Modeling Infant Learning via Symbolic Structural Alignment

摘要

Understanding the mechanisms of learning is one of the central questions of Cognitive Science. Recently Marcus et al. showed that seven-month-old infants can learn to recognize regularities in simple language-like stimuli. Marcus proposed that these results could not be modeled via existing connectionist systems, and that such learning requires infants to be constructing rules containing algebraic variables. This paper proposes a third possibility: that such learning can be explained via structural alignment processes operating over structured representations. We demonstrate the plausibility of this approach by describing a simulation, built out of previously tested models of symbolic similarity processing, that models the Marcus data. Unlike existing connectionist simulations, our model learns within the span of stimuli presented to the infants and does not require supervision. It can handle input with and without noise. Contrary to Marcus proposal, our model does not require the introduction of variables. It incrementally abstracts structural regularities, which do not need to be fully abstract rules for the phenomenon to appear. Our model also proposes a processing explanation for why infants attend longer to the novel stimuli. We describe our model and the simulation results and discuss the role of structural alignment in the development of abstract patterns and rules.