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\"Languages transmit information. They are vessels carrying meaning across metres, kilometres, and around the globe. This book harnesses information theory to measure word-level differences in more than 1200 languages. It further models geographic, demographic and social factors to explain the diversity of encoding strategies. The results support the framework of languages as complex adaptive systems which are shaped by the varying needs of their users.\" -- Page 4 of cover.

This book brings together twelve authors who look at the concept of the \"word\" from several different perspectives, inspiring in the reader a sense of wonder - to think of the lowly word, which we toss away in yesterday’s newspaper, which we ignore on street signs, which we utter without giving a thought to the consequences of the power carried by the word. Moving from a psycholinguist explanation of the acquisition of language, the volume presents the function of the word in \"bad\" jokes, in.

\"One of the basic grammatical categories in linguistics is the phonological word. But how are words made up in terms of their sounds? And how is the information on the sound structure of words used in the processing of words? This volume brings together scholars interested in the complex relations of the phonological word, applying different empirical approaches.\"-- Back cover.

Strings of letters or sounds that lack meaning (i.e., nonwords) have been used in cognitive psychology and psycholinguistics to provide foundational knowledge of human processing and representation, and insights into language-related performance. The present set of studies used the machine psycholinguistic approach (i.e., using nonword stimuli and tasks similar to those used with humans) to gain insight into the performance of Chat GPT in comparison to human performance. In Study 1, Chat GPT was able to provide correct definitions to many extinct words (i.e., real English words that are no longer used). In Study 2 the nonwords were real words in Spanish, and Chat GPT was prompted to provide a word that sounded similar to the nonword. Responses tended to be Spanish words unless the prompt specified that the similar sounding word should be an English word. In Study 3 Chat GPT provided subjective ratings of wordlikeness (and buyability) that correlated with ratings provided by humans, and with the phonotactic probabilities of the nonwords. In Study 4, Chat GPT was prompted to generate a new English word for a novel concept. The results of these studies highlight certain strengths and weaknesses in human and machine performance. Future work should focus on developing AI that complements or extends rather than duplicates or competes with human abilities. The machine psycholinguistic approach may help to discover additional strengths and weaknesses of human and artificial intelligences.

Words on the brink As a language evolves, grammatical rules emerge and exceptions die out. Lieberman et al . have calculated the rate at which a language grows more regular, based on 1,200 years of English usage. Of 177 irregular verbs, 79 became regular in the last millennium. And the trend follows a simple rule: a verb's half-life scales as the square root of its frequency. Irregular verbs that are 100 times as rare regularize 10 times faster. The emergence of a rule (such as adding – ed for the past tense) spells death for exceptional forms. The cover graphic makes the point: verb size corresponds to usage frequency, so large verbs stay at the top, and small verbs fall to the bottom. ' Wed ', the next irregular verb to go, is on the brink. In a separate study, Pagel et al . looked at changing word meanings. Across the Indo-European languages, words like ' tail ' or ' bird ' evolve rapidly and are expressed by many unrelated words. Others, like ' two ', are expressed by closely related word forms across the whole language family. Data from over 80 modern languages show that the more a word is used, the less it changes. Statistical modelling techniques and concepts from biology are applied to linguistic data to show a general and law-like relationship between the frequency with which meanings are used in everyday language and their rate of evolution throughout Indo-European history: the more a word is used, the less likely it is to change over time. The findings demonstrate a fundamental aspect of language evolution that is predicted to apply to all languages. Greek speakers say “ο υρ ”, Germans “ schwanz ” and the French “ queue ” to describe what English speakers call a ‘tail’, but all of these languages use a related form of ‘two’ to describe the number after one. Among more than 100 Indo-European languages and dialects, the words for some meanings (such as ‘tail’) evolve rapidly, being expressed across languages by dozens of unrelated words, while others evolve much more slowly—such as the number ‘two’, for which all Indo-European language speakers use the same related word-form 1 . No general linguistic mechanism has been advanced to explain this striking variation in rates of lexical replacement among meanings. Here we use four large and divergent language corpora (English 2 , Spanish 3 , Russian 4 and Greek 5 ) and a comparative database of 200 fundamental vocabulary meanings in 87 Indo-European languages 6 to show that the frequency with which these words are used in modern language predicts their rate of replacement over thousands of years of Indo-European language evolution. Across all 200 meanings, frequently used words evolve at slower rates and infrequently used words evolve more rapidly. This relationship holds separately and identically across parts of speech for each of the four language corpora, and accounts for approximately 50% of the variation in historical rates of lexical replacement. We propose that the frequency with which specific words are used in everyday language exerts a general and law-like influence on their rates of evolution. Our findings are consistent with social models of word change that emphasize the role of selection, and suggest that owing to the ways that humans use language, some words will evolve slowly and others rapidly across all languages.