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| Two experiments presented infants with artificial languages in which at least two generalizations were logically possible. The results demonstrate that infants made one of the two generalizations tested, the one which was most statistically consistent with the particular subset of the data they received. | Two experiments presented infants with artificial languages in which at least two generalizations were logically possible. The results demonstrate that infants made one of the two generalizations tested, the one which was most statistically consistent with the particular subset of the data they received. | ||
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| + | I will focus on the induction problem - the situation in which a subset of input | ||
| + | clearly has at least two formal descriptions. What does an infant learner exposed to such | ||
| + | input do? There are at least three possibilities: | ||
| + | embodied in the input and can generalize based on either one. A second possibility is that | ||
| + | being faced with evidence of two possible generalizations prevents the learner from | ||
| + | generalizing at all. Finally, and perhaps most interestingly, | ||
| + | of having discerned different formal descriptions for different subsets of the input, | ||
| + | depending on which description better accounts for that particular input. | ||
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| Infants in the **diagonal condition** were familiarized with a subset of the stimuli in which | Infants in the **diagonal condition** were familiarized with a subset of the stimuli in which | ||
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| is consistent with two possible interpretations: | is consistent with two possible interpretations: | ||
| different formal systems make no generalization at all. Or, infants generalize based on the | 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.4 | + | 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//. | ||
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| + | ---- | ||
| + | **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, | ||
| + | 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, | ||
| + | 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, | ||
| + | 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. | ||