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

Chapter 4 Two SRN Prediction Studies
The impact of structural regularity is rather disconfirmed by several studies finding a
subject advantage on the relativizer.
By changing the RC type proportions in favor of the SRC in simulation 2 the object
advantage decreased dramatically. The RC region showed a subject advantage after two
training epochs. Compared to human data this finding is also inconsistent. In empirical
studies, on the RC region only an object preference was found (Hsiao and Gibson, 2003;
Lin and Garnsey, 2007; Qiao and Forster, 2008). The assessment of frequency effects
in simulation 2 is to be understood as a tentative approach to account for the complex
interplay of statistical constraints that drive learning. Direct predictions for sentence
processing patterns may, however, not be justified. An SRN-based regularity test like
in simulation 1 is more or less straightforward as long as the structures in question are
clearly defined. But the structural choice may not reflect the regularity relations that
are really influencing skill in human readers. In order to gain more precise predictions,
further corpus inspections are necessary. For example, the exact proportion of RClike
structures or elided-subject clauses with respect to the whole corpus was neglected
during the present study but could potentially have influenced the results.
Note that the regularity pattern in Mandarin as revealed by the simulations is not
easily comparable with the English simulation. In English there were effects of difficulty
mainly occurring on the verbs. This is due to the number agreement between subject
and predicate. Notably, between the verb and its direct object no agreement is necessary.
This agreement pattern delivers as a side effect a sort of semantic information,
comparable to thematic roles. Therewith, agreement gives rise to a simulation of integration
difficulty effects, evolving from the need to relate verbs to their subject. I
hypothesize that these “integration effects” are the main reason for the nice fit by region
of human data. Mandarin, on the other hand, does not contain specific noun-verb
dependencies. In a sense, the network just needs to count nouns and verbs instead of
establishing pairwise relationships. Thus, the Mandarin-trained network is not required
to deal with the concept of a sentential subject. Consequently, no “integration difficulty”
comparable to English is expected. Of course, this is not comparable to human processing
of Mandarin. Predicates and their arguments are indeed involved in dependencies
like thematic roles and other semantic relationships. It is imaginable that the missing of
specific noun-verb relationships is the reason for the absent pattern match between the
Mandarin simulation and human data. An implementation of the missing dependencies
similar to the simplified English grammar seems straightforward to test that hypothesis.
The interpretability of the result of such a simulation would, however, be questionable.
A possible interpretation of the all-over contradictory simulation results with respect
to human data is that the effects observed here do in fact not reflect experience-relevant
regularities. Assuming on the other hand, that the simulation results are interpretable as
showing regularity properties that play a role in human sentence comprehension, there
are two possible interpretations of the results: a) Assuming regularity plays a role in the
extraction preference, the fact that the regularity effect on the ORC was very weak in
the simulations could be one of the reasons for the inconclusive empirical results. b) On
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