Many models of choice assume that people retrieve memories of past experiences and use them to guide evaluation and choice. In this paper, we examine whether samples of recalled past experiences do indeed underpin our evaluations of options. We showed participants sequences of numerical values and asked them to recall as many of those values as possible and also to state how much they would be willing to pay for another draw from the sequence. Using Bayesian mixed effects modeling, we predicted participants’ evaluation of the sequences from both the average of the values they recalled and the average of the values they saw. Contrary to the predictions of recall-based models, people’s evaluations appear to be sensitive to information beyond what was actually recalled. Moreover, we did not find consistent evidence that memory for specific items influenced evaluation of sequences. We discuss the implications for sampling models of memory and decision-making and alternative explanations.

Misinformed beliefs are difficult to change. Refutations that target false claims typically reduce false beliefs, but tend to be only partially effective. In this study, a social norming approach was explored to test whether provision of peer norms could provide an alternative or complementary approach to refutation. Three experiments investigated whether a descriptive norm—by itself or in combination with a refutation—could reduce the endorsement of worldview-congruent claims. Experiment 1 found that using a single point estimate to communicate a norm affected belief but had less impact than a refutation. Experiment 2 used a verbally-presented distribution of four values to communicate a norm, which was largely ineffective. Experiment 3 used a graphically-presented social norm with 25 values, which was found to be as effective at reducing claim belief as a refutation, with the combination of both interventions being most impactful. These results provide a proof of concept that normative information can aid in the debunking of false or equivocal claims, and suggests that theories of misinformation processing should take social factors into account.

People who strongly endorse conspiracy theories typically exhibit biases in domain-general reasoning. Previous studies, however, have mostly focused on less plausible conspiracy theories (e.g., the moon landing was faked), rather than more plausible ones (e.g., the Russian Federation orchestrated the attack on Sergei Skripal). Here we examine whether reasoning biases are associated with belief in all conspiracy theories or only with belief in less plausible conspiracy theories. In two pre-registered studies, we found that endorsement of implausible conspiracy theories, but not plausible ones, was associated with reduced information sampling in an information-foraging task and with less reflective reasoning. Thus, the relationship between belief in conspiracy theories and reasoning is not homogeneous, and reasoning is not linked specifically to the “conspiracy” in conspiracy theories, but to other factors (e.g., motivations towards sense making) that feature in many types of implausible belief (e.g., paranormal beliefs, delusions).

Political polarization on social media has been extensively studied. However, most research has examined polarization about topics that have preexisting associations with ideology, while few studies have tracked the onset of polarization about novel topics or the evolution of polarization over a prolonged period. The occurrence of COVID-19 provides a unique opportunity to study whether social media discourse about a novel event becomes increasingly polarized along ideological lines over time. This paper analyzes trends in Twitter polarization in relation to COVID-19 and other geopolitical events of 2020. The first two studies use topic analysis to examine the evolving difference over time in discussions of COVID-19 and other topics by liberals and conservatives on social media. COVID-19-related polarization is initially absent but gradually increases over time, in contrast to polarization related to other events. A third study examines structural polarization in retweet networks and finds that the frequency of counterpartisan retweets reduces over time. Across all three studies, we find evidence that Twitter discussion of COVID-19 has become more polarized over time.

How is people’s happiness determined by economic factors such as their income? Big data are essential to answering this question, but there is disagreement about the amount of evidence for causal relationships that different types of analysis give. This chapter reviews different approaches to analysis. First, it is argued that most existing literature both underclaims regarding the evidence for causality given by some types of analysis of big data, such as correlational analyses, and overclaims for other types of analysis, such as those involving panel data. Thus, even correlational data can be informative to the extent that associations are generally rare and that theoretical targets and alternatives are fully specified and given prior probabilities. Second, a new methodological problem is identified for a specific model of the income-rank relationship. According to the income rank hypothesis, people’s well-being is determined not by their income but by the ranked position that their income within a social comparison group. It is shown by simulation that spurious rank effects can occur in regression analyses if there is noise in measured income, but that this problem can be reduced with the use of robust regression techniques. A new analysis of a large dataset, the Panel Study of Income Dynamics, is reported. The results show that income rank effects are not reduced by the use of robust regression techniques, suggesting that previous support for the income rank hypothesis is not due to a noise-related artefact.