Bayesian updating remains the benchmark for dynamic modeling under uncertainty within economics. Recent theory and evidence suggests individuals may process information in a biased manner when it relates to personal characteristics or future life outcomes. Specifically, updating has been found to be asymmetric, with good news receiving more weight than bad news. I put this theory and evidence to the test, by examining information processing across multiple domains with varying stake conditions. I do not find that good news is over-weighted relative to bad news, but in fact, I find the opposite asymmetry. However these updating patterns are present more generally, including when news is neither good nor bad. While updating across all domains and stake conditions is asymmetric and conservative, posteriors remain well approximated by those calculated using Bayes' rule. I investigate further possible determinants of asymmetry and conservatism, finding that the former is sensitive to signal types and the latter is driven solely by non-updates. Most importantly these patterns are present across all domains, cautioning against the interpretation of asymmetric updating or other deviations from Bayes' rule as being motivated by psychological biases.