Sirois, S., Buckingham, D., & Shultz, T. R. (2000). Artificial grammar learning by infants: An auto-associator perspective. Developmental Science, 4, 442-456.
This paper reviews a recent article suggesting infants use a system of algebraic rules to learn an artificial grammar (Marcus, Vijayan, Bandi Rao, & Vishton, 1999). In three reported experiments, infants exhibited increased responding to auditory strings that violated the pattern of elements they were habituated to. We argue that a perceptual interpretation is more parsimonious, as well as more consistent with a broad array of habituation data. We report successful neural network simulations that implement a lower-level interpretation and capture the empirical regularities reported by Marcus and colleagues (1999). The discussion puts the simulation results in the context of the broader debate about interpreting infant habituation. Other neural network models of habituation in general, and of the Marcus et al. (1999) task specifically, are discussed.
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