People often prefer simpler explanations (that posit the presence of fewer causes), judging these more probable than more complex alternatives. However, simplicity preferences are only mathematically justified under certain conditions. We examine one case where, mathematically, complexity preferences are justified, and test whether this corresponds to a boundary condition on simplicity preferences. Specifically, we focus on cases where causes occur frequently (rather than rarely), and where explanations specify the absence of additional causes (e.g., “Cause A and not B or C”) rather than remaining agnostic about their presence or absence (e.g., “Cause A”). Study 1 showed that, in these cases, simplicity preferences were attenuated, but not reversed. Study 2 suggests that simplicity preferences partly stemmed from failures to explicitly represent absent causes. We suggest that biases towards oversimplification may arise due to over-application of a cognitively simple version of Ockham’s razor, that is insensitive to the probability of absent causes.

People often evaluate explanations by considering various ‘explanatory virtues’, such as an explanation’s simplicity (i.e. the number of unexplained causes referred to). Simplicity has been thought to guide these evaluations by providing a cue to the inputs of Bayesian inference (priors and likelihoods), thus indirectly helping compute the outputs: the posterior probability of an explanation being true. Yet simplicity may also play other, more direct, roles in explanation evaluations. While study 1 supported the mediating role of priors and likelihoods in people’s simplicity preferences, study 1 and 2 found that participants still preferred simpler explanations after statistically controlling for priors and likelihoods (either elicited or provided). These results suggest that simplicity guides explanation evaluations not just by providing cues to the inputs of Bayesian inference, but also by serving as a direct cue to the outputs of these inferences – perhaps providing a simplifying heuristic for these evaluations.

When making inferences about another person (the target), perceivers often have to integrate multiple sources of informa-tion. This can include stereotypes about the targets groups (e.g., age, race, occupation) as well as other information aboutthe target (individuating information). In simple situations, perceivers approximate ideal Bayesian information integra-tion, relying more heavily on information that is more informative for the judgement. However under cognitive load withcognitive resources taken up by other demands people may instead rely on simplifying heuristics. We investigate severalpossible heuristics that people may use under load, including relying primarily on stereotypes rather than individuatinginformation, as suggested by previous research, and we test if and how these heuristics depend on how informative eachsource of information is. By clarifying how stereotypes are used in less-than-ideal cognitive conditions, this work hasimplications for when stereotypes will tend to be overused in real-world situations.

People often learn about categories, particularly social categories, based on biased information. Unless people are able to correct for this, they may develop biased beliefs and inferences about these categories. The current research examines if potentially biased information about social groups makes groups appear more homogeneous, and makes people more confident in their inferences about group members. Two sources of biases are considered: due to lacking first-hand experience with a group, or due to having second-hand information from the media or other people. Both sources made groups appear more homogeneous, suggesting that information biases were present and not corrected for. However, only second-hand knowledge led to greater confidence about group members, because, when people lacked first-hand knowledge, their uncertainty about the group average counteracted this effect. This highlights the importance of understanding biases present in people’s information, and corrective processes that may allow people to continue to make unbiased inferences.