Over half a century ago, George Zipf observed that more frequent words tend to be older. Corpus studies since then have confirmed this pattern, with more frequent words being replaced and regularized less often than less frequent words. Two main hypotheses have been proposed to explain this: that frequent words change less because selection against innovation is stronger at higher frequencies, or that they change less because stochastic drift is stronger at lower frequencies. Here, we report the first experimental test (...) of these hypotheses. Participants were tasked with learning a miniature language consisting of two nouns and two plural markers. Nouns occurred at different frequencies and were subjected to treatments that varied drift and selection. Using a model that accounts for participant heterogeneity, we measured the rate of noun regularization, the strength of selection, and the strength of drift in participant responses. Results suggest that drift alone is sufficient to generate the elevated rate of regularization we observed in low‐frequency nouns, adding to a growing body of evidence that drift may be a major driver of language change. (shrink)

Cognitive Science, Volume 46, Issue 3, March 2022.

Language research has come to rely heavily on large‐scale, web‐based datasets. These datasets can present significant methodological challenges, requiring researchers to make a number of decisions about how they are collected, represented, and analyzed. These decisions often concern long‐standing challenges in corpus‐based language research, including determining what counts as a word, deciding which words should be analyzed, and matching sets of words across languages. We illustrate these challenges by revisiting “Word lengths are optimized for efficient communication” (Piantadosi, Tily, & Gibson, (...) 2011), which found that word lengths in 11 languages are more strongly correlated with their average predictability (or average information content) than their frequency. Using what we argue to be best practices for large‐scale corpus analyses, we find significantly attenuated support for this result and demonstrate that a stronger relationship obtains between word frequency and length for a majority of the languages in the sample. We consider the implications of the results for language research more broadly and provide several recommendations to researchers regarding best practices. (shrink)

Zipf famously stated that, if natural language lexicons are structured for efficient communication, the words that are used the most frequently should require the least effort. This observation explains the famous finding that the most frequent words in a language tend to be short. A related prediction is that, even within words of the same length, the most frequent word forms should be the ones that are easiest to produce and understand. Using orthographics as a proxy for phonetics, we test (...) this hypothesis using corpora of 96 languages from Wikipedia. We find that, across a variety of languages and language families and controlling for length, the most frequent forms in a language tend to be more orthographically well‐formed and have more orthographic neighbors than less frequent forms. We interpret this result as evidence that lexicons are structured by language usage pressures to facilitate efficient communication. (shrink)

Cross-linguistic studies focus on inverse correlations (trade-offs) between linguistic variables that reflect different cues to linguistic meanings. For example, if a language has no case marking, it is likely to rely on word order as a cue for identification of grammatical roles. Such inverse correlations are interpreted as manifestations of language users’ tendency to use language efficiently. The present study argues that this interpretation is problematic. Linguistic variables, such as the presence of case, or flexibility of word order, are aggregate (...) properties, which do not represent the use of linguistic cues in context directly. Still, such variables can be useful for circumscribing the potential role of communicative efficiency in language evolution, if we move from cross-linguistic trade-offs to multivariate causal networks. This idea is illustrated by a case study of linguistic variables related to four types of Subject and Object cues: case marking, rigid word order of Subject and Object, tight semantics and verb-medial order. The variables are obtained from online language corpora in thirty languages, annotated with the Universal Dependencies. The causal model suggests that the relationships between the variables can be explained predominantly by sociolinguistic factors, leaving little space for a potential impact of efficient linguistic behavior. (shrink)

Biosemiosis is a process of choice-making between simultaneously alternative options. It is well-known that, when sufficiently young children encounter a new word, they tend to interpret it as pointing to a meaning that does not have a word yet in their lexicon rather than to a meaning that already has a word attached. In previous research, the strategy was shown to be optimal from an information theoretic standpoint. In that framework, interpretation is hypothesized to be driven by the minimization of (...) a cost function: the option of least communication cost is chosen. However, the information theoretic model employed in that research neither explains the weakening of that vocabulary learning bias in older children or polylinguals nor reproduces Zipf’s meaning-frequency law, namely the non-linear relationship between the number of meanings of a word and its frequency. Here we consider a generalization of the model that is channeled to reproduce that law. The analysis of the new model reveals regions of the phase space where the bias disappears consistently with the weakening or loss of the bias in older children or polylinguals. The model is abstract enough to support future research on other levels of life that are relevant to biosemiotics. In the deep learning era, the model is a transparent low-dimensional tool for future experimental research and illustrates the predictive power of a theoretical framework originally designed to shed light on the origins of Zipf’s rank-frequency law. (shrink)

Discovering the meaning of novel communicative cues is challenging and amounts to navigating an unbounded hypothesis space. Several theories posit that this problem can be simplified by relying on positive expectations about the cognitive utility of communicated information. These theories imply that learners should assume that novel communicative cues tend to have low processing costs and high cognitive benefits. We tested this hypothesis in three studies in which toddlers (N = 90) searched for a reward hidden in one of several (...) containers. In all studies, an adult communicated the reward's location with an unfamiliar and ambiguous cue. We manipulated the processing costs (operationalized as inferential chain length) and cognitive benefits (operationalized as informativeness) of the possible interpretations of the cues. Toddlers processing of novel communicative cues were guided by expectations of low processing costs (Study 1) and high cognitive benefits (Studies 2 and 3). More specifically, toddlers treated novel cues as if they were easy to process, informative, and accurate, even when provided with repeated evidence to the contrary. These results indicate that, from toddlerhood onward, expectations of cognitive utility shape the processing of novel communicative cues. These data also reveal that toddlers, who are in the process of learning the language and communicative conventions of people around them, exert a pressure favoring cognitive efficiency in communicative systems. (shrink)