In learning the meanings of words, children are guided by a set of
constraints that give privilege to some potential meanings over
others. These word-learning constraints are sometimes viewed as part
of a specifically linguistic endowment. However, several recent
computational models suggest concretely how word learning -
constraints included - might emerge from more general aspects of
cognition, such as associative learning, attention and rational
inference. This article reviews these models, highlighting the link
between general cognitive forces and the word-learning they
subserve. Ultimately, these cognitive forces might leave their mark
not just on language learning, but also on language itself: in
constraining the space of possible meanings, they place limits on
cross-linguistic semantic variation.