Daniel Brady

Understanding our ideological opponents is crucial for the effective exchange of arguments and the avoidance of escalation, and the reduction of conflict. We operationalize the idea of an “Ideological Turing Test” to measure the accuracy with which people represent the arguments of their ideological opponents. Crucially, this offers a behavioral measure of open-mindedness which goes beyond mere self-report. We recruited 200 participants from opposite sides of three topics with potential for polarization in the UK of the early 2020s (1200 participants total). Participants were asked to provide reasons both for and against their position. Their reasons were then rated by participants from the opposite side. Our criteria for “passing” the test was if an argument was agreed with by opponents to the same extent or higher than arguments made by proponents. We found evidence for high levels of mutual understanding across all three topics. We also found that those who passed were more open-minded toward their opponents, in that they were less likely to rate them as ignorant, immoral, or irrational. Our method provides a behavioral measure of open-mindedness and ability to mimic counterpartisan perspectives that goes beyond self-report measures. Our results offer encouragement that, even in highly polarized debates, high levels of mutual understanding persist.

Advances in artificial intelligence (AI) raise important questions about whether people view moral evaluations by AI systems similarly to human-generated moral evaluations. We conducted a modified Moral Turing Test (m-MTT), inspired by Allen et al. (Exp Theor Artif Intell 352:24–28, 2004) proposal, by asking people to distinguish real human moral evaluations from those made by a popular advanced AI language model: GPT-4. A representative sample of 299 U.S. adults first rated the quality of moral evaluations when blinded to their source. Remarkably, they rated the AI’s moral reasoning as superior in quality to humans’ along almost all dimensions, including virtuousness, intelligence, and trustworthiness, consistent with passing what Allen and colleagues call the comparative MTT. Next, when tasked with identifying the source of each evaluation (human or computer), people performed significantly above chance levels. Although the AI did not pass this test, this was not because of its inferior moral reasoning but, potentially, its perceived superiority, among other possible explanations. The emergence of language models capable of producing moral responses perceived as superior in quality to humans’ raises concerns that people may uncritically accept potentially harmful moral guidance from AI. This possibility highlights the need for safeguards around generative language models in matters of morality.

Learning to drive is a significant event for the transition to adulthood and delay or avoidance may have social, practical, and psychological implications. For those with Developmental Coordination Disorder, driving presents a considerable challenge, and the literature shows that there are differences in driving ability between individuals with and without DCD. The aim of the current research is to further our understanding of the mechanisms underlying the driving experiences of individuals with DCD. Nineteen participants with DCD and 36 controls aged 18–57 years took part in this study. Participants completed standardized tests, questionnaires and a driving simulation task designed to measure speed, road positioning, and rate of change of steering in three conditions with increasing perceptual complexity. Results indicate that behaviors for all participants changed as the perceptual demands of the task increased. However, drivers with DCD were more affected than all other groups, driving more slowly, and driving further to the right. These findings illustrate how the impact of both internal and external constraints negatively affect the success of the driving task for individuals with DCD compared to their TD peers.