There is substantial variation in language experience, yet there is surprising similarity in the language structure acquired. Constraints on language structure may be external modulators that result in this canalization of language structure, or else they may derive from the broader, communicative environment in which language is acquired. In this paper, the latter perspective is tested for its adequacy in explaining robustness of language learning to environmental variation. A computational model of word learning from cross-situational, multimodal information was constructed and (...) tested. Key to the model's robustness was the presence of multiple, individually unreliable information sources to support learning. This “degeneracy” in the language system has a detrimental effect on learning, compared to a noise-free environment, but has a critically important effect on acquisition of a canalized system that is resistant to environmental noise in communication. (shrink)

Psychologists have used experimental methods to study language for more than a century. However, only with the recent availability of large-scale linguistic databases has a more complete picture begun to emerge of how language is actually used, and what information is available as input to language acquisition. Analyses of such “big data” have resulted in reappraisals of key assumptions about the nature of language. As an example, we focus on corpus-based research that has shed new light on the arbitrariness of (...) the sign: the longstanding assumption that the relationship between the sound of a word and its meaning is arbitrary. The results reveal a systematic relationship between the sound of a word and its meaning, which is stronger for early acquired words. Moreover, the analyses further uncover a systematic relationship between words and their lexical categories—nouns and verbs sound differently from each other—affecting how we learn new words and use them in sentences. Together, these results point to a division of labor between arbitrariness and systematicity in sound-meaning mappings. We conclude by arguing in favor of including “big data” analyses into the language scientist's methodological toolbox. (shrink)

This study investigates the child‐directed speech (CDS) of four Russian‐, six German, and six English‐speaking mothers to their 2‐year‐old children. Typologically Russian has considerably less restricted word order than either German or English, with German showing more word‐order variants than English. This could lead to the prediction that the lexical restrictiveness previously found in the initial strings of English CDS by Cameron‐Faulkner, Lieven, and Tomasello (2003) would not be found in Russian or German CDS. However, despite differences between the three (...) corpora that clearly derive from typological differences between the languages, the most significant finding of this study is a high degree of lexical restrictiveness at the beginnings of CDS utterances in all three languages. (shrink)

The transition from Middle English to Modern English in the second half of the 14th century is a turning point in the syntax of the language. It is at once the point when several constraints on nominal arguments that had been gaining ground since Old English become categorical, and the point when a reorganization of the functional category Infl is initiated, whose completion over the next several centuries yields essentially the syntactic system of the present day. From this time on, (...) subjects are obligatory, and they must be placed in Spec-IP position. In the VP, the last traces of OV order disappear, the order of direct and indirect object becomes fixed, the first “recipient passives” enter the language, and objects cease to be separable from the verb by adverbs or adjuncts. The V2 constraint of Old and Middle English is lost, as topicalized constituents cease to trigger verb-fronting. Concurrently, in the Infl system, the first instances of periphrastic do-support begin to replace fronting of the finite main verb, and, with the appearance of split infinitives and pro-infinitives, to starts to pattern as a non-finite Aux rather than, as in earlier stages, as a prefix marking the infinitive. All these changes have been dated to the second half of the 14th century, most of them specifically to the period between 1360 and 1380. (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)

There is substantial variation in language experience, yet there is surprising similarity in the language structure acquired. Constraints on language structure may be external modulators that result in this canalization of language structure, or else they may derive from the broader, communicative environment in which language is acquired. In this paper, the latter perspective is tested for its adequacy in explaining robustness of language learning to environmental variation. A computational model of word learning from cross-situational, multimodal information was constructed and (...) tested. Key to the model's robustness was the presence of multiple, individually unreliable information sources to support learning. This “degeneracy” in the language system has a detrimental effect on learning, compared to a noise-free environment, but has a critically important effect on acquisition of a canalized system that is resistant to environmental noise in communication. (shrink)

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