STRUCTURED ACCESS IN SENTENCE COMPREHENSION Brian W. Dillon Doctor of Philosophy ...

364 Pages·2011·12.36 MB·English

by  Brian Dillon

ABSTRACT

Title of document: STRUCTURED ACCESS IN SENTENCE
COMPREHENSION

Brian W. Dillon
Doctor of Philosophy, 2011

Directed by: Professor Colin Phillips
Department of Linguistics

Abstract: This thesis is concerned with the nature of memory access during the
construction of long-distance dependencies in online sentence comprehension. In
recent years, an intense focus on the computational challenges posed by long-distance
dependencies has proven to be illuminating with respect to the characteristics of the
architecture of the human sentence processor, suggesting a tight link between general
memory access procedures and sentence processing routines (Lewis & Vasishth 2005;
Lewis, Vasishth, & Van Dyke 2006; Wagers, Lau & Phillips 2009). The present
thesis builds upon this line of research, and its primary aim is to motivate and defend
the hypothesis that the parser accesses linguistic memory in an essentially structured
fashion for certain long-distance dependencies. In order to make this case, I focus on
the processing of reflexive and agreement dependencies, and ask whether or not non-
structural information such as morphological features are used to gate memory access
during syntactic comprehension. Evidence from eight experiments in a range of
methodologies in English and Chinese is brought to bear on this question, providing
arguments from interference effects and time-course effects that primarily syntactic
information is used to access linguistic memory in the construction of certain long-
distance dependencies. The experimental evidence for structured access is compatible
with a variety of architectural assumptions about the parser, and I present one
implementation of this idea in a parser based on the ACT-R memory architecture. In
the context of such a content-addressable model of memory, the claim of structured
access is equivalent to the claim that only syntactic cues are used to query memory. I
argue that structured access reflects an optimal parsing strategy in the context of a
noisy, interference-prone cognitive architecture: abstract structural cues are favored
over lexical feature cues for certain structural dependencies in order to minimize
memory interference in online processing.
STRUCTURED ACCESS IN SENTENCE COMPREHENSION

by

Brian William Dillon

Dissertation submitted to the Faculty of the Graduate School of the
University of Maryland, College Park, in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
2011

Advisory Committee:
Professor Colin Phillips, Chair
Professor Norbert Hornstein
Professor William Idsardi
Professor Jeffrey Lidz
External: Professor Robert DeKeyser, SLA
© Copyright by
Brian William Dillon
2011
Acknowledgments

I’d like to thank first and foremost Colin Phillips for all the support he’s given me
over the last six years. Colin has spent a significant amount of time patiently listening
to me and my half-baked ideas week after week, helping me to sharpen those ideas
while simultaneously teaching me how to be a responsible and engaged scientist.
There’s no question that he has really been an all-around top-notch advisor. I’m still
puzzled as to why he thought I was qualified to run an EEG lab way back when, but
I’m thankful that he gave me the chance; I wouldn’t be where I am today if he hadn’t
thought so. My time at Maryland has been an extremely frustrating and extremely
rewarding experience that I wouldn’t trade for anything. For all his time and energy
that he’s given me over the years, I owe a great debt to him that I really can’t sum up
in a paragraph. So I’ll just leave it this: thank you, Colin!

I’ve also been very lucky to work with Bill Idsardi during my time at Maryland. I’m
thankful for his all-in support and encouragement in pursuing my research ideas, and
his incredibly diverse approach to research questions in cognitive science has been an
inspiration along the way. From the highest-level discussions of our work to the
minute details of hierarchical clustering, it seems there was nothing that I couldn’t
talk to Bill about, and he was always willing to lend an ear.

Jeff Lidz was also a huge help through the years. His excitement for language
research was an important source of encouragement in frustrating times. I benefitted
from his insight on too many occasions to count, and he was never too busy to find
the time to talk (or, if he was, he didn’t let on).

Of course, many thanks are due to Norbert Hornstein for his daily afternoon cookie
deliveries, but more importantly, I’m thankful for his many non-cookie related visits
to 1413 H. I’ve really enjoyed and learned a lot from our discussions over the years,
and I’m going to miss them.

So many people deserve thanks for the help and friendship they’ve given me along
the way. Thanks to Ming Xiang, who has been a good friend and colleague since the
beginning. Ming has a special talent for keeping things in perspective and it’s been
great to work with her over the years. Thanks to Matt Wagers, who I’ve learned a lot
from over the years, who very patiently taught me how to run SAT, and who’s been a
good friend to boot.

Thanks also to my good friends and classmates: Pedro Alcocer, Annie Gagliardi, and
Shannon Hoerner have helped me time and time again to relax and not take things so
seriously, and Alex Drummond, Dave Kush, and Terje Lohndal have given me many
impromptu syntax lessons over the years. Thanks to Ewan Dunbar for non-stop math
fun. Thanks to Wing Yee Chow for all her help and discussion over the years; a good
deal of the research in this thesis would not have been possible without her help.

ii
I also feel lucky to have been part of a phenomenal lab during my time at Maryland,
and I’m going to miss everyone from the UMD CNL lab, past and present. Thanks to
everyone, seriously.

