Do Humans Really Learn A n B n Artificial Grammars From Exemplars?

An important topic in the evolution of language is the kinds of grammars that can be computed by humans and other animals. Fitch and Hauser (F&H; 2004) approached this question by assessing the ability of different species to learn 2 grammars, ( AB ) n and A n B n . A n B n was taken to indicate a phrase structure grammar , eliciting a center‐embedded pattern. ( AB ) n indicates a grammar whose strings entail only local relations between the categories of constituents. F&H's data suggest that humans, but not tamarin monkeys, learn an A n B n grammar, whereas both learn a simpler ( AB ) n grammar (Fitch & Hauser, 2004). In their experiments, the A constituents were syllables pronounced by a female voice, whereas the B constituents were syllables pronounced by a male voice. This study proposes that what characterizes the A n B n exemplars is the distributional regularities of the syllables pronounced by either a male or a female rather than the underlying, more abstract patterns. This article replicates F&H's data and reports new controls using either categories similar to those in F&H or less salient ones. This article shows that distributional regularities explain the data better than grammar learning. Indeed, when familiarized with A n B n exemplars, participants failed to discriminate A 3 B 2 and A 2 B 3 from A n B n items, missing the crucial feature that the number of A s must equal the number of B s. Therefore, contrary to F&H, this study concludes that no syntactic rules implementing embedded nonadjacent dependencies were learned in these experiments. The difference between human linguistic abilities and the putative precursors in monkeys deserves further exploration.