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Second Language Processing and Linguistic Theory  

John Archibald

The distinction between representations and processes is central to most models of the cognitive science of language. Linguistic theory informs the types of representations assumed, and these representations are what are taken to be the targets of second language acquisition. Epistemologically, this is often taken to be knowledge, or knowledge-that. Techniques such as Grammaticality Judgment tasks are paradigmatic as we seek to gain insight into what a learner’s grammar looks like. Learners behave as if certain phonological, morphological, or syntactic strings (which may or may not be target-like) were well-formed. It is the task of the researcher to understand the nature of the knowledge that governs those well-formedness beliefs. Traditional accounts of processing, on the other hand, look to the real-time use of language, either in production or perception, and invoke discussions of skill or knowledge-how. A range of experimental psycholinguistic techniques have been used to assess these skills: self-paced reading, eye-tracking, ERPs, priming, lexical decision, AXB discrimination, and the like. Such online measures can show us how we “do” language when it comes to activities such as production or comprehension. There has long been a connection between linguistic theory and theories of processing as evidenced by the work of Berwick (The Grammatical Basis of Linguistic Performance). The task of the parser is to assign abstract structure to a phonological, morphological, or syntactic string; structure that does not come directly labeled in the acoustic input. Such processing studies as the Garden Path phenomenon have revealed that grammaticality and processability are distinct constructs. In some models, however, the distinction between grammar and processing is less distinct. Phillips says that “parsing is grammar,” while O’Grady builds an emergentist theory with no grammar, only processing. Bayesian models of acquisition, and indeed of knowledge, assume that the grammars we set up are governed by a principle of entropy, which governs other aspects of human behavior; knowledge and skill are combined. Exemplar models view the processing of the input as a storing of all phonetic detail that is in the environment, not storing abstract categories; the categories emerge via a process of comparing exemplars. Linguistic theory helps us to understand the processing of input to acquire new L2 representations, and the access of those representations in real time.


Neurolinguistic Approaches in Morphology  

Niels O. Schiller

Neurolinguistic approaches to morphology include the main theories of morphological representation and processing in the human mind, such as full-listing, full-parsing, and hybrid dual-route models, and how the experimental evidence that has been acquired to support these theories uses different neurolinguistic paradigms (visual and auditory priming, violation, long-lag priming, picture-word interference, etc.) and methods (electroencephalography [EEG]/event-related brain potential [ERP], functional magnetic resonance imaging [fMRI], neuropsychology, and so forth).


Japanese Psycholinguistics  

Mineharu Nakayama

The Japanese psycholinguistics research field is moving rapidly in many different directions as it includes various sub-linguistics fields (e.g., phonetics/phonology, syntax, semantics, pragmatics, discourse studies). Naturally, diverse studies have reported intriguing findings that shed light on our language mechanism. This article presents a brief overview of some of the notable early 21st century studies mainly from the language acquisition and processing perspectives. The topics are divided into various sections: the sound system, the script forms, reading and writing, morpho-syntactic studies, word and sentential meanings, and pragmatics and discourse studies sections. Studies on special populations are also mentioned. Studies on the Japanese sound system have advanced our understanding of L1 and L2 (first and second language) acquisition and processing. For instance, more evidence is provided that infants form adult-like phonological grammar by 14 months in L1, and disassociation of prosody is reported from one’s comprehension in L2. Various cognitive factors as well as L1 influence the L2 acquisition process. As the Japanese language users employ three script forms (hiragana, katakana, and kanji) in a single sentence, orthographic processing research reveal multiple pathways to process information and the influence of memory. Adult script decoding and lexical processing has been well studied and research data from special populations further helps us to understand our vision-to-language mapping mechanism. Morpho-syntactic and semantic studies include a long debate on the nativist (generative) and statistical learning approaches in L1 acquisition. In particular, inflectional morphology and quantificational scope interaction in L1 acquisition bring pros and cons of both approaches as a single approach. Investigating processing mechanisms means studying cognitive/perceptual devices. Relative clause processing has been well-discussed in Japanese because Japanese has a different word order (SOV) from English (SVO), allows unpronounced pronouns and pre-verbal word permutations, and has no relative clause marking at the verbal ending (i.e., morphologically the same as the matrix ending). Behavioral and neurolinguistic data increasingly support incremental processing like SVO languages and an expectancy-driven processor in our L1 brain. L2 processing, however, requires more study to uncover its mechanism, as the literature is scarce in both L2 English by Japanese speakers and L2 Japanese by non-Japanese speakers. Pragmatic and discourse processing is also an area that needs to be explored further. Despite the typological difference between English and Japanese, the studies cited here indicate that our acquisition and processing devices seem to adjust locally while maintaining the universal mechanism.


Connectionism in Linguistic Theory  

Xiaowei Zhao

Connectionism is an important theoretical framework for the study of human cognition and behavior. Also known as Parallel Distributed Processing (PDP) or Artificial Neural Networks (ANN), connectionism advocates that learning, representation, and processing of information in mind are parallel, distributed, and interactive in nature. It argues for the emergence of human cognition as the outcome of large networks of interactive processing units operating simultaneously. Inspired by findings from neural science and artificial intelligence, connectionism is a powerful computational tool, and it has had profound impact on many areas of research, including linguistics. Since the beginning of connectionism, many connectionist models have been developed to account for a wide range of important linguistic phenomena observed in monolingual research, such as speech perception, speech production, semantic representation, and early lexical development in children. Recently, the application of connectionism to bilingual research has also gathered momentum. Connectionist models are often precise in the specification of modeling parameters and flexible in the manipulation of relevant variables in the model to address relevant theoretical questions, therefore they can provide significant advantages in testing mechanisms underlying language processes.


Discriminative Learning and the Lexicon: NDL and LDL  

Yu-Ying Chuang and R. Harald Baayen

Naive discriminative learning (NDL) and linear discriminative learning (LDL) are simple computational algorithms for lexical learning and lexical processing. Both NDL and LDL assume that learning is discriminative, driven by prediction error, and that it is this error that calibrates the association strength between input and output representations. Both words’ forms and their meanings are represented by numeric vectors, and mappings between forms and meanings are set up. For comprehension, form vectors predict meaning vectors. For production, meaning vectors map onto form vectors. These mappings can be learned incrementally, approximating how children learn the words of their language. Alternatively, optimal mappings representing the end state of learning can be estimated. The NDL and LDL algorithms are incorporated in a computational theory of the mental lexicon, the ‘discriminative lexicon’. The model shows good performance both with respect to production and comprehension accuracy, and for predicting aspects of lexical processing, including morphological processing, across a wide range of experiments. Since, mathematically, NDL and LDL implement multivariate multiple regression, the ‘discriminative lexicon’ provides a cognitively motivated statistical modeling approach to lexical processing.