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Large amounts of neuroimaging and omics data have been generated for studies of mental health. Collaborations among research groups that share data have shown increased power for new discoveries of brain abnormalities, genetic mutations, and associations among genetics, neuroimaging and behavior. However, sharing raw data can be challenging for various reasons. A federated data analysis allowing for collaboration without exposing the raw dataset of each site becomes ideal. Following this strategy, a decentralized parallel independent component analysis (dpICA) is proposed in this study which is an extension of the state-of-art Parallel ICA (pICA). pICA is an effective method to analyze two data modalities simultaneously by jointly extracting independent components of each modality and maximizing connections between modalities. We evaluated the dpICA algorithm using neuroimage and genetic data from patients with schizophrenia and health controls, and compared its performances under various conditions with the centralized pICA. The results showed dpICA is robust to sample distribution across sites as long as numbers of samples in each site are sufficient. It can produce the same imaging and genetic components and the same connections between those components as the centralized pICA. Thus our study supports dpICA is an accurate and effective decentralized algorithm to extract connections from two data modalities. 

According to the justified true belief (JTB) account of knowledge, people can truly know something only if they have a belief that is both justified and true (i.e., knowledge is JTB). This account was challenged by Gettier, who argued that JTB does not explain knowledge attributions in certain situations, later called “Gettier-type cases,” wherein protagonists are justified in believing something to be true, but their belief was correct only because of luck. Laypeople may not attribute knowledge to protagonists with justified but only luckily true beliefs. Although some research has found evidence for these so-called Gettier intuitions, Turri et al. found no evidence that participants attributed knowledge in a counterfeit-object Gettier-type case differently than in a matched case of JTB. In a large-scale, cross-cultural conceptual replication of Turri and colleagues’ Experiment 1 ( N = 4,724) using a within-participants design and three vignettes across 19 geopolitical regions, we did find evidence for Gettier intuitions; participants were 1.86 times more likely to attribute knowledge to protagonists in standard cases of JTB than to protagonists in Gettier-type cases. These results suggest that Gettier intuitions may be detectable across different scenarios and cultural contexts. However, the size of the Gettier intuition effect did vary by vignette, and the Turri et al. vignette produced the smallest effect, which was similar in size to that observed in the original study. Differences across vignettes suggest that epistemic intuitions may also depend on contextual factors unrelated to the criteria of knowledge, such as the characteristics of the protagonist being evaluated.