Texts available on the Web have been generated by human minds. We observe that simple patterns are over-represented: abcdef is more frequent than arfbxg and 1000 appears more often than 1282. We suggest that word frequency patterns can be predicted by cognitive models based on complexity minimization. Conversely, the observation of word frequencies offers an opportunity to infer particular cognitive mechanisms involved in their generation.

Several computational methods have been proposed toevaluate the relevance of an instantiated cause to anobserved consequence. The paper reports on an ex-periment to investigate the adequacy of some of thesemethods as descriptors of human judgments aboutcausal relevance.