigm that presents stimulus information contingent on eye fixations. We provide data from the first application of this method to risky decision making, and show how it compares to basic eye-tracking and mouse-tracing methods. We also enumerate the practical, theoretical, and analytic advantages this method offers above and beyond both mouse-tracing with occlusion and basic eye tracking of information without occlusion. We include the use of new metrics that offer more precision than those typically calculated on mouse-tracing data as well as those not possible or feasible within the mouse-tracing paradigm.

Judgment and decision making research overwhelmingly uses null hypothesis significance testing as the basis for statistical inference. This article examines an alternative, Bayesian approach which emphasizes the choice between two competing hypotheses and quantifies the balance of evidence provided by the data—one consequence of which is that experimental results may be taken to strongly favour the null hypothesis. We apply a recently-developed “Bayesian t-test” to existing studies of the anchoring effect in judgment, and examine how the change in approach affects both the tone of hypothesis testing and the substantive conclusions that one draws. We compare the Bayesian approach with Fisherian and Neyman-Pearson testing, examining its relationship to conventional p-values, the influence of effect size, and the importance of prior beliefs about the likely state of nature. The results give a sense of how Bayesian hypothesis testing might be applied to judgment and decision making research, and of both the advantages and challenges that a shift to this approach would entail.

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.

Hierarchical Bayesian methods offer a principled and comprehensive way to relate psychological models to data. Here we use them to model the patterns of information search, stopping and deciding in a simulated binary comparison judgment task. The simulation involves 20 subjects making 100 forced choice comparisons about the relative magnitudes of two objects (which of two German cities has more inhabitants). Two worked-examples show how hierarchical models can be developed to account for and explain the diversity of both search and stopping rules seen across the simulated individuals. We discuss how the results provide insight into current debates in the literature on heuristic decision making and argue that they demonstrate the power and flexibility of hierarchical Bayesian methods in modeling human decision-making.

Taking a falsificationist perspective, the present paper identifies two major shortcomings of existing approaches to comparative model evaluations in general and strategy classifications in particular. These are (1) failure to consider systematic error and (2) neglect of global model fit. Using adherence measures to evaluate competing models implicitly makes the unrealistic assumption that the error associated with the model predictions is entirely random. By means of simple schematic examples, we show that failure to discriminate between systematic and random error seriously undermines this approach to model evaluation. Second, approaches that treat random versus systematic error appropriately usually rely on relative model fit to infer which model or strategy most likely generated the data. However, the model comparatively yielding the best fit may still be invalid. We demonstrate that taking for granted the vital requirement that a model by itself should adequately describe the data can easily lead to flawed conclusions. Thus, prior to considering the relative discrepancy of competing models, it is necessary to assess their absolute fit and thus, again, attempt falsification. Finally, the scientific value of model fit is discussed from a broader perspective.

Often research in judgment and decision making requires comparison of multiple competing models. Researchers invoke global measures such as the rate of correct predictions or the sum of squared (or absolute) deviations of the various models as part of this evaluation process. Reliance on such measures hides the (often very high) level of agreement between the predictions of the various models and does not highlight properly the relative performance of the competing models in those critical cases where they make distinct predictions. To address this important problem we propose the use of pair-wise comparisons of models to produce more informative and targeted comparisons of their performance, and we illustrate this procedure with data from two recently published papers. We use Multidimensional Scaling of these comparisons to map the competing models. We also demonstrate how intransitive cycles of pair-wise model performance can signal that certain models perform better for a given subset of decision problems.

One major statistical and methodological challenge in Judgment and Decision Making research is the reliable identification of individual decision strategies by selection of diagnostic tasks, that is, tasks for which predictions of the strategies differ sufficiently. The more strategies are considered, and the larger the number of dependent measures simultaneously taken into account in strategy classification (e.g., choices, decision time, confidence ratings; Glöckner, 2009), the more complex the selection of the most diagnostic tasks becomes. We suggest the Euclidian Diagnostic Task Selection (EDTS) method as a standardized solution for the problem. According to EDTS, experimental tasks are selected that maximize the average difference between strategy predictions for any multidimensional prediction space. In a comprehensive model recovery simulation, we evaluate and quantify the influence of diagnostic task selection on identification rates in strategy classification. Strategy classification with EDTS shows superior performance in comparison to less diagnostic task selection algorithms such as representative sampling. The advantage of EDTS is particularly large if only few dependent measures are considered. We also provide an easy-to-use function in the free software package R that allows generating predictions for the most commonly considered strategies for a specified set of tasks and evaluating the diagnosticity of those tasks via EDTS; thus, to apply EDTS, no prior programming knowledge is necessary.

