Predictors of lexical stability in an artificially learnt language


Lexical items in the vocabulary of a language undergo dramatic changes over time, explaining the mechanisms that cause this change has been an important topic for the cognitive sciences. One particular focus for researchers has been understanding the dynamics of change in word forms. The rate (or half-life) at which word forms change over time varies greatly, and corpus-based cladistic studies have shown that certain properties, such as word frequency, length and age of acquisition, can be used to predict this variation. We test through the use of an artificial language learning paradigm the extent to which these psycholinguistic factors affect accurate learning of word forms, linking processes of acquisition with processes of evolutionary change. Our findings provide an insight into the underlying mechanisms that drive diachronic change within a language’s vocabulary, highlighting the important role that the learning process has on lexical change.

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