In three experiments using 977 participants, we investigated whether people would show belief bias by letting their prior beliefs on politically charged topics unduly influence their reasoning when updating beliefs based on evidence. Participants saw data from fictional studies and made judgments of how strongly COVID-19 mitigation measures influenced the number of COVID-19 cases (political problems) or a medicine influenced number of headaches (neutral problems). Based on rational Bayesian models using strong versus weak priors to represent biased beliefs about causal strength, we predicted that people who strongly supported the use of mitigation measures (mainly liberals) would overestimate causal strength on political problems relative to neutral problems while those who strongly opposed mitigation measures (mainly conservatives) would underestimate strength on political problems. Results suggested that belief bias is driven more by specific beliefs relevant to the reasoning context than by general attitudinal factors like political ideology. In Experiments 1 and 2, liberals and conservatives who strongly supported mitigation measures overestimated strength on political problems. In Experiment 3, conservatives who strongly opposed the use of mitigation measures underestimated causal strength on political problems and conservatives who supported mitigation measures made higher strength judgments on political problems than those who opposed these measures. Public Significance StatementIn these studies, people made biased judgments about the strength or effectiveness of COVID-19 mitigation measures (including wearing masks and social distancing), but these biases were based more on their prior beliefs about these measures than on their political ideology. Study participants who felt that mitigation measures are very useful-mainly liberals but also some conservatives-overestimated the strength of mitigation measures when interpreting the results of scientific studies, while participants who felt that mitigation measures are not useful at all-some conservatives-underestimated the strength of mitigation measures. These findings suggest that giving the public accurate and trustworthy information about how effective specific public health measures are (including vaccines) will help them make better health decisions.

In three experiments based on 977 participants, we investigated whether people would show belief bias by letting their prior beliefs on politically charged topics unduly influence their reasoning when updating beliefs based on evidence. Participants saw data from fictional studies and made judgments of how strongly COVID-19 mitigation measures influenced the number of COVID-19 cases (political problems) or a medicine influenced number of headaches (neutral problems). We predicted that liberals would overestimate and conservatives would underestimate causal strength on political problems relative to neutral problems. In Experiments 1 and 2, liberals showed this overestimation bias. Surprisingly, college-student conservatives in Experiment 2 showed the same overestimation as liberals. These findings made sense because all three groups who overestimated the strength of mitigation measures held prior beliefs that strongly favored use of these measures. In Experiment 3, conservatives’ judgments of the strength of mitigation measures after seeing evidence increased as their degree of prior support for these measures increased. Furthermore, conservatives who strongly opposed the use of mitigation measures underestimated causal strength in the political problems. These results suggest that belief bias is driven more by specific beliefs relevant to the reasoning context than to general attitudinal factors like political ideology.

Safe driving requires wisely allocating focal attention among multiple changing events and comprehending events that are attended to. Research suggests that attentional skills can be improved by training. In this experiment, we are using a low-fidelity driving simulator to train participants using part-task training on two attentional subskills: identifying (comprehending) and tracking potential hazards; and detecting and avoiding imminent hazards. Following initial familiarization with the driving simulator, each participant will receive training in one of these two attentional subskills. Scene comprehension probes train (and measure) identifying and tracking potential hazards by having participants watch a moving driving scenario and then select the vehicle that behaved hazardously during the scene. In hazard avoidance probes, participants must make driving responses to avoid imminent hazards without hitting nearby vehicles. After the training phase, there is a test phase measuring near transfer, to hazards similar to training, and far transfer, to untrained hazards. We hypothesize that the participants who receive part-task training on identifying and tracking hazards should perform better at scene comprehension probes than the hazard-avoidance training group in both near and far transfer conditions. We also hypothesize that the group trained on avoiding imminent hazards will perform better on hazard avoidance probes in both near and far transfer conditions.

In three experiments based on 977 participants, we investigated whether people would show belief bias by letting their prior beliefs on politically charged topics unduly influence their reasoning when updating beliefs based on evidence. Participants saw data from fictional studies and made judgments of how strongly COVID-19 mitigation measures influenced the number of COVID-19 cases (political problems) or a medicine influenced number of headaches (neutral problems). We predicted that liberals would overestimate and conservatives would underestimate causal strength on political problems relative to neutral problems. In Experiments 1 and 2, liberals showed this overestimation bias. Surprisingly, college-student conservatives in Experiment 2 showed the same overestimation as liberals. These findings made sense because all three groups who overestimated the strength of mitigation measures held prior beliefs that strongly favored use of these measures. In Experiment 3, conservatives' judgments of the strength of mitigation measures after seeing evidence increased as their degree of prior support for these measures increased. Furthermore, conservatives who strongly opposed the use of mitigation measures underestimated causal strength in the political problems. These results suggest that belief bias is driven more by specific beliefs relevant to the reasoning context than to general attitudinal factors like political ideology.

Recycling is a widely adopted practice that can reduce waste in landfills and increase profits for companies, corporations, and universities, such as Clemson, that sell recyclable materials. For these materials to be sold at a competitive price, however, they must be uncontaminated. This is a considerable barrier for Clemson University because users’ knowledge and experience with recycling do not align with Clemson University’s expected recycling practices. In an effort to bridge this gap, we followed human-centered design practices to generate and test prototypes of new system signage. We found that users were more accurate and confident in their sorting behavior when interacting with the new signage. Users also perceived the new signage as more usable than the old signage. In addition to these findings, the results of this research include a user testing methodology and experimental protocol that can be implemented in other contexts where large populations sort waste.