Publications

Abstract

Sentence production is the process we use to create language-specific sentences that convey particular meanings. In production, there are complex interactions between meaning, words, and syntax at different points in sentences. Computational models can make these interactions explicit and connectionist learning algorithms have been useful for building such models. Connectionist models use domaingeneral mechanisms to learn internal representations and these mechanisms can also explain evidence of long-term syntactic adaptation in adult speakers. This paper will review work showing that these models can generalize words in novel ways and learn typologically-different languages like English and Japanese. It will also present modeling work which shows that connectionist learning algorithms can account for complex sentence production in children and adult production phenomena like structural priming, heavy NP shift, and conceptual/lexical accessibility.

Abstract

Relative clauses are a syntactic device to create complex sentences and they make language structurally productive. Despite a considerable number of experimental studies, it is still largely unclear how children learn relative clauses and how these are processed in the language system. Researchers at the MPI for Psycholinguistics used a computational learning model to gain novel insights into these issues. The model explains the differential development of relative clauses in English as well as cross-linguistic differences

Abstract

In traditional theories of language comprehension, syntactic and semantic processing are inextricably linked. This assumption has been challenged by the ‘Semantic Illusion Effect’ found in studies using Event Related brain Potentials. Semantically anomalous sentences did not produce the expected increase in N400 amplitude but rather one in P600 amplitude. To explain these findings, complex models have been devised in which an independent semantic processing stream can arrive at a sentence interpretation that may differ from the interpretation prescribed by the syntactic structure of the sentence. We review five such multi-stream models and argue that they do not account for the full range of relevant results because they assume that the amplitude of the N400 indexes some form of semantic integration. Based on recent evidence we argue that N400 amplitude might reflect the retrieval of lexical information from memory. On this view, the absence of an N400-effect in Semantic Illusion sentences can be explained in terms of priming. Furthermore, we suggest that semantic integration, which has previously been linked to the N400 component, might be reflected in the P600 instead. When combined, these functional interpretations result in a single-stream account of language processing that can explain all of the Semantic Illusion data.

Abstract

Linguistic adaptation is a phenomenon where language representations change in response to linguistic input. Adaptation can occur on multiple linguistic levels such as phonology (tuning of phonotactic constraints), words (repetition priming), and syntax (structural priming). The persistent nature of these adaptations suggests that they may be a form of implicit learning and connectionist models have been developed which instantiate this hypothesis. Research on implicit learning, however, has also produced evidence that explicit chunk knowledge is involved in the performance of these tasks. In this review, we examine how these interacting implicit and explicit processes may change our understanding of language learning and processing.

Abstract

Relative clause processing depends on the grammatical role of the head noun in the subordinate clause. This has traditionally been explained in terms of cognitive limitations. We suggest that structure-related processing differences arise from differences in experience with these structures. We present a connectionist model which learns to produce utterances with relative clauses from exposure to message-sentence pairs. The model shows how various factors such as frequent subsequences, structural variations, and meaning conspire to create differences in the processing of these structures. The predictions of this learning-based account have been confirmed in behavioral studies with adults. This work shows that structural regularities that govern relative clause processing can be explained within a usage-based approach to recursion.

Abstract

Language acquisition involves learning nonadjacent dependencies that can obtain between words in a sentence. Several artificial grammar learning studies have shown that the ability of adults and children to detect dependencies between A and B in frames AXB is influenced by the amount of variation in the X element. This paper presents a model of statistical learning which displays similar behavior on this task and generalizes in a human-like way. The model was also used to predict human behavior for increased distance and more variation in dependencies. We compare our model-based approach with the standard invariance account of the variability effect.