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Abstract In the classical information theoretic framework, information “value” is proportional to how novel/surprising the information is. Recent work building on such notions claimed that false news spreads faster than truth online because false news is more novel and therefore surprising. However, another determinant of surprise, semantic meaning (e.g., information’s consistency or inconsistency with prior beliefs), should also influence value and sharing. Examining sharing behavior on Twitter, we observed separate relations of novelty and belief consistency with sharing. Though surprise could not be assessed in those studies, belief consistency should relate to less surprise, suggesting the relevance of semantic meaning beyond novelty. In two controlled experiments, belief-consistent (vs. belief-inconsistent) information was shared more despite consistent information being the least surprising. Manipulated novelty did not predict sharing or surprise. Thus, classical information theoretic predictions regarding perceived value and sharing would benefit from considering semantic meaning in contexts where people hold pre-existing beliefs. 

Adaptive optics-optical coherence tomography (AO-OCT) allows for the three-dimensional visualization of retinal ganglion cells (RGCs) in the living human eye. Quantitative analyses of RGCs have significant potential for improving the diagnosis and monitoring of diseases such as glaucoma. Recent advances in machine learning (ML) have made possible the automatic identification and analysis of RGCs within the complex three-dimensional retinal volumes obtained with such imaging. However, the current state-of-the-art ML approach relies on fully supervised training, which demands large amounts of training labels. Each volume requires many hours of expert manual annotation. Here, two semi-supervised training schemes are introduced, (i) cross-consistency training and (ii) cross pseudo supervision that utilize unlabeled AO-OCT volumes together with a minimal set of labels, vastly reducing the labeling demands. Moreover, these methods outperformed their fully supervised counterpart and achieved accuracy comparable to that of human experts.