The ideas presented in this thesis have benefitted from discussions with many, many
people. In particular I’d like to thank Rick Lewis and Shravan Vasishth, who have
both given me a lot of support and helpful feedback on this work. The computational
modeling in this paper would not have been possible without Rick’s guidance.
Additionally, I am grateful to Taomei Guo, who very generously provided me with
support for running the Chinese experiments reported here. Special thanks are also
due to a number of amazing researchers who have helped me develop ideas or given
me helpful guidance at several stages in this thesis: Rajesh Bhatt, Lyn Frazier, Roger
Levy, Brian McElree, Adrian Staub, and Amy Weinberg.

The research I report here was supported by a number of outstanding research
assistants who I’ve had a lot of fun working with. Many thanks to Peiyao Chen,
Fengqin Liu, Alan Mishler, Mike Shvartsman, Shayne Sloggett, and Angela Stanley.

Last but definitely not least: thank you, Jorge, for being my best friend throughout all
of this and for being so supportive of my choices over the years. I’m incredibly lucky
to have you in my life, and I hope to be so lucky for a long time to come.

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Table of Contents
Acknowledgments
  ii

List
 of
 Tables
  vii

List
 of
 Figures
  ix

Chapter
 1:
 Introduction
  1

Models
 of
 memory
 and
 syntactic
 representation
  9

Cue-­‐based
 parsing
 and
 the
 psycholinguistic
 enterprise
  15

Outline
 of
 the
 dissertation
  19

Chapter
 2:
 The
 argument
 from
 interference:
 English
 agreement
 and
 reflexives
  24

The
 argument
 from
 interference
  26

Partial-­‐match
 interference
 in
 subject-­‐verb
 agreement
  31

Lack
 of
 interference
 in
 reflexive
 dependencies
  40

Experiment
 1:
 Direct
 comparison
 of
 agreement
 and
 reflexives
  47

Participants
  49

Stimuli
  49

Offline
 judgments
  51

Procedure
  53

Data
 Analysis
  55

Results:
 Agreement
  58

Results:
 Reflexives
  61

Direct
 comparison
 of
 interference
 effect
  63

Discussion
 of
 Experiment
 1
  64

Experiment
 2:
 Agreement
 revisited
  68

Participants
  69

Materials
  70

Procedure
  70

Data
 Analysis
  71

Results
  72

Discussion
  76

Experiment
 3:
 Reflexives
 revisited
  80

Participants
  80

Materials
  81

Data
 Analysis
  81

Results
  81

Discussion
  85

Overview
 of
 Experiments
 1-­‐3
  87

Discussion
  90

Reflexive
 interpretation
  96

Attraction
 in
 reflexive
 production
  99

The
 difference
 between
 agreement
 and
 reflexives
  107

Conclusion
  113

Chapter
 3:
 Revisiting
 the
 interference
 argument:
 optimal
 information
 retrieval

  116

Rational
 memory
 access
  119

Implementing
 rational
 retrieval:
 the
 model
  124

Modeling
 reflexive
 and
 agreement
 dependencies
  129

The
 relation
 between
 model
 predictions
 and
 experimental
 findings
  129

iv
Modeling
 feature-­‐based
 and
 structured
 access
  133

The
 model
  135

Experiment
 4:
 comparison
 of
 interference
 effect
 for
 agreement
 and
 reflexives
  138

Experiment
 5:
 comparison
 of
 access
 strategies
 for
 reflexives
  146

Interim
 conclusions
  152

The
 predictions
 of
 rational
 memory
 access
 models
  153

Relation
 to
 previous
 work
  164

Conclusion
  173

Chapter
 4:
 Processing
 long-­‐distance
 reflexives
 in
 Mandarin
 Chinese
  175

