Speakers frequently have a choice among multiple ways of expressing one and the same thought. When choosing between syntactic constructions for expressing a given meaning, speakers are sensitive to probabilistic tendencies for syntactic, semantic or contextual properties of an utterance to favor one construction or another. Taken together, such tendencies may align to make one construction overwhelmingly more probable, marginally more probable, or no more probable than another. Here, we present evidence that acoustic features of spontaneous speech reflect these probabilities: when speakers choose a less probable construction, they are more likely to be disfluent, and their fluent words are likely to have a relatively longer duration. Conversely, words in more probable constructions are shorter and spoken more fluently. Our findings suggest that the differing probabilities of a syntactic construction in context are not epiphenomenal, but reflect a part of a speakers' knowledge of their language.

The kinds of entities that can be described as causing an event depend, in part, on the language one speaks. Whereas in English and Chinese it is possible to say The knife cut the bread or The key opened the door, in Korean and many other languages, such sentences sound very odd. According to the initiator hypothesis, languages fall into two major groups with respect to possible external arguments in causal expressions: those that require that the causer be capable of generating its own energy and those that require only that the causer participate in the causal chain leading up to a particular result. In support of this hypothesis, we show that ability to self-energize has a larger impact on acceptability ratings in Korean than in either English or Chinese (Exp. 1). We also show that restrictions on possible causers extend to the semantics of possible causes in the descriptions of animations depicting causal chains (Exp. 2). Finally, we show that cross-linguistic differences in the linguistic coding of causers may have consequences for the way people conceptualize animations of causal chains in terms of number of events (Exp. 3). Implications for the representation of verb meaning and the semantics of external arguments in other languages are discussed.

The ontological distinction between discrete individuated objects and continuous substances, and the way this distinction is expressed in different languages has been a fertile area for examining the relation between language and thought. In this paper we combine simulations and a cross-linguistic word learning task as a way to gain insight into the nature of the learning mechanisms involved in word learning. First, we look at the effect of the different correlational structures on novel generalizations with two kinds of learning tasks implemented in neural networks—prediction and correlation. Second, we look at English- and Spanish-speaking 2-3-year-olds' novel noun generalizations, and find that count/mass syntax has a stronger effect on Spanish- than on English-speaking children's novel noun generalizations, consistent with the predicting networks. The results suggest that it is not just the correlational structure of different linguistic cues that will determine how they are learned, but the specific learning mechanism and task in which they are involved.

We present an account of semantic representation that focuses on distinct types of information from which word meanings can be learned. In particular, we argue that there are at least two major types of information from which we learn word meanings. The first is what we call experiential information. This is data derived both from our sensory-motor interactions with the outside world, as well as from our experience of own inner states, particularly our emotions. The second type of information is language-based. In particular, it is derived from the general linguistic context in which words appear. The paper spells out this proposal, summarizes research supporting this view and presents new predictions emerging from this framework.

This review discusses the contributions of functional imaging (fMRI/PET) to our understanding of how semantic concepts are represented and processed in the human brain. The sensory-motor theory of semantic memory suggests that semantic processing relies on reactivation of sensory-motor representations that were involved in perception and action. More specifically, it attributes an apparent category-specific (e.g. tool vs. animals) organization of semantics to anatomical segregation for different semantic features (e.g. action vs. visual). Within this framework, we will review functional imaging evidence that semantic processing of tools and actions may rely on activations within the visuo-motor system.