Recently the principles of efficient communication have provided useful characterizations of semantic typology: the diversity of attested languages can be described by competing pressures for simplicity and informativeness. While this approach has achieved success in several semantic domains, the formalizations used to define complexity across domains vary. In this note, we list the conditions under which the two main approaches of defining complexity: channel rate and description length, unify and, thus, conclusions about near-optimal communicative efficiency generalize across formalizations. We illustrate this equivalence using simulations of communicative efficiency for Boolean concepts. We round out this note discussing the (un)importance of description languages and the limits on generalizing this equivalence for other behavioral targets for explanation.

Grammatical features vary widely across languages and thisvariation has been studied in detail. The functions of gram-matical features, however, are not entirely clear and a numberof puzzles remain. For example, why do some languages haverich feature inventories but others have few if any grammaticalfeatures? Why do many languages have features that appearto encode semantic information (e.g. animacy) that is alreadyknown to the listener? We present a computational frameworkthat addresses questions like these by formalizing one way inwhich grammatical features aid communication. We use themodel to illustrate how morpho-syntactic feature inventorieshelp to solve the problem of communicating semantic struc-tures under cognitive pressures.

Current models of word learning focus on the mapping between words and their referents and remain mute with regard to conceptual representation. We develop a cross-situational model of word learning that captures word-concept mapping by jointly inferring the referents and underlying concepts for each word. We also develop a variant of our model that incorporates recursion, which entertains the idea that children can use learned words to aid future learning. We demonstrate both models’ ability to learn kinship terms and show that adding recursion into the model speeds acquisition

People have the capability to process text three times fasterthan they would naturally read it, yet many current theories ofsentence processing rely on natural reading times as a proxyfor processing difficulty. How can people read material soquickly in spite of information processing limitations sug-gested by sentence processing theories? One possibility is thatsurprisal effects on reading time, the hallmark of processingdifficulty under sentence processing theories, might arise fromperceptual processing, implying no relation between surprisaland sentence processing difficulty. In this paper, we conducteda novel self-paced rapid serial visual presentation (RSVP) ex-periment, which controlled perceptual processes to probe forsentence processing related surprisal effects. We further testedhow readers might compensate for information processing lim-its during RSVP. We find support for sentence processing re-lated surprisal effects, the pattern of which is consistent with aFirst-In, First-Out (FIFO) buffer model.

Over the last 50 years, there have been efforts on behalf of the US government to simplify public legal documents for the benefit of society at large . However, there has been no systematic evaluation of how effective these efforts--collectively referred to as the ``plain language movement''--have been. Here we report the results of a large-scale longitudinal corpus analysis (n$\approx$225 million words), in which we compare every law passed by congress between 1951 to 2009 (as well as concurrent resolutions and proclamations), with a comparably sized sample of English texts from four different genres published during the same time period. We find that laws remain laden with features associated with processing difficulty--including center-embedding, passive voice, low-frequency jargon and capitalization--relative to each of the four baseline genres of English, and that the prevalence of these features has not meaningfully declined since the onset of the plain language movement (in some cases, their prevalence has increased). These findings suggest that top-down efforts to simplify legal language have thus far remained largely ineffectual, despite the apparent tractability of these changes, raising and informing difficult questions of law and public policy.

Languages vary in the way they categorize semantic domains. Incidentally certain semantic systems appear more often than others across the world. Recent research has shown that the attested variability can be explained as the result of languages being a plurality of optimal solutions to efficiency constraints. However, the question of the prevalence remains open. Assuming that languages are a form of culturally transmitted cognitive technology, the Typological Prevalence Hypothesis proposes that the prevalence of a linguistic system is explained by how cognitively natural it is to learn and use. We aim to formalize and test this hypothesis by proposing an information-theoretic measure of communicative and developmental naturalness applied to color typology. While controlling for phylogenetic relatedness, we find that both communicative and developmental naturalness are important predictors of typological prevalence.

Word frequency effects have long served as an empirical andtheoretical test bed for theories of language processing. Anumber of recent studies have suggested that Contextual Di-versity (CD) is a better metric of retrieval processes than wordfrequency. Motivated by these findings, we sketch an activeaccount of lexical access during sentence processing: lan-guage users store statistics about contextualized lexical rep-resentations and use lexical-contextual relations to both con-struct context and predict words given the context. In linewith our account, we provide evidence from a frequency judg-ment experiment suggesting that words are not stored indepen-dently of their contexts of use. To further examine CD effectsin reading, we analyzed reading times in self-paced readingand eye-tracking corpora. We demonstrate that as context isconstructed, the role of CD in lexical retrieval is attenuated,reflecting a trade-off between context construction and contex-tualized word prediction.

When learning about the world, we develop mental represen-tations or concepts for things we have never seen. At the sametime, we also develop representations for things that are similarto what we have experienced. Traditionally, similarity-basedand rule-based systems have been used as distinct models tocapture conceptual representation. However, it seems implau-sible that we do not flexibly deploy both systems. Whetherboth systems can be used simultaneously to represent compo-nents of a single concept is an open empirical question. Oneexample suggesting that the use of both systems is possible isthe concept of a ZEBRA , which looks like a horse but striped.Using an artificial concept learning task, we test whether peo-ple can combine similarity and rules compositionally in orderto represent concepts. Our results suggest that people are ableto compose similarity and rules when mentally representing asingle concept.

Whereas principles of communicative efficiency and legal doctrine dictate that laws be comprehensible to the common world, empirical evidence suggests legal documents are largely incomprehensible to lawyers and laypeople alike. Here, a corpus analysis (n=59 million words) first replicated and extended prior work revealing laws to contain strikingly higher rates of complex syntactic structures relative to six baseline genres of English. Next, two pre-registered text generation experiments (n=280) tested two leading hypotheses regarding how these complex structures enter into legal documents in the first place. In line with the \textit{magic spell hypothesis}, we found people tasked with writing official laws wrote in a more convoluted manner than when tasked with writing unofficial legal texts of equivalent conceptual complexity. Contrary to the \textit{copy-and-edit hypothesis}, we did not find evidence that people editing a legal document wrote in a more convoluted manner than when writing the same document from scratch. From a cognitive perspective, these results suggest law to be a rare exception to the general tendency in human language towards communicative efficiency. In particular, these findings indicate law's complexity to be derived from its performativity, whereby low-frequency structures may be inserted to signal law's authoritative, world-state-altering nature, at the cost of increased processing demands on readers. From a law and policy perspective, these results suggest that the tension between the ubiquity and impenetrability of the law is not an inherent one, and that laws can be simplified without a loss or distortion of communicative content.

Collocations, semi-productive lexical combinations with one figurative and one literal word, are said to be a “pain in the neck” for researchers and L2 learners. The present study aims: (i) to conceptually replicate the processing costs incurred by L1 speakers when processing collocations using a larger and more diverse set of items, (ii) to use literalness judgements to test whether L1 speakers are aware of the semi-transparent meaning of a collocation, and (iii) to test whether the presence of processing costs associated with collocations can be predicted from literalness judgements. If so, we propose that literalness judgements could be used as a diagnostic for reli- ably identifying collocations. We replicate the L1 processing costs with a larger stimulus set and demonstrate that speakers are aware of the semi-transparent meaning of the collocation. We further show that L1 speaker judgements about the literal- ness of a word combination can be used to predict its status as a collocation.