Linking
 structured
 access
 and
 structured
 search
  178

The
 argument
 from
 time
 course
  180

Chinese
 long-­‐distance
 anaphors
  192

Experiment
 6:
 SAT
 Evidence
  200

Participants
  201

Materials
  202

Procedure
  205

Data
 Analysis
  207

Empirical
 Accuracy
 Analysis
  210

Model
 Selection
 Analysis
  211

Parameter
 Estimation
 Analysis
  213

Discussion
  222

Experiment
 7:
 ERP
 Evidence
  226

Participants
  231

Materials
  231

Procedure
  233

EEG
 Recording
  234

EEG
 Analysis
  234

Results:
 Behavioral
 Data
  236

Results:
 ERP
 Data
  237

Discussion
  239

General
 Discussion
  244

Locality
 bias
 in
 ziji
 dependencies
  246

Alternative
 accounts
 of
 the
 data
  250

Linguistic
 and
 discourse
 antecedents
 for
 ziji
  253

Conclusion
  255

Chapter
 5:
 Structured
 access
 across
 dependency
 types
  257

A
 puzzle
 for
 the
 hypothesis
 of
 structured
 access
 in
 comprehension
  260

Revisiting
 Chinese
 anaphors
  265

The
 scope
 of
 structured
 access:
 contrasting
 ziji
 &
 ta-­‐ziji
  270

Experiment
 8:
 Ziji
 and
 sub-­‐commanding
 antecedents
  276

Participants
  276

Stimuli
  276

Procedure
  278

Offline
 judgments
  279

Data
 Analysis
  280

Results
  282

Experiment
 9:
 Ta-­‐ziji
 and
 sub-­‐commanding
 antecedents
  283

Participants
  283

Stimuli
  284

v
Procedure
  285

Offline
 judgments
  285

Data
 Analysis
  287

Results
  287

Discussion
  289

Structured
 access
 as
 syntactic
 parsing
  294

Agreement
 as
 uninterpreted
 syntax
  301

Structured
 access
 as
 an
 optimal
 access
 strategy
  304

Structured
 access
 and
 Mandarin
 anaphors
  312

The
 footprint
 of
 structured
 access
  314

Conclusion
  320

Chapter
 6:
 Conclusion
  322

No
 consideration
 of
 illicit
 antecedents:
 Experiments
 1-­‐5
  323

No
 immediate
 access
 to
 distant
 but
 accessible
 antecedents:
 Experiments
 6-­‐9
  324

Interpreting
 interference
 effects
  325

Structured
 access
 and
 the
 architecture
 of
 comprehension
  326

Structured
 access
 as
 an
 optimal
 adaptation
  327

Blocking
 effects
  329

Negative
 constraints
  330

Conclusion
  332

Appendix
 A:
 Retrieval
 schedules
 for
 models
 in
 Chapter
 3
  333

Agreement
 conditions:
  333

Reflexive
 conditions:
  334

References
  335

vi
List of Tables

Table 2.1: Summary of agreement conditions in Experiment 1. Critical and spillover
regions included in the analysis are underlined. ....................................................... 50

Table 2.2: Summary of reflexive conditions in Experiment 1. Critical and spillover
regions included in the analysis are underlined. ....................................................... 50

Table 2.3: Mean judgments and standard error by subjects for Experiment 1 rating
study. Values are on a 7-point scale where 7 is perfectly acceptable, and 1 is
completely unacceptable. .......................................................................................... 52

Table 2.4: Table of means (in ms where applicable) for agreement conditions for first
pass, total time, and probability of regression. Standard error by participant is
shown in parentheses. ............................................................................................... 58

Table 2.5: Table of means (in ms where applicable) for reflexive conditions for first
pass, total time, and probability of regression. Standard error by participant is
shown in parentheses. ............................................................................................... 60

Table 2.6: Summary of fixed effects for best-fit models on agreement conditions at
the critical agreeing verb region, including t-values (z-values for first-pass
regression probability data). An asterisk (*) indicates significance at α = 0.05,
while a cross (†) indicates significance at α = 0.10. First-pass and total time
coefficients are in milliseconds. ................................................................................ 62

Table 2.7: Summary of fixed effects for best-fit models on reflexive conditions at the
critical reflexive region, including t-values (z-values for first-pass regression
probability data). An asterisk (*) indicates significance at α = 0.05, while a cross
(†) indicates significance at α = 0.10. First-pass and total time coefficients are in
milliseconds. ............................................................................................................. 63

Table 2.8: Summary of agreement conditions in Experiment 2. Regions included in
the analysis are underlined. ....................................................................................... 70

Table 2.9: Table of means (in ms where applicable) for Experiment 2, agreement
conditions with a singular head noun, for first pass, total time, and probability of
regression. Standard error by participant is shown in parentheses. .......................... 71

Table 2.10: Table of means (in ms where applicable) for Experiment 2, agreement
conditions with a plural head noun, for first pass, total time, and probability of
regression. Standard error by participant is shown in parentheses. .......................... 72

Table 2.11: Summary of fixed effects for best-fit models at the critical agreeing verb
region in Experiment 2, including t-values (z-values for first-pass regression
probability data). An asterisk (*) indicates significance at α = 0.05; a cross (†)
indicates significance at α = 0.10. First-pass and total time coefficients are in
milliseconds. ............................................................................................................. 74

Table 2.12: Summary of reflexive conditions in Experiment 3. Regions included in
the analysis are underlined. ....................................................................................... 81

Table 2.13: Table of means (in ms where applicable) for Experiment 3, reflexive
conditions with a singular head noun, for first pass, total time, and probability of
regression. Standard error by participant is shown in parentheses. .......................... 82

Table 2.14: Table of means (in ms where applicable) for Experiment 3, reflexive
conditions with a plural head noun, for first pass, total time, and probability of
regression. Standard error by participant is shown in parentheses. .......................... 84

vii