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 different formal systems make no generalization at all. Or, infants generalize based on the
 formal description that is more likely to have generated the input.
+\\
+These data, coupled with infants’ failure to discriminate under the same
+familiarization conditions in Exp. 1, suggest that infants in the column condition made
+only the generalization involving the position of the syllable //di//.
+\\
+----
+**Conclusions:**
+\\
+A question raised by the experiments
+is **what caused infants in the column condition to generalize based on the location of
+//di// rather than making the more abstract generalization**?
+\\
+One possibility is that the data are consistent with the **Subset Principle** (Manzini &
+Wexler, 1987), in which learners select among possible parameter values based on which
+value generates the smallest language compatible with the input data. Note that, if we
+interpret ‘language’ to mean ‘set of sentences,’ a learner would need to generate all of the
+sentences for each parameter value and determine which value generated fewer sentences
+(but see Wexler, 1993). Wexler and Manzini reduce the computational task for the learner
+by placing relevant parameters in a markedness hierarchy, in which the learner begins with
+the least marked value, which generates the smallest language.
+\\
+Another possibility is consistent with the **Bayesian approaches** to generalization
+(e.g., Tenenbaum & Griffiths, 2001), in which learners compare the subset of the input
+they have received to the range of input generated by different formal descriptions. For
+example, an infant might tacitly compute that it is extremely unlikely, given an AAB
+grammar, the only input ends in di. Depending on its implementation, this approach might
+also be computationally challenging. However, it has the advantage of applying to a more
+general (e.g., non-parameterized) learning problems, and it allows increasing confidence
+in hypothesis selection with increasing input set size. Importantly, this solution to the
+induction problem entails learners choosing among formal descriptions that they have
+already generated from the data using general purpose mechanisms (Saffran, Reeck,
+Niebuhr, & Wilson, 2005) or that are part of their innate endowment for language
+(e.g. Valian, 1990). For example, Saffran et al. (2005) demonstrated that the structure of
+the input determines the primitives (in this case absolute vs. relative pitch) over which
+generalizations are made. This type of research, in which learners ‘choose’ among
+different generalizations allowed by input data, may ultimately allow us to distinguish
+between theories of language development.