The universal properties of human languages have been the subject of intense study across the language sciences. We report computational and corpus evidence for the hypothesis that a prominent subset of these universal properties-those related to word order-result from a process of optimization for efficient communication among humans, trading off the need to reduce complexity with the need to reduce ambiguity. We formalize these two pressures with information-theoretic and neural-network models of complexity and ambiguity and simulate grammars with optimized word-order parameters on large-scale data from 51 languages. Evolution of grammars toward efficiency results in word-order patterns that predict a large subset of the major word-order correlations across languages.

Verb subcategorization frequencies (verb biases) have been widely studied in psycholinguistics and play an important role in human sentence processing. Yet available resources on subcategorization frequencies suffer from limited coverage, limited ecological validity, and divergent coding criteria. Prior estimates of verb transitivity, for example, vary widely with corpus size, coverage, and coding criteria This article provides norming data for 281 verbs of interest to psycholinguistic research, sampled from a corpus of American English, along with a detailed coding manual. We examine the effect on transitivity bias of various coding decisions and methods of computing verb biases.

Across languages, adjectives are subject to ordering restric-tions. Recent research shows that these are predicted by ad-jective subjectivity, but the question remains open why this isthe case. We first conduct a corpus study and not only replicatethe subjectivity effect, but also find a previously undocumentedeffect of mutual information between adjectives and nouns.We then describe a rational model of adjective use in whichlisteners explicitly reason about judgments made by differentspeakers, formalizing the notion of subjectivity as agreementbetween speakers. We show that, once incremental process-ing is combined with memory limitations, our model predictseffects both of subjectivity and mutual information. We con-firm the adequacy of our model by evaluating it on corpus data,finding that it correctly predicts ordering in unseen data withan accuracy of 96.2 %. This suggests that adjective orderingcan be explained by general principles of human communica-tion and language processing.