No abstract

In four experiments, participants were presented with nouns referring to entities that are associated with an up or down location (e.g., roof, root). The required response either was compatible with the referent location or was not (e.g., upward vs. downward movement after reading roof). Across experiments, we manipulated whether the experimental task required word reading or not, as well as whether the response involved a movement or was stationary. In all experiments, participants' responses were significantly faster in the compatible than in the incompatible condition. This strongly suggests that location information is automatically activated when nouns are being processed.

We investigated the question of whether comprehenders mentally simulate a described situation even when this situation is explicitly negated in the sentence. In two experiments, participants read negative sentences such as There was no eagle in the sky, and subsequently responded to pictures of mentioned entities in the context of a recognition task. Participants' responses following negative sentences were faster when the depicted entity matched rather than mismatched the negated situation. These results suggest that comprehenders simulate the negated situation when processing a negated sentence. The results thereby provide further support for the experiential-simulations view of language comprehension.

In this article, the R package LSAfun is presented. This package enables a variety of functions and computations based on Vector Semantic Models such as Latent Semantic Analysis (LSA) Landauer, Foltz and Laham (Discourse Processes 25:259-284, 1998), which are procedures to obtain a high-dimensional vector representation for words (and documents) from a text corpus. Such representations are thought to capture the semantic meaning of a word (or document) and allow for semantic similarity comparisons between words to be calculated as the cosine of the angle between their associated vectors. LSAfun uses precreated LSA spaces and provides functions for (a) Similarity Computations between words, word lists, and documents; (b) Neighborhood Computations, such as obtaining a word's or document's most similar words, (c) plotting such a neighborhood, as well as similarity structures for any word lists, in a two-or three-dimensional approximation using Multidimensional Scaling, (d) Applied Functions, such as computing the coherence of a text, answering multiple choice questions and producing generic text summaries; and (e) Composition Methods for obtaining vector representations for two-word phrases. The purpose of this package is to allow convenient access to computations based on LSA.

In 2 experiments, participants read narratives containing a color term that was mentioned either within the scope of an explicit negative or not, and with the described situation being such that the color was either present or not. Accessibility of the color term was measured by means of a probe-recognition task either 500 ms (Experiment 1) or 1,500 ms (Experiment 2) after participants read the sentence mentioning color. After the 500-ms delay, the accessibility of the color term was influenced by the structure of the sentence. After the 1,500-ms delay, the accessibility was influenced by the content of the described situation. These results are consistent with the view that comprehenders construct a linguistic representation of the text as well as a situation model in which only present properties are represented. An alternative account, according to which comprehenders only construct a perceptual simulation of the described situation, is discussed.