Testing the Tolerance Principle: Children form productive rules when it is more computationally efficient to do so

During language acquisition, children must learn when to generalize 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 effort to quantify the conditions for generalization, the Tolerance Principle, has been shown to accurately predict children’s generalizations in dozens of corpus-based studies. This principle hypothesizes that a general rule will be formed when it is computationally more efficient than storing lexical forms individually. It is formalized as: a rule R will generalize if the number of exceptions does not exceed the number of words in the category N divided by the natural log of N (N/lnN). Here we test the principle in an artificial language of 9 nonsense nouns. As predicted, children exposed to 5 regular forms and 4 exceptions generalized, applying the regular form to 100% of novel test words. Children exposed to 3 regular forms and 6 exceptions did not extend the rule, even though the token frequency of the regular form was still high in this condition. The Tolerance Principle thus appears to capture a basic principle of generalization in rule formation.