Publications

Abstract

This Frontiers Special Issue will synthesize current findings on the cognitive neuroscience of metaphor, provide a forum for voicing novel perspectives, and promote new insights into the metaphorical brain.

Abstract

A neurobiological model of language is discussed that overcomes the shortcomings of the classical Wernicke-Lichtheim-Geschwind model. It is based on a subdivision of language processing into three components: Memory, Unification, and Control. The functional components as well as the neurobiological underpinnings of the model are discussed. In addition, the need for extension beyond the classical core regions for language is shown. Attentional networks as well as networks for inferential processing are crucial to realize language comprehension beyond single word processing and beyond decoding propositional content.

Abstract

Can studying left- and right-handers inform us about cognition? In this chapter, we give an overview of research showing that studying left- and right-handers is informative for understanding the way the brain is organized (i.e., lateralized), as there appear to be differences between left- and right-handers in this respect, but also on the behavioral level handedness studies can provide new insights. According to theories of embodied cognition, our body can influence cognition. Given that left- and right-handers use their bodies differently, this might reflect their performance on an array of cognitive tasks. Indeed, handedness can have an influence on, for instance, what side of space we judge as more positive, the way we gesture, how we remember things, and how we learn new words. Laterality research can, therefore, provide valuable information as to how we act and why

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