From abortion to vaccination, liberals and conservatives may not share the same views, yet they share a tendency to persist in their own beliefs despite knowing that millions of others disagree. In this paper, we investigate whether this shared tendency for persistence is supported by different psychological mechanisms across the two groups. An experiment with a large, nationally-representative sample in the U.S (N = 2,000) focusing on 12 controversies revealed similar rates of persistence across ideologies. However, the drivers of persistence differed across the two groups: Conservatives disproportionately relied on meta-epistemic explanations (i.e., appeals to subjectivity or unknowability) to maintain their controversial scientific views, whereas liberals tended to restrict such explanations to religious and moral controversies. Moreover, reliance on different explanations for persistence predicted differences in affective polarization.

People think they ought to make some decisions on the basis of deliberative analysis, and others on the basis of intuitive, gut feelings. What accounts for this variation in people’s preferences for intuition versus deliberation? We propose that intuition might be prescribed for some decisions because people’s folk theory of decision-making accords a special role to authenticity, where authenticity is uniquely associated with intuitive choice. Two pre-registered experiments find evidence in favor of this claim. In Experiment 1 (N=631), we find that decisions made on the basis of intuition (vs. deliberation) are more likely to be judged authentic, especially in domains where authenticity is plausibly valued. In Experiment 2 (N=177), we find that people are more likely to prescribe intuition as a basis for choice when the value of authenticity is heightened experimentally. These effects hold beyond previously recognized influences, such as computational costs, presumed efficacy, objectivity, complexity, and expertise.

People have a remarkable ability to remain steadfast in their beliefs in the face of large-scale disagreement. This has important consequences (e.g., societal polarization), yet its psychological underpinnings are poorly understood. In this paper, we answer foundational questions regarding belief persistence, from its prevalence to variability. Across two Experiments (N = 356, N = 354), we find that participants are aware of societal disagreement about controversial issues, yet overwhelmingly (~85%) do not question their views if asked to reflect on this disagreement. Both studies provide evidence that explanations for persistence vary across domains, with epistemic and meta-epistemic explanations among the most prevalent.

Decisions are often better when pursued after deliberation and careful thought. So why do we so often eschew deliberation, and instead rely on more intuitive, gut responses? We suggest that in addition to well-recognized factors (such as the costs of deliberation), people hold normative commitments concerning how decisions ought to be made. In some cases (e.g., when choosing a romantic partner), relying on deliberation (over intuition) could be seen as inauthentic or send a problematic social signal. In Experiment 1 (N = 654), we show that people in fact hold such domain-sensitive processing commitments, that they are distinct from reported descriptive tendencies, and that they contribute to predicting reported choice. In Experiment 2 (N = 555), we show that choosing intuitively vs. deliberately supports different inferences concerning confidence and authenticity, with the domain variation in inferences in Experiment 2 closely tracking the domain variation in normative commitments observed in Experiment 1. In Experiment 3 (N = 1002), we rule out an alternative explanation. These findings inform theories of judgment and decision-making, as well as efforts towards improving decision-making through critical thinking.

We are able to learn from others through a combination of trust and vigilance: we trust and believe people who are reliable and have our interests at heart; we ignore those who are incompetent or self-interested. While past work has studied how others' competence influences social learning, relatively little attention has been paid to how others' motivations influence such processes. To address this gap, we develop a Bayesian model of vigilance that considers the speaker's instrumental self-interest, and test predictions of this model through an experiment. In accordance with our model, participants become more vigilant when informants stand to benefit from influencing their actions. When perceived self-interest is maximal, testimony can be discounted wholesale, rendering middle ground increasingly difficult, if not impossible, to find. Our results have implications for research on polarization, misinformation, and disagreement.

Decades of research have examined the consequences of disagreement, both negative (harm to relationships) and positive (fostering learning opportunities). Yet the psychological mechanisms underlying disagreement judgments themselves are poorly understood. Much research assumes that disagreement tracks divergence: the difference between two individuals' beliefs with respect to a proposition. We test divergence as a theory of interpersonal disagreement through two experiments (N = 60, N = 60) and predictive models. Our data and modeling show that judgments of disagreement track divergence, but also the direction and extremity of beliefs. Critically, disagreement judgments track key social judgments (e.g., inferences of warmth, competence, and bias) above and beyond divergence, with notable variation across domains.

Being able to learn from small amounts of data is a key characteristic of human intelligence, but exactly how small? In this paper, we introduce a novel experimental paradigm that allows us to examine classification in an extremely data-scarce setting, asking whether humans can learn more categories than they have exemplars (i.e., can humans do "less-than-one shot'' learning?). An experiment conducted using this paradigm reveals that people are capable of learning in such settings, and provides several insights into underlying mechanisms. First, people can accurately infer and represent high-dimensional feature spaces from very little data. Second, having inferred the relevant spaces, people use a form of prototype-based categorization (as opposed to exemplar-based) to make categorical inferences. Finally, systematic, machine-learnable patterns in responses indicate that people may have efficient inductive biases for dealing with this class of data-scarce problems.