A recent proposal argued a structure like X1’s-X2 is recursive if X1 and X2 are substitutable as the head; the head requirement avoids wrong generalizations like ‘*dogs chase cats chase rats’ since NP is not the head of NP1-V-NP2 although the NPs can be substitutable. To test the role of headedness in the distributional learning of recursion, we exposed 50 adults to one of two artificial languages: both allowed A1-B-A2 strings, with A1 and A2 substitutable; but the head was A2 in one language and B in the other. Non-head elements were optional, so the two languages allowed different linear strings. In the rating test, at predicted, participants from both conditions learned the head; and while there was no difference for ABA strings between conditions, suggesting they all learned linear substitutability, participants exposed to head substitutability (A-head) were more willing to allow recursion (ABABA) with both attested and unattested words.

Languages differ regarding the depth, structure, and syntactic domains of recursive structures. Even within a language, some structures allow infinite self-embedding while others are more restricted. For example, English allows infinite free embedding of the prenominal genitive -s, whereas the postnominal genitive of is largely restricted to one level and to a limited set of items. Therefore, speakers need to learn from experience which specific structures allow free embedding and which do not. One effort to account for the underlying learning mechanism, the distributional learning proposal, suggests the recursion of a structure (e.g. X1’s- X2) is licensed if X1 position and X2 position are productively substitutable in the input. A series of corpus studies have confirmed the availability of such distributional cues in child directed speech. The present study further tests the distributional learning proposal with an artificial language learning experiment.

While the ability of recursion is considered universally available, there are considerable cross- and with-linguistic differences regarding the rules for recursive embedding, which must be learned from language-specific experience. One proposal argues that the recursivity of a structure is learnable as a productive generalization from distributional information in non-embedded input, and adults can indeed use such distributional cues to acquire recursive structures in an artificial language. However, it is not yet known whether children can use distributional information in this way. In this work, we examine children's distributional learning of recursive structures. We exposed children to non-embedded sentences in an artificial grammar, where we manipulated the productivity of the structure across conditions. At test, we found that children exposed to productive input were more likely to accept recursively embedded sentences unattested during the exposure phase. The results suggest that children can make use of distributional information to acquire recursive structures.

During language acquisition, children must learn when togeneralize a pattern – applying it broadly and to new words(‘add –ed’ in English) – and when to restrict generalization,storing the pattern only with specific lexical items. One effortto quantify the conditions for generalization, the TolerancePrinciple, has been shown to accurately predict children’sgeneralizations in dozens of corpus-based studies. Thisprinciple hypothesizes that a general rule will be formedwhen it is computationally more efficient than storing lexicalforms individually. It is formalized as: a rule R will generalizeif the number of exceptions does not exceed the number ofwords in the category N divided by the natural log of N(N/lnN). Here we test the principle in an artificial language of9 nonsense nouns. As predicted, children exposed to 5 regularforms and 4 exceptions generalized, applying the regular formto 100% of novel test words. Children exposed to 3 regularforms and 6 exceptions did not extend the rule, even thoughthe token frequency of the regular form was still high in thiscondition. The Tolerance Principle thus appears to capture abasic principle of generalization in rule formation.

Children exposed to inconsistent language variation regularize this variation in their productions (Hudson-Kam &Newport, 2005). Existing demonstrations of regularization observe this behavior when the signal-to-noise ratio is greater-than-or-equal-to 40%, but whether regularization occurs when the dominant form is less widespread has not been investigated. Arecent computational model, the Sufficiency Principle, quantifies when a pattern is widespread enough to generalize (Yang,2016): Let R be a generalization over N items, of which M are attested to follow R. R extends to all N items iff: N-M