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- W89987582 abstract "Children learn words astonishingly skilfully. Even infants can reliably “fast map”novel category labels to their referents without feedback or supervision (Carey &Bartlett, 1978; Houston-Price, Plunkett, & Harris, 2005). Using both empirical andneural network modelling methods this thesis presents an examination of both the fastand slow mapping phases of children's early word learning in the context of object andaction categorisation. A series of empirical experiments investigates the relationshipbetween within-category perceptual variability on two-year-old children’s ability tolearn labels for novel categories of objects and actions. Results demonstrate thatvariability profoundly affects both noun and verb learning.A review paper situates empirical word learning research in the context of recentadvances in the application of computational models to developmental research. Datafrom the noun experiments are then simulated using a Dynamic Neural Field (DNF)model (see Spencer & Schoner, 2009), suggesting that children’s early object categoriescan emerge dynamically from simple label-referent associations strengthened over time.Novel predictions generated by the model are replicated empirically, providing proofof-concept for the use of DNF models in simulations of word learning, as wellemphasising the strong featural basis of early categorisation.The noun data are further explored using a connectionist architecture (Morse, deGreef, Belpaeme & Cangelosi, 2010) in a robotic system, providing the groundwork forfuture research in cognitive robotics. The implications of these different approaches tocognitive modelling are discussed, situating the current work firmly in the dynamicsystems tradition whilst emphasising the value of interdisciplinary research inmotivating novel research paradigms." @default.
- W89987582 created "2016-06-24" @default.
- W89987582 creator A5022622644 @default.
- W89987582 date "2012-01-01" @default.
- W89987582 modified "2023-09-25" @default.
- W89987582 title "An investigation of fast and slow mapping" @default.
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