Connectionist Modeling of Experience-based Effects in Sentence ...

German Word Order
Chapter 3 Connectionist Modelling of Language Comprehension
Konieczny and Ruh (2003) ran simulations on German relative clauses using the model
parameters of MC02. The results are inconsistent with the empirical subject preference.
German ORCs clearly exhibit lower error rates on the embedded verb. In addition the
results do not show a frequency × regularity interaction. This is not so surprising
considering german word order properties. In English the regularity effect is attributed
to the shared SVO ordering of main clauses and SRCs that separate both structures from
the SOV ordered ORCs. On the other hand, in German while main clauses commonly
exhibit an SVO ordering as in English the order in SRCs and ORCs is SOV and OSV
respectively. In addition the free word order in German also allows an OVS structure
in main clauses. This makes four different possible word orders in German that are not
expected to give rise to a regularity preferring one of the two RC types. As pointed out
in 1.3.3 a canonicity account based on thematic orderings cannot make clear predictions
assuming SVO as the canonic ordering. On the other hand, an SOV-canonicity account
would make the correct predictions. However, to derive structural frequency-based SOV
regularity the structural scope of the model would have to be extended. Simple main
clauses do not provide the desired regular structures as they do in English. I suspect
that including sentential complements and other subordinate clauses exhibiting an SOV
pattern would result in the desired frequency × regularity interaction with an advantage
for SRCs. The exact reason for the actual advantage of ORCs over SRCs in the
current model will be discusses at the end of the next section, which is concerned in
more detail with the SRN’s structure-based learning in RC processing.
3.3.3 What is learned?
As laid out in the previous section the model by MacDonald and Christiansen (2002)
was criticized in many issues. It is not completely evident how much of the networks
prediction can be attributed to a frequency × regularity effect and what are merely
artifacts. A mere correlation between network experience and human reading span is
no sufficient evidence for an experience effect for human readers. Similarly, the effect of
structural regularity differences between SRCs and ORCs does not necessarily cause the
preference pattern for human readers. Also, assuming the conclusions drawn from the
mentioned correlations are correct, the question remains of what exactly the learning
effect in relative clauses is based upon. An exposure-based theory driven by structural
frequency is in the need to say something about the specific structural cues essential for
shaping the efficiency of RC processing.
Assessing Experience
Wells et al. (2009) designed an experiment that took the challenge of replicating the
effects of experience with human readers for having a basis to assess the accuracy of
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