Linguistic interventions aim to change our linguistic practices. A commonly discussed type of linguistic intervention is meaning revision, which seeks to associate existing words with new or revised meanings. But why does retaining old words matter so much? Why not instead introduce new words to express the newly defined meanings? Drawing on relevant psycholinguistic research, this paper develops an empirically motivated, general, and practically useful pro tanto reason to retain rather than replace the original word during the process of conceptual (...) improvement. (shrink)

Identifying the referent of novel words is a complex process that young children do with relative ease. When given multiple objects along with a novel word, children select the most novel item, sometimes retaining the word‐referent link. Prior work is inconsistent, however, on the role of object novelty. Two experiments examine 18‐month‐old children's performance on referent selection and retention with novel and known words. The results reveal a pervasive novelty bias on referent selection with both known and novel names and, (...) across individual children, a negative correlation between attention to novelty and retention of new word‐referent links. A computational model examines possible sources of the bias, suggesting novelty supports in‐the‐moment behavior but not retention. Together, results suggest that when lexical knowledge is weak, attention to novelty drives behavior, but alone does not sustain learning. Importantly, the results demonstrate that word learning may be driven, in part, by low‐level perceptual processes. (shrink)

One of the great challenges in word learning is that words are typically uttered in a context with many potential referents. Children's tendency to associate novel words with novel referents, which is taken to reflect a mutual exclusivity (ME) bias, forms a useful disambiguation mechanism. We study semantic learning in pragmatic agents—combining the Rational Speech Act model with gradient‐based learning—and explore the conditions under which such agents show an ME bias. This approach provides a framework for investigating a pragmatic account (...) of the ME bias in humans but also for building artificial agents that display an ME bias. A series of analyses demonstrates striking parallels between our model and human word learning regarding several aspects relevant to the ME bias phenomenon: online inference, long‐term learning, and developmental effects. By testing different implementations, we find that two components, pragmatic online inference and incremental collection of evidence for one‐to‐one correspondences between words and referents, play an important role in modeling the developmental trajectory of the ME bias. Finally, we outline an extension of our model to a deep neural network architecture that can process more naturalistic visual and linguistic input. Until now, in contrast to children, deep neural networks have needed indirect access to (supposed to be novel) test inputs during training to display an ME bias. Our model is the first one to do so without using this manipulation. (shrink)