Abstract: When people discuss which of several hypotheses is true, they share not only their beliefs but also their interpretation of each one, that is, the distribution of signals each hypothesis is expected to produce. This interpretation is an opinion about what each hypothesis means and what evidence it predicts, and it is revised only through conversation, not by confronting it with the observed data. Standard non-Bayesian social learning captures only belief-sharing and keeps the likelihoods private. We study what happens when agents share both beliefs and likelihoods under static networks. We show that interpretations converge geometrically to a common reading shaped by the most influential agents. Then, beliefs concentrate, almost surely and at an exponential rate, on the hypothesis that, under this consensus interpretation, best explains the signals privately observed by all agents taken together. As a result, sharing interpretations can lead a population to adopt, unanimously and confidently, a hypothesis it would have rejected if it had shared only beliefs. Whether this helps or hurts when a true hypothesis exists depends on how accurately agents initially read the data they observe. When every agent initially interprets the true hypothesis exactly as the data it would observe, sharing interpretations cannot strictly improve learning and can strictly harm it when agents observe different data distributions. When interpretations are logically coherent and some agents misread their own data, sharing can either rescue the population, leading it to learn the true hypothesis, if an accurate reading dominates the interpretation network, or corrupt it, leading it to a wrong hypothesis, if a misreading one does.