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 design and conduct as a series of simulation studies in which we systematically manipulate, across three models, learning input, learning parameters, and decision-making procedures at testing. Our simulation results suggest a formal unified view of learning principles that is based on the shared ground between three mechanisms. By doing so, we argue that the traditional controversy between hypothesis testing and associative learning as two distinct learning machineries may not exist, and the exploration of learning mechanisms within such a unified view may reveal new insights about learning processes that fall between these two classic extremes. |