Two studies examined the relationship between individual differences in cognitive reflection (CRT) and the tendency to accord genuinely moral (non-conventional) status to a range of counter-normative acts — that is, to treat such acts as wrong regardless of existing social opinion or norms. We contrasted social violations that are intrinsically harmful to others (e.g., fraud, thievery) with those that are not (e.g., wearing pajamas to work and engaging in consensual acts of sexual intimacy with an adult sibling). Our key hypothesis was that more reflective (higher CRT) individuals would tend to moralize selectively — treating only intrinsically harmful acts as genuinely morally wrong — whereas less reflective (lower CRT) individuals would moralize more indiscriminately. We found clear support for this hypothesis in a large and ideologically diverse sample of American adults. The predicted associations were not fully accounted for by the subjects’ political orientation, sensitivity to gut feelings, gender, age, educational attainment, or their placement on a sexual morals-specific measure of social conservatism. Our studies are the first to demonstrate that, in addition to modulating the intensity of moral condemnation, reflection may also play a key role in setting the boundaries of the moral domain as such.

The economic literature on negotiation shows that strategic concerns can be a barrier to agreement, even when the buyer values the good more than the seller. Yet behavioral research demonstrates that human interaction can overcome these strategic concerns through communication. We show that there is also a downside of this human interaction: cynicism. Across two studies we focus on a seller-buyer interaction in which the buyer has uncertain knowledge about the goods for sale, but has a positive expected payoff from saying “yes” to the available transaction. Study 1 shows that most buyers accept offers made by computers, but that acceptance rates drop significantly when offers are made by human sellers who communicate directly with buyers. Study 2 clarifies that this effect results from allowing human sellers to communicate with buyers, and shows that such communication focuses the buyers’ attention on the seller’s trustworthiness. The mere situation of negotiated interaction increases buyers’ attention to the sellers’ self-serving motives and, consequently, buyers’ cynicism. Unaware of this downside of interaction, sellers actually prefer to have the opportunity to communicate with buyers.

The current study tested the boundary conditions of ethical decision-making by increasing cognitive load. This manipulation is believed to hinder deliberation, and, as we argue, reduces the cognitive capacity needed for a self-serving bias to occur. As telling a lie is believed to be more cognitively taxing than telling the truth, we hypothesized that participants would be more honest under high cognitive load than low cognitive load. 173 participants anonymously rolled a die three times and reported their outcomes — of which one of the rolls would be paid out — while either under high or low cognitive load. For the roll that determined pay, participants under low cognitive load, but not under high cognitive load, reported die rolls that were significantly different from a uniform (honest) distribution. The reported outcome of this roll was also significantly higher in the low load condition than in the high load condition, suggesting that participants in the low load condition lied to get higher pay. This pattern was not observed for the second and third roll where participants knew the rolls were not going to be paid out and where therefore lying would not serve self-interest. Results thus indicate that having limited cognitive capacity will unveil a tendency to be honest in a situation where having more cognitive capacity would have enabled one to serve self-interest by lying.

When people predict their future behavior, they tend to place too much weight on their current intentions, which produces an optimistic bias for behaviors associated with currently strong intentions. More realistic self-predictions require greater sensitivity to situational barriers, such as obstacles or competing demands, that may interfere with the translation of current intentions into future behavior. We consider three reasons why people may not adjust sufficiently for such barriers. First, self-predictions may focus exclusively on current intentions, ignoring potential barriers altogether. We test this possibility, in three studies, with manipulations that draw greater attention to barriers. Second, barriers may be discounted in the self-prediction process. We test this possibility by comparing prospective and retrospective ratings of the impact of barriers on the target behavior. Neither possibility was supported in these tests, or in a further test examining whether an optimally weighted statistical model could improve on the accuracy of self-predictions by placing greater weight on anticipated situational barriers. Instead, the evidence supports a third possibility: Even when they acknowledge that situational factors can affect the likelihood of carrying out an intended behavior, people do not adequately moderate the weight placed on their current intentions when predicting their future behavior.

