|
Abstract:
I argue that quantificational NPs
(QNPs) trigger semantic predictions (distinct from syntactic
predictions) that help guide the construction of a sentence's
structure. I report a self-paced reading study supporting
(1):
(1) Quantifier Prediction Principle: When the
processor comes across a QNP, it makes a prediction that some
constituent denoting a set of individuals will function as the
argument of the QNP.
Referential NPs (type e) denote
individuals. QNPs (type <<e,t>,t>) denote
functions from sets of individuals to truth-values. QNPs can
take arguments, and, according to (1), the processor predicts such
arguments. This is illustrated in (2a) for "Every child
smiled". Q* stands in for the predicted argument. The
prediction is cashed out when the argument is processed, as in
(2b).
(2a) S: Ux [child(x)-->Q*(x)] (2b) S:
Ux[child(x)-->smiled(x)]
/ / \
NP: every child NP :every child
VP:smiled
/\ /\ /\
LQ Ux [child(x)--> Q(x)] LQ
Ux[child(x)-->Q(x)] Lx [smiled(x)]
Code: L = lambda (the functor corresponding to
lambda abstraction)
U = Universal Quantifier
To test (1), semantic predictions must be
differentiated from syntactic VP-predictions. [1]/[2]
observed that subjects do not detect missing VPs in multiple center
embedding constructions [MCECs] ((3b) is judged better than
(3a)). The VP prediction corresponding to the second NP seems
missing. I compared the effects of QNPs in different
positions in MCECs with missing VPs. Forty-eight participants
read sentences like (4) using a phrase-by-phrase self-paced moving
window technique. Since there is no syntactic VP prediction
corresponding to the second NP, (1) predicts that a missing VP will
be noted when the second NP is a QNP. The semantic prediction
triggered by the QNP will remain unsatisfied. This anomaly
will slow down reading times. No such prediction is made if
the second NP is a referential NP. The experimental results
confirmed this hypothesis and support (1). Mean reading times
for regions [d] and [e] were faster for (4c) than for (4b)
[1935.479 vs. 2147.542 and 1501.265 vs. 1732.563]. The
differences were almost significant by subjects (p = .066 and
.052), and fully significant by items (p = .017 and .022).
Reading times for (4a) were as slow as those for (4b), perhaps due
to similarity-based interference in working memory ([3]). To
conclude, I will discuss further implications of the hypothesis
that there are semantic predictions, and I will compare my analysis
with [4], who analyze the effects of QNPs in terms of a metric for
syntactic complexity.
| (3) |
a. |
The patient who the nurse
who the clinic hired admitted met the surgeon. |
|
b. |
The patient who the nurse
who the clinic hired met the surgeon. |
| (4) |
a. |
[a] The critic | [b] who the
artist | [c] who the gallery was promoting | [d] made unpleasant
remarks during the opening, | [e] and several people
complained. |
|
b. |
[a] The critic | [b] who
every artist | [c] who the gallery was promoting | [d] made
unpleasant remarks during the opening, | [e] and several people
complained. |
|
c. |
[a] Every critic | [b] who
the artist | [c] who the gallery was promoting | [d] made
unpleasant remarks during the opening, | [e] and several people
complained. |
References
[1] Frazier, Lyn, 1985. Syntactic
Complexity, in Natural Language Parsing, 129-189.
[2] Gibson, E., & J. Thomas,
1999. Memory Limitations and Structural Forgetting: LCP
14(3):225-48.
[3] Lewis, R, 2000. Specifying
Architectures for Language Processing, in AMLAP, 56-89
[4] Warren, T., & E. Gibson, 2001,
Ms. The influence of referential processing on sentence
complexity
|
