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

Chapter 2 Issues in Relative Clause Processing
According to Gibson and Thomas, exceeding a theoretic memory capacity limit by excessive
load causes a loss of costly predictions. A successful parse is possible as long as
memory demands throughout the sentence stay within a certain capacity range. However,
when high complexity causes the load to exceed the limit, a breakdown of the
parser has to be prevented by pruning activation. In the sense of the discrete nature of
SPLT this means that the prediction of certain syntactic categories have to be dropped.
The pruning hypothesis assumes that the predictions to be forgotten are those causing
the biggest part of SPLT memory cost at the current point in the sentence. In example
(17) the point of highest memory cost is the deepest embedded subject the clinic (NP3).
At this point two predictions are held in memory: VP2 predicted by NP2 and VP3
predicted by NP3. Since VP2 is further up in the sentence and has to be held longer
in memory than the successive VP3, it causes more memory cost. Consequently, the
prediction of the second VP gets pruned and therewith forgotten.
(17) a. [The patient]NP1 whoi [the nurse]NP2 whoj [the clinic]NP3 [had hired ej ]VP3
[admitted ei]VP2 [met Jack]VP1 .
b. * [The patient]NP1 whoi [the nurse]NP2 whoj [the clinic]NP3 [had hired ej ]VP3
[met Jack]VP1 .
Vasishth et al. (2008) restate the pruning hypothesis in terms of decay as defined in
the DLT (Gibson, 2000) and refer to it as the VP-forgetting Hypothesis. Vasishth et al.
calculate Integration and Storage Cost at the three VPs to determine the “point of greatest
difficulty” in the sentence. The DLT cost predictions for example (17) are illustrated
in figure 2.5. At the first VP (VP3) two integrations take place. The object the nurse
with two intervening discourse referents (clinic and hired) and the subject the clinic with
one intervening discourse referent (hired) are integrated. At this moment there are two
active predictions held in memory: the predicate of the upper RC (admitted), caused
by reading nurse, and the main verb. This makes a total cost of 4. At the second verb
(admitted) the object the patient and the subject the nurse are integrated. The patient
has a distance of four discourse referents (nurse, clinic, hired, and admitted) from the
verb, the object nurse is separated by two, and just the matrix verb is predicted. This
makes a total memory cost of 8 at the VP2 site. Finally, on the third VP, by integrating
the patient and predicting a direct object, a cost of 6 is gained. Concluding from the
calculations, VP2 has the highest memory cost and, hence, is forgotten.
The difference between Vasishth et al.’s and Gibson and Thomas’ account is that
the latter added Storage Cost on the noun and Integration Cost of the predicted verb,
whereas Vasishth et al. just use the total cost on the verb. The predictions, however, are
the same. Let me try to reformulate the decay approach more intuitively. The important
measure of the decay approach is Integration Cost. By counting the number of intervening
discourse referents it is a discrete indirect measure of time. Or, as Vasishth et al. put
it: it is “a discretized abstraction over some activation decay function that determines
the strength of a memorial representation.” Hence, decay could be described as a function
of time and intervening memory load with the assumption that a high memory load
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