Four laboratory studies were conducted to test the hypothesis that correct Bayesian reasoning can be predicted by two factors of task complexity — the number of mental steps required to reach the normative solution, and the compatibility between the framing of data presented and the framing of the question posed. The findings show that participants performed better on frequency format questions only when one mental step was required to solve the task and when the data were in a compatible frequency format. By contrast, participants performed more poorly on more complicated tasks which required more mental steps (in a compatible frequency or probability format) or when the data and question formats were incompatible (Studies 1 and 2). Incompatibility between data and question formats was also associated with higher reaction times (Study 2b). Furthermore, on problems that incorporated incompatibility between the data sample size and the target (question) sample size, participants performed better on the probability question than the frequency question, regardless of data format (Study 3). The latter findings highlight the ecological advantage of translating data into probability terms, which are normalized in a range between 0 and 1, and thus can be transferred from one situation to another.

Decisions that consumers make often rest on evaluations of attributes, such as how large, expensive, good, or fattening an option seems. Extant research has demonstrated that these evaluations in turn depend upon the recently experienced distribution of attribute values (e.g., positively or negatively skewed). In many situations decisions rely on recalling the attribute values of each option, a process that has been neglected in much of the previous literature. In two experiments, participants learned attribute information for labeled stimuli presented within either a positively or negatively skewed distribution and then they recalled values from labels after approximately one minute. The results demonstrated effects that are inconsistent with predictions of the category-adjustment model (Duffy, Huttenlocher, Hedges & Crawford, 2010) that recalled values would shift toward the mean of the distribution of values presented. Instead, results were consistent with predictions of the comparison-induced distortion model (Choplin & Hummel, 2002) that remembered values would depend on the density of stimuli within the attribute range. Reasons for these results, alternative models, and implications for decision-making are discussed.

We demonstrate the usefulness of cognitive models for combining human estimates of probabilities in two experiments. The first experiment involves people’s estimates of probabilities for general knowledge questions such as “What percentage of the world’s population speaks English as a first language?” The second experiment involves people’s estimates of probabilities in football (soccer) games, such as “What is the probability a team leading 1–0 at half time will win the game?”, with ground truths based on analysis of large corpus of games played in the past decade. In both experiments, we collect people’s probability estimates, and develop a cognitive model of the estimation process, including assumptions about the calibration of probabilities and individual differences. We show that the cognitive model approach outperforms standard statistical aggregation methods like the mean and the median for both experiments and, unlike most previous related work, is able to make good predictions in a fully unsupervised setting. We also show that the parameters inferred as part of the cognitive modeling, involving calibration and expertise, provide useful measures of the cognitive characteristics of individuals. We argue that the cognitive approach has the advantage of aggregating over latent human knowledge rather than observed estimates, and emphasize that it can be applied in predictive settings where answers are not yet available.

It is important to understand the impact of individual differences in decision making from childhood to adulthood. This cohort-based study extends our knowledge by comparing decision making of children across the age range of 8 to 17 years and their parents. Based on prior research and theory focusing on different types of framing effects, we uncover several key differences across ages, including levels of risk taking and sensitivity to expected value differences between risky and riskless choices. Furthermore, we find that measures such as Numeracy and Surgency help explain both age-related and individual differences on our tasks, especially for decisions involving risk. We discuss the role of diverse task measures in understanding how individual difference factors affect different aspects of decision making, including the ability and effort to process numerical information and the ability to suppress affective reactions to stimulus labels.

Affective states can change how people react to measures aimed at influencing their decisions such as providing a default option. Previous research has shown that when defaults maintain the status quo positive mood increases reliance on the default and negative mood decreases it. Similarly, it has been demonstrated that positive mood enhances the preference for inaction. We extend this research by investigating how mood states influence reliance on the default if the default leads to a change, thus pitting preference for status quo against a preference for inaction. Specifically, we tested in an online study how happiness and sadness influenced reliance on two types of default (1) a default maintaining status quo and (2) a default inducing change. Our results suggest that the effect of emotions depends on the type of default: people in a happy mood were more likely than sad people to follow a default when it maintained status quo but less likely to follow a default when it introduced change. These results are in line with mood maintenance theory.