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

4.5 Conclusion
of the VP-predicting NP in the representational cycle of the SRN decays faster. This in
turn is compensated by increased training on center-embedding compared to the English
simulation, resulting in comparable error values when trained without commas. This
is, of course, an ad-hoc hypothesis and needs further investigation, which is beyond the
scope of this thesis.
4.5 Conclusion
This thesis investigated the explanatory power of a certain implementation of the experience
account. The well approved SRN modeling approach of MacDonald and Christiansen
(2002) was adopted to test its predictions on two phenomena currently discussed
in literature. The RC extraction type preference in Mandarin and the forgetting effect
in complex center-embedding was discussed and then modeled. At first, the two problems
were approached theoretically, reviewing results of empirical studies and discussing
potential predictions of available theories. Concerning the Mandarin relative clauses,
the studies showed exceptionally mixed results. However, an observed object advantage
appeared always on the RC region, whereas a subject advantage was found only on the
relativizer/head noun region. That fact and the experiment by Qiao and Forster (2008)
are suggesting that Chinese Mandarin might have to be counted as an exception to a
universal subject preference. On the other hand, the results for the forgetting effect were
very clear and best explained by language-specific experience.
In chapter 3 the simple recurrent network was introduced and its properties were
discussed. An SRN is a very simple and domain-unspecific model, but it accounts for
the three necessities introduced in the beginning of this thesis: a) biological factors
(architectural limits), b) continuous gradedness in performance, and c) experience.
In chapter 4 the experience theory predictions regarding the two sample problems were
practically assessed. Just like in the discussion and critique of MC02, the simulation
results presented here looked promising on first sight; but sub-experiments and detailed
data analysis revealed considerable inconsistencies with respect to human data. The
Mandarin RC simulation predicted an object preference, but the location of the effect
was not consistent with human data. In addition, the second simulation demonstrated
that the regularity effect was very weak. It becomes clear that the training material used
must be carefully chosen in order to guarantee comparability with other simulations and
empirical studies. The forgetting effect was predicted to be present in English but not in
German, consistent with human data. However, further simulations revealed the comma
insertion as the most important factor.
Of course, it has to be clear that the simple network trained on a simple grammar
would not learn the same constraints as humans do. These simulations are rather approximations
pointing to a certain direction. A noticeable problem of the SRN predictions
is their dependency on local coherence, which can also be described as a low memory
span. This is, however, mainly dependent upon the specific properties of the learning
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