Some Generalizations About Linguistic Generalization By Infants

One dimension on which more vs. less strongly constrained models of language acquisition vary is the amount of evidence required for a particular linguistic generalization. ‘Triggering’ models require, in the limit, only a single datum to set an innate parameter, whereas less constrained models often arrive at a generalization by performing statistics over many exemplars from an input set. I will present data from three lines of research with 9- to 17-month-old infants, which explore the amount and type of input required for learners to generalize beyond the stimuli encountered in a brief laboratory exposure. All of the studies suggest that generalization requires a minimal number of data points, but more than just one, and that different subsets of the input lead to different generalizations. Some surprising new data suggest that some types of generalization (those attested by natural languages) may be easier to make than others (those unattested by natural languages). Taken together, the data provide direction for examining the ways in which innate constraints and learning via statistics may combine in human language development.