According to decades of research, whether negotiations succeed depends on how much of the stake each person will get. Yet, real-world stakes often consist of resources that vary on quality, not just quantity. While it may appear obvious that people should reject qualitatively inferior offers, just as they reject quantitatively unequal offers, it is less clear why. Across three incentive-compatible studies (N = 1,303) using the ultimatum game, we evaluate three possible reasons for why people reject qualitatively unequal negotiation offers (that are 50% of the stake): fairness, mere inequality, or badness. Data across the three studies are consistent with the fairness account. Casting doubt on the possibility that people reject qualitatively unequal offers merely because they are ‘bad’, Studies 1 and 2 found that participants were more likely to reject the same coins when these were inferior (e.g., 200 × 5¢ coins) to the negotiation partner’s coins (e.g., 5 × $2 coins) than when both parties received the same undesirable coins (e.g., both received 200 × 5¢ coins). Supporting a fairness explanation, rejection rates of the qualitatively inferior offer were higher when the proposal came from a human (vs. a computer), suggesting that rejection stemmed in part from a desire to punish the negotiation partner for unfair treatment (Study 3). Nevertheless, some participants still rejected the unequal offer from a computer, suggesting that mere inequality matters as well. In sum, the findings highlight that quality, not just quantity, is important for attaining fair negotiation outcomes.

Researchers have found evidence of both hot hand and gambler’s fallacy biases in lottery number selection. Which of the two opposite effects is observed is often dependent upon the nature of the lottery game, the particular sample, the local culture of the participants, or the time transpired since the seed event. By observing hundreds of millions of lottery entries over 118 consecutive semiweekly drawings, we present evidence of both effects and their longitudinal properties. With respect to the selection of individual numbers, lottery participants tend to avoid recently selected winning numbers. This gambler’s fallacy effect diminishes and the number becomes increasingly ‘hot’ until it is selected again. With respect to winning number combinations, we found strong evidence of a small but persistent hot hand bias. This bias gradually diminishes over time, but remains detectable and highly consistent for a number of years.

People often overestimate their understanding of how things work. For instance, people believe that they can explain even ordinary phenomena such as the operation of zippers and speedometers in greater depth than they really can. This is called the illusion of explanatory depth. Fortunately, a person can expose the illusion by attempting to generate a causal explanation for how the phenomenon operates (e.g., how a zipper works). This might be because explanation makes salient the gaps in a person’s knowledge of that phenomenon. However, recent evidence suggests that people might be able to expose the illusion by instead explaining a different phenomenon. Across three preregistered experiments, we tested whether the process of explaining one phenomenon (e.g., how a zipper works) would lead someone to report knowing less about a completely different phenomenon (e.g., how snow forms). In each experiment, we found that attempting to explain one phenomenon led participants to report knowing less about various phenomena. For example, participants reported knowing less about how snow forms after attempting to explain how a zipper works. We discuss alternative accounts of the illusion of explanatory depth that might better fit our results. We also consider the utility of explanation as an indirect, non-confrontational debiasing method in which a person generalizes a feeling of ignorance about one phenomenon to their knowledge base more generally.

Although the individual has been the focus of most research into judgment and decision-making (JDM), important decisions in the real world are often made collectively rather than individually, a tendency that has increased in recent times with the opportunities for easy information exchange through the Internet. From this perspective, JDM research that factors in this social context has increased generalizability and mundane realism relative to that which ignores it. We delineate a problem-space for research within which we locate protocols that are used to study or support collective JDM, identify a common research question posed by all of these protocols—‘What are the factors leading to opinion change for the better (‘virtuous opinion change’) in individual JDM agents?’—and propose a modeling approach and research paradigm using structured groups (i.e., groups with some constraints on their interaction), for answering this question. This paradigm, based on that used in studies of judge-adviser systems, avoids the need for real interacting groups and their attendant logistical problems, lack of power, and poor experimental control. We report an experiment using our paradigm on the effects of group size and opinion diversity on judgmental forecasting performance to illustrate our approach. The study found a U-shaped effect of group size on the probability of opinion change, but no effect on the amount of virtuous opinion change. Implications of our approach for development of more externally valid empirical studies and theories of JDM, and for the design of structured-group techniques to support collective JDM, are discussed.