The two most influential models in delay discounting research have been the exponential (E) and hyperbolic (H) models. We develop a new methodology to design binary choice questions such that exponential and hyperbolic discount rates can be purposefully manipulated to make their rate parameters orthogonal (Pearson’s R = 0), negatively correlated (R = –1), positively correlated (R = +1), or to hold one rate constant while allowing the other to vary. Then we extend the method to similarly contrast different versions of the hyperboloid model. The arithmetic discounting model (A), which is based on differences between present and future rewards rather than their ratios, may easily be made orthogonal to any other pair of models. Our procedure makes it possible to design choice stimuli that precisely vary the relationship between different discount rates. However, the additional control over the correlation between different discount rate parameters may require the researcher to either restrict the range that those rate parameters can take, or to expand the range of times the participant must wait for future rewards.

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.

Narrow self-interest is often used as a simplifying assumption when studying people making decisions in social contexts. Nonetheless, people exhibit a wide range of different motivations when choosing unilaterally among interdependent outcomes. Measuring the magnitude of the concern people have for others, sometimes called Social Value Orientation (SVO), has been an interest of many social scientists for decades and several different measurement methods have been developed so far. Here we introduce a new measure of SVO that has several advantages over existent methods. A detailed description of the new measurement method is presented, along with norming data that provide evidence of its solid psychometric properties. We conclude with a brief discussion of the research streams that would benefit from a more sensitive and higher resolution measure of SVO, and extend an invitation to others to use this new measure which is freely available.

The aim of this article is to evaluate the contribution of process tracing data to the development and testing of models of judgment and decision making (JDM). We draw on our experience of editing the “Handbook of process tracing methods for decision research” recently published in the SJDM series. After a brief introduction we first describe classic process tracing methods (thinking aloud, Mouselab, eye-tracking). Then we present a series of examples of how each of these techniques has made important contributions to the development and testing of process models of JDM. We discuss the issue of large data volumes resulting from process tracing and remedies for handling those. Finally, we argue for the importance of formulating process hypotheses and opt for a multi-method approach that focuses on the cross-validation of findings.

Research on the processing of recognition information has focused on testing the recognition heuristic (RH). On the aggregate, the noncompensatory use of recognition information postulated by the RH was rejected in several studies, while RH could still account for a considerable proportion of choices. These results can be explained if either a) a part of the subjects used RH or b) nobody used it but its choice predictions were accidentally in line with predictions of the strategy used. In the current study, which exemplifies a new approach to model testing, we determined individuals’ decision strategies based on a maximum-likelihood classification method, taking into account choices, response times and confidence ratings simultaneously. Unlike most previous studies of the RH, our study tested the RH under conditions in which we provided information about cue values of unrecognized objects (which we argue is fairly common and thus of some interest). For 77.5% of the subjects, overall behavior was best explained by a compensatory parallel constraint satisfaction (PCS) strategy. The proportion of subjects using an enhanced RH heuristic (RHe) was negligible (up to 7.5%); 15% of the subjects seemed to use a take the best strategy (TTB). A more-fine grained analysis of the supplemental behavioral parameters conditional on strategy use supports PCS but calls into question process assumptions for apparent users of RH, RHe, and TTB within our experimental context. Our results are consistent with previous literature highlighting the importance of individual strategy classification as compared to aggregated analyses.

The recognition heuristic (RH; Goldstein & Gigerenzer, 2002) suggests that, when applicable, probabilistic inferences are based on a noncompensatory examination of whether an object is recognized or not. The overall findings on the processes that underlie this fast and frugal heuristic are somewhat mixed, and many studies have expressed the need for considering a more compensatory integration of recognition information. Regardless of the mechanism involved, it is clear that recognition has a strong influence on choices, and this finding might be explained by the fact that recognition cues arouse affect and thus receive more attention than cognitive cues. To test this assumption, we investigated whether recognition results in a direct affective signal by measuring physiological arousal (i.e., peripheral arterial tone) in the established city-size task. We found that recognition of cities does not directly result in increased physiological arousal. Moreover, the results show that physiological arousal increased with increasing inconsistency between recognition information and additional cue information. These findings support predictions derived by a compensatory Parallel Constraint Satisfaction model rather than predictions of noncompensatory models. Additional results concerning confidence ratings, response times, and choice proportions further demonstrated that recognition information and other cognitive cues are integrated in a compensatory manner.

