Publications

Abstract

Reading is a cultural invention that needs to recruit cortical infrastructure that was not designed for it (cultural recycling of cortical maps). In the case of reading both visual cortex and networks for speech processing are recruited. Here I discuss current views on the neurobiological underpinnings of spoken language that deviate in a number of ways from the classical Wernicke-Lichtheim-Geschwind model. More areas than Broca’s and Wernicke’s region are involved in language. Moreover, a division along the axis of language production and language comprehension does not seem to be warranted. Instead, for central aspects of language processing neural infrastructure is shared between production and comprehension. Arguments are presented in favor of a dynamic network view, in which the functionality of a region is co-determined by the network of regions in which it is embedded at particular moments in time. Finally, core regions of language processing need to interact with other networks (e.g. the attentional networks and the ToM network) to establish full functionality of language and communication. The consequences of this architecture for reading are discussed.

Abstract

Speakers of English habitually encode motion events using manner-of-motion verbs (e.g., spin, roll, slide) whereas Spanish speakers rely on path-of-motion verbs (e.g., enter, exit, approach). Here, we ask whether the language-specific verb representations used in encoding motion events induce different modes of “thinking-for-speaking” in Spanish–English bilinguals. That is, assuming that the verb encodes the most salient information in the clause, do bilinguals find the path of motion to be more salient than manner of motion if they had previously described the motion event using Spanish versus English? In our study, Spanish–English bilinguals described a set of target motion events in either English or Spanish and then participated in a nonlinguistic similarity judgment task in which they viewed the target motion events individually (e.g., a ball rolling into a cave) followed by two variants a “same-path” variant such as a ball sliding into a cave or a “same-manner” variant such as a ball rolling away from a cave). Participants had to select one of the two variants that they judged to be more similar to the target event: The event that shared the same path of motion as the target versus the one that shared the same manner of motion. Our findings show that bilingual speakers were more likely to classify two motion events as being similar if they shared the same path of motion and if they had previously described the target motion events in Spanish versus in English. Our study provides further evidence for the “thinking-for-speaking” hypothesis by demonstrating that bilingual speakers can flexibly shift between language-specific construals of the same event “on-the-fly.”

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