Continuous risky decisions refer to decisions that involve trade-offs among options with persistent risks. People can use the probability of occurrence per unit time (e.g., ‘the probability of occurrence is 1% per month’) or the average time of risk occurrence (e.g., ‘the average occurrence time is 100 months’) to represent continuous risky options. In this study, we examined the effect of the presentation format (i.e., the probability of occurrence per unit time vs. the average time of risk occurrence) on continuous risky decisions in the gain domain and further explored the underlying mechanism. In Study 1 (N = 122), we demonstrated the effect of presentation format on continuous risky decisions and the moderating effect of the magnitude of probabilities. Specifically, when the probabilities were relatively low, compared with the probability of occurrence per unit time, using the average time of risk occurrence to present the continuous risky options led to more risk-averse decisions. However, when the probabilities were relatively high, compared with the probability of occurrence per unit time, the presentation format of the average time occurrence led to more risk-seeking decisions. In Study 2 (N = 136), we found that the moderating effect of the option probabilities on continuous risky decisions was mediated by the subjective attribute-wise difference judgment. In Study 3 (N = 221), we replicated the effect of presentation format on continuous risky decisions in more natural scenarios. The study offered a deep understanding of the mechanism of continuous risky decision-making, and the results were conducive to further developing theories in relevant fields.

This paper introduces a new software interface to elicit belief distributions of any shape: Click-and-Drag. The interface was tested against the state of the art in the experimental literature—a text-based interface and multiple sliders—and in the online forecasting industry—a distribution-manipulation interface similar to the one used by the most popular crowd-forecasting website. By means of a pre-registered experiment on Amazon Mechanical Turk, quantitative data on the accuracy of reported beliefs in a series of induced-value scenarios varying by granularity, shape, and time constraints, as well as subjective data on user experience were collected. Click-and-Drag outperformed all other interfaces by accuracy and speed, and was self-reported as being more intuitive and less frustrating, confirming the pre-registered hypothesis. Aside of the pre-registered results, Click-and-Drag generated the least drop-out rate from the task, and scored best in a sentiment analysis of an open-ended general question. Further, the interface was used to collect homegrown predictions on temperature in New York City in 2022 and 2042. Click-and-Drag elicited distributions were smoother with less idiosyncratic spikes. Free and open source, ready to use oTree, Qualtrics and Limesurvey plugins for Click-and-Drag, and all other tested interfaces are available at https://beliefelicitation.github.io/.

This article revisits and further investigates the extent to which scope insensitivity in helping contexts can be reduced by the unit asking (UA) method. UA is an intervention that first asks people to help one unit and then asks for willingness to help multiple units. In 3 studies (N = 3,442), participants took on the role of policymakers to allocate help (motivation to help and willingness to pay) to local aid projects. They underwent either UA or a control condition (in which they stated their willingness to help only to the multiple units). Against expectations, the first 2 studies found a reversed UA effect for helping motivation, such that help decreased when participants were in the UA condition. However, the third study found a UA effect for helping motivation when participants made the sequential assessments within one project (when the individual unit belonged to the multiple units-group), rather than between projects (when the individual unit belonged to another group). Thus, our results suggest that the 2 assessments critical for the UA method should be done within the same project rather than between 2 projects to successfully reduce scope insensitivity. Further, the age of the unit (child or adult), the number of the unit(s), the composition of the group (homogeneous or heterogeneous), and the size of the group did not substantially reduce scope insensitivity with UA.

Political divisions have become a central feature of modern life. Here, we ask whether these divisions affect advice-taking from co- and counter-partisans in a nonpolitical context. In an incentivized task assessing the accuracy of nonpolitical news headlines, we find partisan bias in advice-taking: Democratic participants are less swayed by (accurate) information that comes from Republicans compared to the same information from Democrats (Republican participants display no such bias). We then adjudicate between two possible mechanisms for this biased advice-taking: a preference-based account, where participants are motivated to take less advice from counter-partisans because doing so is unpleasant; versus a belief-based account, where participants sincerely believe co-partisans are more competent at the task (even though this belief is incorrect). To do so, we examine the impact of a substantial increase in the stakes, which should increase accuracy motivations (and thereby reduce the relative impact of partisan motivations). We find that increasing the stakes does not reduce biased advice-taking, hence no evidence to support the bias is driven by preference. Consistent with the belief-based account, we find that Democratic participants (incorrectly) believe their co-partisans are better at the task, and this incorrect belief is much less severe among Republican participants. Further supporting the notion that the stated beliefs are sincere, raising the stakes of the belief elicitation of relative partisan competence does not affect the stated beliefs. Finally, participants—instead of ignoring the feedback—actually substantially update in favor of their counter-partisans given feedback that suggests counter-partisans are competent.

