The cultural cognition thesis posits that individuals rely extensively on cultural meanings in forming perceptions of risk. The logic of the cultural cognition thesis suggests that a two-channel science communication strategy, combining information content (“Channel 1”) with cultural meanings (“Channel 2”), could promote open-minded assessment of information across diverse communities. We test this kind of communication strategy in a two-nation (United States, n = 1,500; England, n = 1,500) study, in which scientific information content on climate change was held constant while the cultural meaning of that information was experimentally manipulated. We found that cultural polarization over the validity of climate change science is offset by making citizens aware of the potential contribution of geoengineering as a supplement to restriction of CO2 emissions. We also tested the hypothesis, derived from a competing model of science communication, that exposure to information on geoengineering would lead citizens to discount climate change risks generally. Contrary to this hypothesis, we found that subjects exposed to information about geoengineering were slightly more concerned about climate change risks than those assigned to a control condition.

We conducted a two-nation study (United States, n = 1500; England, n = 1500) to test a novel theory of science communication. The cultural cognition thesis posits that individuals make extensive reliance on cultural meanings in forming perceptions of risk. The logic of the cultural cognition thesis suggests the potential value of a distinctive two-channel science communication strategy that combines information content ("Channel 1") with cultural meanings ("Channel 2") selected to promote openminded assessment of information across diverse communities. In the study, scientific information content on climate change was held constant while the cultural meaning of that information was experimentally manipulated. Consistent with the study hypotheses, we found that making citizens aware of the potential contribution of geoengineering as a supplement to restriction of CO2 emissions helps to offset cultural polarization over the validity of climate-change science. We also tested the hypothesis, derived from competing models of science communication, that exposure to information on geoengineering would provoke discounting of climate-change risks generally. Contrary to this hypothesis, we found that subjects exposed to information about geoengineering were slightly more concerned about climate change risks than those assigned to a control condition.

Our personal preferences affect a broad array of social behaviors. This includes the way we learn the preferences of others, an ability that often relies on limited or ambiguous information. Here we report an egocentric influence on this type of social learning that is reflected in both performance and response times. Using computational models that combine inter-trial learning and intra-trial choice, we find transient effects of participants’ preferences on the learning process, through the influence of priors, and persistent effects on the choice process. A second experiment shows that these effects generalize to non-social learning, though participants in the social learning experiment appeared to additionally benefit by using their knowledge about the popularity of certain preferences. We further find that the domain-general egocentric influences we identify can yield performance advantages in uncertain environments.

Confirmation bias—the tendency to overweight information that matches prior beliefs or choices—has been shown to manifest even in simple reinforcement learning. In line with recent work, we find that participants learned significantly more from choice-confirming outcomes in a reward-learning task. What is less clear is whether asymmetric learning rates somehow benefit the learner. Here, we combine data from human participants and artificial agents to examine how confirmation-biased learning might improve performance by counteracting decisional and environmental noise. We evaluate one potential mechanism for such noise reduction: visual attention—a demonstrated driver of both value-based choice and predictive learning. Surprisingly, visual attention showed the opposite pattern to confirmation bias, as participants were most likely to fixate on “missed opportunities”, slightly dampening the effects of the confirmation bias we observed. Several million simulated experiments with artificial agents showed this bias to be a reward-maximizing strategy compared to several alternatives, but only if disconfirming feedback is not completely ignored—a condition that visual attention may help to enforce.