One major challenge to behavioral decision research is to identify the cognitive processes underlying judgment and decision making. Glöckner (2009) has argued that, compared to previous methods, process models can be more efficiently tested by simultaneously analyzing choices, decision times, and confidence judgments. The Multiple-Measure Maximum Likelihood (MM-ML) strategy classification method was developed for this purpose and implemented as a ready-to-use routine in STATA, a commercial package for statistical data analysis. In the present article, we describe the implementation of MM-ML in R, a free package for data analysis under the GNU general public license, and we provide a practical guide to application. We also provide MM-ML as an easy-to-use R function. Thus, prior knowledge of R programming is not necessary for those interested in using MM-ML.

Impaired decision behavior has been repeatedly observed in schizophrenia patients. We investigated several cognitive mechanisms that might contribute to the jumping-to-conclusions bias (JTC) seen in schizophrenia patients: biases in information-gathering, information weighting and integration, and overconfidence, using the process tracing paradigm Mouselab. Mouselab allows for an in-depth exploration of various decision-making processes in a structured information environment. A total of 37 schizophrenia patients and 30 healthy controls participated in the experiment. Although showing less focused and systematic information search, schizophrenia patients practically considered all pieces of information and showed no JTC in the sense of collecting less pieces of evidence. Choices of patients and controls both approximated a rational solution quite well, but patients showed more extreme confidence ratings. Both groups mainly used weighted additive decision strategies for information integration and only a small proportion relied on simple heuristics. Under high stress induced by affective valence plus time pressure, however, schizophrenia patients switched to equal weighting strategies: less valid cues and more valid ones were weighted equally.

In recent years, numerous studies comparing intuition and deliberation have been published. However, relatively little is known about the cognitive processes underlying the two decision modes. In two studies, we analyzed the effects of decision mode instructions on processes of information search and integration, using eye-tracking technology in a between-participants (Study 1) and a within-participants (Study 2) design. Our findings indicate that the instruction to deliberate does not necessarily lead to qualitatively different information processing compared to the instruction to decide intuitively. We found no difference in mean fixation duration and the distribution of short, medium and long fixations. Short fixations in particular prevailed under both decision mode instructions, while long fixations indicating a conscious and calculation-based information processing were rarely observed. Instruction-induced deliberation led to a higher number of fixations, a more complete information search and more repeated information inspections. We interpret our findings as support for the hypothesis that intuitive and deliberate decision modes share the same basic processes which are supplemented by additional operations in the deliberate decision mode.

One of the core challenges of decision research is to identify individuals’ decision strategies without influencing decision behavior by the method used. Bröder and Schiffer (2003) suggested a method to classify decision strategies based on a maximum likelihood estimation, comparing the probability of individuals’ choices given the application of a certain strategy and a constant error rate. Although this method was shown to be unbiased and practically useful, it obviously does not allow differentiating between models that make the same predictions concerning choices but different predictions for the underlying process, which is often the case when comparing complex to simple models or when comparing intuitive and deliberate strategies. An extended method is suggested that additionally includes decision times and confidence judgments in a simultaneous Multiple-Measure Maximum Likelihood estimation. In simulations, it is shown that the method is unbiased and sensitive to differentiate between strategies if the effects on times and confidence are sufficiently large.

We claim that understanding human decisions requires that both automatic and deliberate processes be considered. First, we sketch the qualitative differences between two hypothetical processing systems, an automatic and a deliberate system. Second, we show the potential that connectionism offers for modeling processes of decision making and discuss some empirical evidence. Specifically, we posit that the integration of information and the application of a selection rule are governed by the automatic system. The deliberate system is assumed to be responsible for information search, inferences and the modification of the network that the automatic processes act on. Third, we critically evaluate the multiple-strategy approach to decision making. We introduce the basic assumption of an integrative approach stating that individuals apply an all-purpose rule for decisions but use different strategies for information search. Fourth, we develop a connectionist framework that explains the interaction between automatic and deliberate processes and is able to account for choices both at the option and at the strategy level.

From 1997 to 2009, 1,862 dermatology, gynaecology, and paediatrics (DGP) associated clinical yeast isolates were analysed for species occurrence, specimen origin and type, (multi-) resistance pattern, and testing period. The top seven of the isolated DGP-associated species remained the same as compared to total medical wards, with Candida albicans (45%) as most frequent pathogen. However, the DGP wards and DGP ICUs showed species-specific profiles; that is, the species distribution is clinic-specific similar and however differs in their percentage from ward to ward. By applying the “one fungus one name” principle, respectively, the appropriate current taxonomic species denominations, it has been shown that no trend to emerging species from 1998 to 2008 could be detected. In particular the frequently isolated non-Candida albicans species isolated in the DGP departments have already been detected in or before 1997. As yeasts are part of the cutaneous microbiota and play an important role as opportunistic pathogens for superficial infections, proper identification of the isolates according to the new nomenclature deems to be essential for specific and calculated antifungal therapy for yeast-like DGP-related infectious agents.