The Dual Effect of COVID-19 on Intergroup Conflict in the Korean Peninsula.

The coronavirus pandemic has fundamentally shifted the way human beings interact, both as individuals and groups, in the face of such a widespread outbreak. This paper seeks to investigate the effects of COVID-19 on intergroup emotions and attitudes within an intractable intergroup conflict, specifically, through the lens of the Korean conflict. Using a two-wave, cross-sectional design, this study was able to track the profound psychological changes in intergroup emotions and attitudes both prior to the pandemic and during its onslaught. Results of these two wave representative samples show that South Korean citizens demonstrated higher levels of fear of their neighbors in North Korea after the outbreak of COVID-19 than before. In turn, this led to increased societal support of hostile government policies towards North Koreans. Conversely, the same participants exhibited higher levels of empathy towards North Koreans during the pandemic, which led to a higher willingness to collaborate with their outgroup. This dual effect on intergroup emotions within intractable conflicts brings forth new avenues from which societies may be able to restrain the destructive influence of the COVID-19 threat on intergroup relations - as well as harvesting its constructive potential for reconciling warring intergroup relations.

Causal explanation in the face of contradiction.

Previous work has shown that predictions can be mediated by mechanistic beliefs. The present study shows that such mediation only occurs in the face of contradictory, and not corroborative, evidence. In four experiments, we presented participants with causal statements describing a common-cause structure (E1 ← C → E2). Then we informed them of the states of C and E1 and asked them to judge the likelihood of E2. In Experiments 1 and 2, we manipulated whether the mechanisms supporting the two effects were the same or different, and whether the evidence presented confirmed or contradicted the participants' expectations. The relation between the mechanisms only influenced predictions when evidence contradicted the expectations, but not when it was consistent. In Experiments 3 and 4, we used a common-cause structure with identical mechanisms. We manipulated the order in which predictions were made. When confirmatory predictions were made before contradictory predictions, mechanistic modulation was not observed in the confirmatory case. In contrast, the modulation was found when confirmatory predictions were made after contradictory ones. The results support the contradiction hypothesis that causal structure is revised during prediction, but only in the face of unexpected evidence.

Mechanistic beliefs determine adherence to the Markov property in causal reasoning.

What kind of information do people use to make predictions? Causal Bayes nets theory implies that people should follow structural constraints like the Markov property in the form of the screening-off rule, but previous work shows little evidence that people do. We tested six hypotheses that attempt to explain violations of screening off, some by asserting that people use mechanistic knowledge to infer additional latent structure. In three experiments, we manipulated whether the causal relations among variables within a causal structure were supported by the same or different mechanisms. The experiments differed in the type of causal structures (common cause vs. chain), the way that causal structures were presented (verbal description vs. observational learning), how the mechanisms were presented (explicit description vs. implicit description vs. visual hint), and the number of predictions requested (2 vs. 24). The results revealed that the screening-off rule was violated more often when the mechanisms were the same than when they were different. The findings suggest that people use knowledge about underlying mechanisms to infer latent structure for prediction.