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 and
neural network modelling methods this thesis presents an examination of both the fast
and slow mapping phases of children's early word learning in the context of object and
action categorisation. A series of empirical experiments investigates the relationship
between within-category perceptual variability on two-year-old children’s ability to
learn labels for novel categories of objects and actions. Results demonstrate that
variability profoundly affects both noun and verb learning.
A review paper situates empirical word learning research in the context of recent
advances in the application of computational models to developmental research. Data
from the noun experiments are then simulated using a Dynamic Neural Field (DNF)
model (see Spencer & Schöner, 2009), suggesting that children’s early object categories
can 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 well
emphasising the strong featural basis of early categorisation.
The noun data are further explored using a connectionist architecture (Morse, de
Greef, Belpaeme & Cangelosi, 2010) in a robotic system, providing the groundwork for
future research in cognitive robotics. The implications of these different approaches to
cognitive modelling are discussed, situating the current work firmly in the dynamic
systems tradition whilst emphasising the value of interdisciplinary research in
motivating novel research paradigms