1,282 research outputs found
Reanalyzing language expectations: Native language knowledge modulates the sensitivity to intervening cues during anticipatory processing
Issue Online:21 September 2018We investigated how native language experience shapes anticipatory language processing. Two groups of bilinguals (either Spanish or Basque natives) performed a word matching task (WordMT) and a picture matching task (PictureMT). They indicated whether the stimuli they visually perceived matched with the noun they heard. Spanish noun endings were either diagnostic of the gender (transparent) or ambiguous (opaque). ERPs were time-locked to an intervening gender-marked determiner preceding the predicted noun. The determiner always gender agreed with the following noun but could also introduce a mismatching noun, so that it was not fully task diagnostic. Evoked brain activity time-locked to the determiner was considered as reflecting updating/reanalysis of the task-relevant preactivated representation. We focused on the timing of this effect by estimating the comparison between a gender-congruent and a gender-incongruent determiner. In the WordMT, both groups showed a late N400 effect. Crucially, only Basque natives displayed an earlier P200 effect for determiners preceding transparent nouns. In the PictureMT, both groups showed an early P200 effect for determiners preceding opaque nouns. The determiners of transparent nouns triggered a negative effect at similar to 430 ms in Spanish natives, but at similar to 550 ms in Basque natives. This pattern of results supports a "retracing hypothesis" according to which the neurocognitive system navigates through the intermediate (sublexical and lexical) linguistic representations available from previous processing to evaluate the need of an update in the linguistic expectation concerning a target lexical item.Spanish Ministry of Economy and Competitiveness (MINECO), Agencia Estatal de Investigación (AEI), Fondo Europeo de Desarrollo Regional (FEDER) (grant PSI2015‐65694‐P to N. M.), Spanish Ministry of Economy and Competitiveness “Severo Ochoa” Programme for Centres/Units of Excellence in R&D (grant SEV‐2015‐490
Robust Processing of Natural Language
Previous approaches to robustness in natural language processing usually
treat deviant input by relaxing grammatical constraints whenever a successful
analysis cannot be provided by ``normal'' means. This schema implies, that
error detection always comes prior to error handling, a behaviour which hardly
can compete with its human model, where many erroneous situations are treated
without even noticing them.
The paper analyses the necessary preconditions for achieving a higher degree
of robustness in natural language processing and suggests a quite different
approach based on a procedure for structural disambiguation. It not only offers
the possibility to cope with robustness issues in a more natural way but
eventually might be suited to accommodate quite different aspects of robust
behaviour within a single framework.Comment: 16 pages, LaTeX, uses pstricks.sty, pstricks.tex, pstricks.pro,
pst-node.sty, pst-node.tex, pst-node.pro. To appear in: Proc. KI-95, 19th
German Conference on Artificial Intelligence, Bielefeld (Germany), Lecture
Notes in Computer Science, Springer 199
Statistical model of human lexical category disambiguation
Research in Sentence Processing is concerned with discovering the mechanism by
which linguistic utterances are mapped onto meaningful representations within the
human mind. Models of the Human Sentence Processing Mechanism (HSPM) can
be divided into those in which such mapping is performed by a number of limited
modular processes and those in which there is a single interactive process. A further,
and increasingly important, distinction is between models which rely on innate
preferences to guide decision processes and those which make use of experiencebased
statistics.
In this context, the aims of the current thesis are two-fold:
• To argue that the correct architecture of the HSPM is both modular and
statistical - the Modular Statistical Hypothesis (MSH).
• To propose and provide empirical support for a position in which human
lexical category disambiguation occurs within a modular process, distinct
from syntactic parsing and guided by a statistical decision process.
Arguments are given for why a modular statistical architecture should be preferred
on both methodological and rational grounds. We then turn to the (often ignored)
problem of lexical category disambiguation and propose the existence of a presyntactic
Statistical Lexical Category Module (SLCM). A number of variants of the
SLCM are introduced. By empirically investigating this particular architecture we
also hope to provide support for the more general hypothesis - the MSH.
The SLCM has some interesting behavioural properties; the remainder of the thesis
empirically investigates whether these behaviours are observable in human sentence
processing. We first consider whether the results of existing studies might be
attributable to SLCM behaviour. Such evaluation provides support for an HSPM
architecture that includes this SLCM and allows us to determine which SLCM
variant is empirically most plausible. Predictions are made, using this variant, to
determine SLCM behaviour in the face of novel utterances; these predictions are then
tested using a self-paced reading paradigm. The results of this experimentation fully
support the inclusion of the SLCM in a model of the HSPM and are not compatible
with other existing models.
As the SLCM is a modular and statistical process, empirical evidence for the SLCM
also directly supports an HSPM architecture which is modular and statistical. We
therefore conclude that our results strongly support both the SLCM and the MSH.
However, more work is needed, both to produce further evidence and to define the
model further
Lingering misinterpretations of garden path sentences arise from competing syntactic representations
Recent work has suggested that readers 19 initial and incorrect interpretation of temporarily ambiguous ("garden path") sentences (e.g., Christianson, Hollingworth, Halliwell, & Ferreira, 2001) sometimes lingers even after attempts at reanalysis. These lingering effects have been attributed to incomplete reanalysis. In two eye tracking experiments, we distinguish between two types of incompleteness: the language comprehension system might not build a faithful syntactic structure, or it might not fully erase the structure built during an initial misparse. The first experiment used reflexive binding and the Gender Mismatch paradigm to show that a complete and faithful structure is built following processing of the garden-path. The second experiment used two-sentence texts to examine the extent to which the garden-path meaning from the first sentence interferes with reading of the second. Together, the results indicate that misinterpretation effects are attributable not to failure in building a proper structure, but rather to failure in cleaning up all remnants of earlier attempts to build that syntactic representation
The evolution of case grammar
There are few linguistic phenomena that have seduced linguists so skillfully as grammatical case has done. Ever since Panini (4th Century BC), case has claimed a central role in linguistic theory and continues to do so today. However, despite centuries worth of research, case has yet to reveal its most important secrets. This book offers breakthrough explanations for the understanding of case through agent-based experiments in cultural language evolution. The experiments demonstrate that case systems may emerge because they have a selective advantage for communication: they reduce the cognitive effort that listeners need for semantic interpretation, while at the same time limiting the cognitive resources required for doing so
The evolution of case grammar
There are few linguistic phenomena that have seduced linguists so skillfully as grammatical case has done. Ever since Panini (4th Century BC), case has claimed a central role in linguistic theory and continues to do so today. However, despite centuries worth of research, case has yet to reveal its most important secrets. This book offers breakthrough explanations for the understanding of case through agent-based experiments in cultural language evolution. The experiments demonstrate that case systems may emerge because they have a selective advantage for communication: they reduce the cognitive effort that listeners need for semantic interpretation, while at the same time limiting the cognitive resources required for doing so
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