Hypothesis Testing and Associative Learning in Cross-Situational Word Learning: Are They One and the Same?

Recent studies (e.g. Yu & Smith, in press; Smith & Yu, submitted) show that both adults and young children possess powerful statistical computation capabilities-- they can infer the referent of a word from highly ambiguous contexts involving many words and many referents. This paper goes beyond demonstrating empirical behavioral evidence-- we seek to systematically investigate the nature of the underlying learning mechanisms. Toward this goal, we propose and implement a set of computational models based on three mechanisms: (1) hypothesis testing; (2) dumb associative learning; and (3) advanced associative learning. By applying these models to the same materials used in learning studies with adults and children, we first conclude that all the models can fit behavioral data reasonably well. The implication is that these mechanisms – despite their seeming difference--may be fundamentally (or formally) the same. In light of this, we propose a formal unified view of learning principles that is based on the shared ground between them. By doing so, we suggest that the traditional controversy between hypothesis testing and associative learning as two distinct learning machineries may not exist