The Mental Lexicon - Volume 6, Issue 1, 2011

Although for many years a sharp distinction has been made in language research between rules and words — with primary interest on rules — this distinction is now blurred in many theories. If anything, the focus of attention has shifted in recent years in favor of words. Results from many different areas of language research suggest that the lexicon is representationally rich, that it is the source of much productive behavior, and that lexically specific information plays a critical and early role in the interpretation of grammatical structure. But how much information can or should be placed in the lexicon? This is the question I address here. I review a set of studies whose results indicate that event knowledge plays a significant role in early stages of sentence processing and structural analysis. This poses a conundrum for traditional views of the lexicon. Either the lexicon must be expanded to include factors that do not plausibly seem to belong there; or else virtually all information about word meaning is removed, leaving the lexicon impoverished. I suggest a third alternative, which provides a way to account for lexical knowledge without a mental lexicon.

A basic assumption of the lexical decision task is that a correct response to a word requires access to a corresponding mental representation of that word. However, systematic patterns of similarities and differences between words and nonwords can lead to an inherent bias for a particular response to a given stimulus. In this paper we introduce LD1NN, a simple algorithm based on one-nearest-neighbor classification that predicts the probability of a word response for each stimulus in an experiment by looking at the word/nonword probabilities of the most similar previously presented stimuli. Then, we apply LD1NN to the task of detecting differences between a set of words and different sets of matched nonwords. Finally, we show that the LD1NN word response probabilities are predictive of response times in three large lexical decision studies and that predicted biases for and against word responses corresponds with respectively faster and slower responses to words in the three studies.

Everything you wanted to know about eye movement monitoring but were afraid to ask: This is a primer for researchers new to the use of eye-tracking and particularly those with an interest in language production. It summarizes the early history of eye movement monitoring in language production research, briefly reviews the relationship between visual attention and eye movements, and details the practical concerns of collecting and analyzing gaze data. In particular, the paper discusses the features and functions of available eye-tracking software and hardware systems, as well as tools for automatically processing speech. We close with a review of dependent measures that have been derived from eye movement data as well as how they were used and interpreted.

In this article, I will give an overview of eye tracking studies on morphological processing since 2005 and a few earlier studies. An earlier survey article of Pollatsek and Hyönä (2006) covers almost all studies until then, but a number of interesting articles have been left undiscussed or were published after 2005. Before that, I will discuss (a) the advantages of studying morphological processing by means of eye tracking; (b) methodological issues related to eye movement experiments on morphological processing; (c) the dependent measures one can extract from the eye movement record and how they can be used in assessing the time course of morphological processing; (d) the boundary paradigm that has been used in morphological processing studies. I will argue that eye tracking should be used more often in morphological processing research, since it allows for studying morphologically complex words in a natural way and at the same time its rich data output allows for deeper levels of analyses than some other methods do.

The field of neuroimaging has experienced a tremendous boom due to technological advances in the last ten years and this is also reflected in the electroencephalography / event-related potentials (EEG/ERP) method. This contribution provides an overview of the main EEG/ERP hardware systems and software development currently on the market and the benefits of such technology for the study of language issues. We discuss the “added-value” such technology brings to the research of language and the possibilities of combining various neuroimaging technique with emphasis on the integration of EEG/ERP and functional magnetic resonance imaging (fMRI). Our contribution ends with a look at what we think may be the methodologies that drive the field forward in the not too distant future.

There is a new and growing interest in psycholinguistics in the mental representation of (not necessarily phrasal) multi-word sequences and in how knowledge of these sequences relates to word, phrase, and sentence knowledge. In this paper we summarize the evidence for the existence of distinct mental representations for these types of sequences. Studies of sentence processing, contextual ambiguity resolution, speech production and compound word processing provide indirect evidence for frequency effects for multi-word sequences. Recent studies of adult reading behavior have looked more directly at the effects of holistic frequency on reading performance. We end by considering the relevance of multi-word sequences to existing cognitive models of language and speculating on how they may impact on future models.