People often test changes to see if the change is producing the desired result (e.g., does taking an antidepressant improve my mood, or does keeping to a consistent schedule reduce a child’s tantrums?). Despite the prevalence of such decisions in everyday life, it is unknown how well people can assess whether the change has influenced the result. According to interrupted time series analysis (ITSA), doing so involves assessing whether there has been a change to the mean (‘level’) or slope of the outcome, after versus before the change. Making this assessment could be hard for multiple reasons. First, people may have difficulty understanding the need to control the slope prior to the change. Additionally, one may need to remember events that occurred prior to the change, which may be a long time ago. In Experiments 1 and 2, we tested how well people can judge causality in 9 ITSA situations across 4 presentation formats in which participants were presented with the data simultaneously or in quick succession. We also explored individual differences. In Experiment 3, we tested how well people can judge causality when the events were spaced out once per day, mimicking a more realistic timeframe of how people make changes in their lives. We found that participants were able to learn accurate causal relations when there is a zero pre-intervention slope in the time series but had difficulty controlling for nonzero pre-intervention slopes. We discuss these results in terms of 2 heuristics that people might use.

This article reports a series of studies of judgments of satisfaction with salary, manipulating the distribution of salaries of others doing the same work. The experiments were designed to compare 6 theories of contextual effects in judgment, including adaptation-level theory, correlation–regression theory, inferred distribution (ID) theory, decision by sampling (DbS), ensemble (EN) theory, and range–frequency (RF) theory. Manipulations of the frequency distribution using cubic density functions produce a double crossover of curves relating judgments to salaries; this double crossover violates implications of 4 of the theories but remains consistent with DbS and RF theories. ID theory assumes that rank is inferred from the mean and endpoints, so it fails to describe the double crossover. Manipulations of the endpoints produce changes in the heights and slopes of the curves, which are not explained by DbS and are partially inconsistent with EN theory. EN theory implies no effect of the rank of a salary and assumes that endpoints only affect judgments of salaries on the same side of the mean, contrary to the results. RF theory implies that ratings of stimuli holding the same ranks in 2 contexts with differing endpoints should be linearly related, and the data appeared consistent with this implication. RF theory is the only theory that gives a consistent account of all of the results. RF theory can be extended in order to estimate the effective context, which appears to differ systematically between people according to their full-time incomes.

Influential ‘fast-and-slow’ dual process models suggest that sound reasoning requires the correction of fast, intuitive thought processes by slower, controlled deliberation. Recent findings with high-level reasoning tasks started to question this characterization. Here we tested the generalizability of these findings to low-level cognitive control tasks. More specifically, we examined whether people who responded accurately to the classic Stroop and Flanker tasks could also do so when their deliberate control was minimized. A two-response paradigm, in which people were required to give an initial ‘fast’ response under time–pressure and cognitive load, allowed us to identify the presumed intuitive answer that preceded the final ‘slow’ response given after deliberation. Across our studies, we consistently find that correct final Stroop and Flanker responses are often non-corrective in nature. Good performance in cognitive control tasks seems to be driven by accurate ‘fast’ intuitive processing, rather than by ‘slow’ controlled correction of these intuitions. We also explore the association between Stroop and reasoning performance and discuss implications for the dual process view of human cognition.

What are life’s biggest decisions? In Study 1, I devised a taxonomy comprising 9 decision categories, 58 decision types, and 10 core elements of big decisions. In Study 2, I revealed people’s perceptions of and expectations for the average person’s big life decisions. In the flagship Study 3, 658 participants described their 10 biggest past and future decisions and rated each decision on a variety of decision elements. This research reveals the characteristics of a big life decision, which are the most common, most important, and most positively evaluated big life decisions, when such decisions happen, and which factors predict ‘good’ decisions. This research contributes to knowledge that could help people improve their lives through better decision-making and living with fewer regrets.

Much like other social and nonsocial evaluations, estimates of numerical quantities are susceptible to comparative influences. However, numerical representations can take either a nonsymbolic (e.g., a grouping of dots) or a symbolic numerical form (e.g., Hindu–Arabic numerals), which each produce comparative biases in opposite directions. The current work takes a fine-grained curve fitting approach across a wide range of values to the investigation of their potential nonlinear influence in the context of a numerical estimation task. A series of 3 experiments (N = 1,613) showed how nonsymbolic standards produce linear contrastive patterns (Study 1), whereas symbolic numerical anchors show a cubic assimilative effect with a leveling off in strength for more extreme standards (Study 2). Sequential contrast from the previous patterns and assimilation to the previous response were found to be present and additive in the presence of both representations but were larger in absence of the symbolic numerical anchors (Study 3).

Past research has shown that perceived scientific consensus (or lack thereof) on an issue predicts belief in misinformation. In the current study (N = 729), we investigated how perceived consensus among both experts and laypeople predicts beliefs in localized and specific conspiracy theories in Turkey, a non-WEIRD country. Participants in our study were found to overestimate consensus among both experts and laypeople regarding baseless conspiracy theories surrounding the alleged secret articles of the Lausanne Treaty and unused mining reserves in Turkey. Notably, conspiracy believers exhibited a higher tendency to overestimate consensus compared to non-believers. Furthermore, perceived expert consensus had a stronger association with conspiracy beliefs than perceived laypeople consensus. We also explored the correlates of conspiracy beliefs and perceived consensus, including socioeconomic factors, worldview, cognitive sophistication, and personality. The results further indicate that the correlations between belief and perceived consensus manifest with comparable magnitudes, irrespective of the specific conspiracy theories under consideration. These findings support the potential of perceived consensus as an important factor for understanding conspiracy beliefs.

