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

Gender agreement attraction in Greek comprehension

This work explores gender agreement attraction in comprehension. Attraction occurs when an agreement error (such as, “the key to the cabinets are rusty”) goes unnoticed, leading to the illusion of grammaticality due to a mismatch between the value of the head and the value of a local intervening phase (attractor). According to retrieval accounts, these errors occur during cue retrieval from memory and predict illusions of grammaticality. Alternatively, representational accounts predict that the errors occur due to the faulty representation of certain features, thus, illusions of ungrammaticality are also expected. In four experiments we explore: (a) whether gender agreement attraction occurs in Greek and the strategy/-ies employed, (b) the role of the agreement target, (c) the timing of gender agreement attraction, (d) the role of phonological matching between the nominal inflectional morphemes of the attractor and the agreement target, and (e) participants’ sensitivity to agreement when there is no conflict from the attractor. In all four experiments, the grammaticality of the sentence and the attractor value (match or mismatch with the head) and also the phonological matching between the attractor and the agreement target in ungrammatical sentences were manipulated. The agreement target was either an adjectival predicate or an object-clitic and the gender value of the head was feminine or neuter. Attraction was found in all measures during the time-course of adjectival predicates (Experiment 1) and object-clitics (Experiment 2), and in timed (Experiment 3), and untimed (Experiment 4) judgments. Even more, both gender values showed attraction and the results mainly suggest that participants experience illusions of grammaticality, confirming retrieval accounts. Phonological matching did not modulate attraction in any of the experiments, suggesting that the similarity in the morphophonological realization between the agreement target and the attractor does not increase attraction. Furthermore, participants were sensitive to gender agreement violations in the absence of gender mismatch between the head and the attractor, suggesting that they respect agreement rules and have both neuter and feminine available in their feature content repertoire, although with some tendency in favor of neuter in feminine agreement contexts. The impact of these findings is discussed within the concept of attraction and sensitivity to agreement violations

DNA ANALYSIS USING GRAMMATICAL INFERENCE

An accurate language definition capable of distinguishing between coding and non-coding DNA has important applications and analytical significance to the field of computational biology. The method proposed here uses positive sample grammatical inference and statistical information to infer languages for coding DNA. An algorithm is proposed for the searching of an optimal subset of input sequences for the inference of regular grammars by optimizing a relevant accuracy metric. The algorithm does not guarantee the finding of the optimal subset; however, testing shows improvement in accuracy and performance over the basis algorithm. Testing shows that the accuracy of inferred languages for components of DNA are consistently accurate. By using the proposed algorithm languages are inferred for coding DNA with average conditional probability over 80%. This reveals that languages for components of DNA can be inferred and are useful independent of the process that created them. These languages can then be analyzed or used for other tasks in computational biology. To illustrate potential applications of regular grammars for DNA components, an inferred language for exon sequences is applied as post processing to Hidden Markov exon prediction to reduce the number of wrong exons detected and improve the specificity of the model significantly

The Conceptualization of Grammatical Number

The current study investigated the nature of the mental representation of grammatical number. We used methodology from Stanfield and Zwaan (2001) in an attempt to distinguish amodal from perceptual systems. Participants read a sentence that ended with either a singular or plural noun. After reading the sentence, they viewed a picture that matched or mismatched the number of the sentence final-noun. They then judged whether the referent in the picture had been in the sentence. Participants were slower when matching a singular lexical stimulus (e.g. apple) to a plural graphic stimulus (See Figure 2) compared to the other three conditions. The results did not follow the pattern found by Stanfield & Zwaan (2001). The results are more consistent with logical entailment

Principles and Implementation of Deductive Parsing

We present a system for generating parsers based directly on the metaphor of parsing as deduction. Parsing algorithms can be represented directly as deduction systems, and a single deduction engine can interpret such deduction systems so as to implement the corresponding parser. The method generalizes easily to parsers for augmented phrase structure formalisms, such as definite-clause grammars and other logic grammar formalisms, and has been used for rapid prototyping of parsing algorithms for a variety of formalisms including variants of tree-adjoining grammars, categorial grammars, and lexicalized context-free grammars.Comment: 69 pages, includes full Prolog cod

Dutch-Indonesian Interlanguage Psycholinguistic Study on Syntax

This article focuses on the psycholinguistic study of the syntactic aspects of Dutch-Indonesian interlanguage. The study is based on the interlanguage syntax observed in an oral test given to thirty Indonesian learners of Dutch as a second language, whose purpose is to test the processability theory of Pienemann (2005a, b, c, 2007). The results of the study provide evidence for the validity of Pienemann's theory. Learners who have acquired sentences with the highest level of processing will also already have acquired sentences with a lower level of processing. The results from learners with a high level of Dutch proficiency verify the processability theory with more certainty than the results of learners with a lower proficiency. Learners tend to rely on meaning if they are not confident of their grammatical proficiency. Interlanguage is the result of the immediate need to encode in the mind concepts and ideas into the form of linguistic items, within a fraction of a millisecond, whilst the supporting means are limited, and whilst learners already have acquired a first language and possibly another language as well

Two approaches to automatic matching of atomic grammatical features in LFG

The alignment of a bilingual corpus is an important step in data preparation for data-driven machine translation. LFG f-structures provide bilexical labelled dependencies in the form of lemmas and core grammatical functions linking those lemmas, but also important grammatical features (TENSE, NUMBER, CASE, etc.) representing morphological and semantic information. These grammatical features can often be translated independently from the lemmas or words. It is therefore of practical interest to develop methods that align grammatical features which can be considered translations of each other (e.g. the number features of the corresponding words in the source and target parts of the corpus) in data-driven LFG-based MT. In a parallel grammar development scenario, such as ParGram, this is to a large extent captured through manually hardcoding the correspondences in the hand-crafted grammars, using similar or identical feature names for similar phenomena across languages. However, for a completely automatic learning method it is desirable to establish these correspondences without human assistance. In this paper we present and evaluate two approaches to the automatic identification of correspondences between atomic features of LFG (and similar) grammars for different languages. The methods can be used to evaluate the correspondence between feature names in hand-crafted parallel grammars or find correspondences between features in grammars for different languages where feature alignments are not known