Mutual Exclusivity in Cross-Situational Statistical Learning

Abstract

The Mutual Exclusivity (ME) constraint -a preference for mapping one word to one object -has been shown to be a powerful aid to children learning new words.We ask whether cross-situational language learning, in which word meanings are learned through computation of word-object co-occurrences across a series of highly ambiguous trials, is subject to the ME constraint.Our results show that participants can break the constraint to learn one-to-two word-referent mappings both when the referents are separated across time and when they are interleaved.This demonstrates the robustness of cross-situational statistical learning.We then use participants' ratings of their knowledge after individual trials to shed light on the underlying learning mechanism.Our results suggest that the ME constraint may be applied at multiple points along learning -within a single trial, across trials, and at test -which may explain one of its residual test effects found in the traditional language literature.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 36 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.