How was Chinese people’s confidence in the political system linked to their perception of Covid infection risk, perception of health threat of Covid infection, lockdown experience, and attitudes toward the dynamic Zero-Covid policy? Using 2 waves of online surveys conducted in 2020 and 2022, we investigated how these factors were related. Individuals who were more supportive of the Zero-Covid policy were more confident. Those who were less supportive of the Zero-Covid policy were from areas with more severe Covid infections and experienced a longer lockdown as well; these individuals also perceived higher Covid infection risk and health threat. As such, their confidence in the political system was also more likely to drop from 2020 to 2022. In sum, these findings suggest that Chinese people’s confidence in the political system was linked to their Covid infection risk perception, perceived Covid threat to health, lockdown experience, and attitudes toward the Zero-Covid policy. These findings were corroborated with the severity of Covid infections in the province and individual’s political beliefs and orientation.

A child’s world is full of cues that may help to learn about decision options by providing valuable predictions. However, not all cues are always equally valid. To enhance decision-making, one should use cue validities as weights in decision-making. Prior research showed children’s difficulty in doing so. In 2 conceptual replication studies, we investigated preschoolers’ competencies when they encounter a cue whose prediction is always correct. We assessed 5- to 6-year-olds’ cue evaluations and decision-making in an information-board-game. Participants faced 3 cues when repeatedly choosing between 2 locations to find treasures: A nonprobabilistic, high-validity cue that always provided correct predictions (p = 1) paired with 2 probabilistically correct (Study 1: p = .34, p = .17) or 2 nonprobabilistic, incorrect cues (Study 2: p = 0). Participants considered cue validities—albeit in a rudimentary form. In their cue evaluations, they preferred the high-validity cue, indicating their ability to understand and use cue validity for evaluations. However, in their decision-making, they did not prioritize the high-validity cue. Rather, they frequently searched and followed the predictions of less valid (Study 1) and incorrect cues (Study 2). Our studies strengthen the current state of decision research suggesting that the systematic use of cue validities in decision-making develops throughout childhood. Apparently, having appropriate cue evaluations that reflect cue validities is not sufficient for their use in decision-making. We discuss our findings while considering the importance of learning instances for the development of decision competencies.

While working memory capacity is associated with superior performance on a number of tasks, could it paradoxically sometimes be associated with suboptimal performance? Corbin et al. (2010, Judgment and Decision Making 5(2), 110–115) found that, in a between-subjects design, higher WMC is associated with a larger risky-choice framing effect, traditionally conceived of as a departure from rational principles. Such surprising findings are of potentially great theoretical importance; yet the original study was underpowered. In this registered report, we aimed to replicate and extend the original findings, by conducting an online experiment among 425 North Americans. To extend the findings beyond the specific single tasks used in the original study, we used three WMC tasks with different processing components and six framing problems involving human lives. In a close replication, the frame significantly interacted with neither the Ospan short absolute score nor the Ospan short partial score in predicting ratings on the disease-framing problem. Similarly, in an extended replication, a composite WMC score did not significantly interact with the frame in predicting ratings on three framing problems involving human lives. The Bayes factors showed that the data were 3 to 10 times more likely under the null hypothesis of no interaction between WMC and frame. Taken together, these findings show an absence of association between the between-subjects risky-choice framing effect and WMC. This outcome is compatible with four out of the six theoretical accounts we considered, and is uniquely predicted by the default-interventionist dual-process account and the pragmatic inference account. Further research can more rigorously pit conflicting predictions of these accounts against each other.

Combining experts’ subjective probability estimates is a fundamental task with broad applicability in domains ranging from finance to public health. However, it is still an open question how to combine such estimates optimally. Since the beta distribution is a common choice for modeling uncertainty about probabilities, here we propose a family of normative Bayesian models for aggregating probability estimates based on beta distributions. We systematically derive and compare different variants, including hierarchical and non-hierarchical as well as asymmetric and symmetric beta fusion models. Using these models, we show how the beta calibration function naturally arises in this normative framework and how it is related to the widely used Linear-in-Log-Odds calibration function. For evaluation, we provide the new Knowledge Test Confidence data set consisting of subjective probability estimates of 85 forecasters on 180 queries. On this and another data set, we show that the hierarchical symmetric beta fusion model performs best of all beta fusion models and outperforms related Bayesian fusion models in terms of mean absolute error.