Abstract
Abstract—Researchers in human language processing and
acquisition are making an increasing use of computational
models. Computer simulations provide a valuable platform to
reproduce hypothesised learning mechanisms that are otherwise
very difficult, if not impossible, to verify on human subjects.
However, computational models come with problems and risks. It
is difficult to (automatically) extract essential information about
the developing internal representations from a set of simulation
runs, and often researchers limit themselves to analysing learning
curves based on empirical recognition accuracy through time.
The associated risk is to erroneously deem a specific learning
behaviour as generalisable to human learners, while it could also
be a mere consequence (artifact) of the implementation of the
artificial learner or of the input coding scheme.
In this paper a set of simulation runs taken from the ACORNS
project is investigated. First a look ‘inside the box’ of the
learner is provided by employing novel quantitative methods
for analysing changing structures in large data sets. Then,
the obtained findings are discussed in the perspective of their
ecological validity in the field of child language acquisition.
