Ten years ago, a group of researchers investigating the processing of morphological information met in the south of France to discuss how morphology affects word recognition, perception and production from a cross-linguistic perspective. This special issue is the fourth volume to expose the results of this on-going research effort.

The volume begins with a comprehensive review of the nature of morphological priming, followed by a series of experimental papers that examine morphological processing in a variety of languages such as English, Danish, Dutch, Finnish, Chinese, and Spanish. The parallel monitoring of morphological processing in reading, speech perception and production, using a wide array of experimental methods such as masked priming, long-term priming, the monitoring of eye movements, and the recording of electrophysiological activity, provides converging evidence regarding the nature of morphemic representations in the various languages.

The cross-linguistic perspective that characterizes the research effort of the present volume, as well as the previous ones, is used to investigate whether there are qualitative differences in the principles of lexical organization and lexical processing in different alphabetic orthographies that arise from qualitative differences in morphological structure.

This special issue samples the state of the art in research that attempts to describe the functional units that intervene between low-level perceptual processes and access to whole-word representations in long-term memory during visual word recognition. The different articles in this special issue cover various candidates for such processing units, defined in terms of orthographic, phonological, or morphological information. The most obvious candidate in terms of orthographic information is the individual letter. One article examines the way in which a word's component letters are combined in the correct order during early orthographic processing. At a slightly higher level of representation, several articles provide a focus on the role of syllabic representations in the processing of polysyllabic words, and examine the extent to which such syllabic representations are orthographic or phonological in nature. One article provides evidence concerning the role of interfixes in the processing of compound words, thus addressing the issue of how morphological representations exert their influence on the word recognition process. Altogether, the papers included in this special issue report a series of challenging findings that cannot be ignored by current computational models of visual word. Evidence is provided in favour of more flexible orthographic coding schemes that are typically used in models of visual word recognition. The syllabic effects that are reported call for a syllabic level of representation that is absent in the vast majority of computational models, and the effects of paradigmatic analogy in processing morphologically complex words should help limit the possible ways of representing morphological information in the visual word recognition system.

This volume provides an overview of a relatively neglected branch of connectionism known as localist connectionism. The singling out of localist connectionism is motivated by the fact that some critical modeling strategies have been more readily applied in the development and testing of localist as opposed to distributed connectionist models (models using distributed hidden-unit representations and trained with a particular learning algorithm, typically back-propagation). One major theme emerging from this book is that localist connectionism currently provides an interesting means of evolving from verbal-boxological models of human cognition to computer-implemented algorithmic models. The other central messages conveyed are that the highly delicate issue of model testing, evaluation, and selection must be taken seriously, and that model-builders of the localist connectionist family have already shown exemplary steps in this direction.

Computational modeling has tremendously advanced our understanding of the processes involved in normal and impaired reading. While previous research has mainly focused on simulating reading aloud of monosyllabic words in English, the present special issue highlights some new directions in the field of word recognition and reading aloud. These new lines of research include the learning orthographic and phonological representations in both supervised and unsupervised networks, and the extension of existing models to multi-syllabic word processing both in English and in other languages, such as Italian, French and German. The special issue also covers hotly debated issues concerning the front-end of the reading process, the neural plausibility of current models of word recognition and naming, the viability of Bayesian approaches to understanding reading, as well as the long-standing opposition between rule-based and statistical learning. Finally, this special issue includes simulation work on novel benchmark phenomena, such as the effects of fast phonology, masked onset priming and syllabic neighbourhood. Altogether, the present special issue provides a critical analysis and synthesis of current computational models of reading and cutting edge research concerning the next generation of computational models of word recognition and reading aloud.

This volume provides an overview of a relatively neglected branch of connectionism known as localist connectionism. The singling out of localist connectionism is motivated by the fact that some critical modeling strategies have been more readily applied in the development and testing of localist as opposed to distributed connectionist models (models using distributed hidden-unit representations and trained with a particular learning algorithm, typically back-propagation). One major theme emerging from this book is that localist connectionism currently provides an interesting means of evolving from verbal-boxological models of human cognition to computer-implemented algorithmic models. The other central messages conveyed are that the highly delicate issue of model testing, evaluation, and selection must be taken seriously, and that model-builders of the localist connectionist family have already shown exemplary steps in this direction.

Ten years ago, a group of researchers investigating the processing of morphological information met in the south of France to discuss how morphology affects word recognition, perception and production from a cross-linguistic perspective. This special issue is the fourth volume to expose the results of this on-going research effort. The volume begins with a comprehensive review of the nature of morphological priming, followed by a series of experimental papers that examine morphological processing in a variety of languages such as English, Danish, Dutch, Finnish, Chinese, and Spanish. The parallel monitoring of morphological processing in reading, speech perception and production, using a wide array of experimental methods such as masked priming, long-term priming, the monitoring of eye movements, and the recording of electrophysiological activity, provides converging evidence regarding the nature of morphemic representations in the various languages. The cross-linguistic perspective that characterizes the research effort of the present volume, as well as the previous ones, is used to investigate whether there are qualitative differences in the principles of lexical organization and lexical processing in different alphabetic orthographies that arise from qualitative differences in morphological structure.

First Published in 2005. Routledge is an imprint of Taylor & Francis, an informa company.

This Special Issue is the third volume produced by a group of researchers who convene every two years to discuss the role of morphology in word recognition.

Computational modeling has tremendously advanced our understanding of the processes involved in normal and impaired reading. While previous research has mainly focused on simulating reading aloud of monosyllabic words in English, the present special issue highlights some new directions in the field of word recognition and reading aloud. These new lines of research include the learning orthographic and phonological representations in both supervised and unsupervised networks, and the extension of existing models to multi-syllabic word processing both in English and in other languages, such as Italian, French and German. The special issue also covers hotly debated issues concerning the front-end of the reading process, the neural plausibility of current models of word recognition and naming, the viability of Bayesian approaches to understanding reading, as well as the long-standing opposition between rule-based and statistical learning. Finally, this special issue includes simulation work on novel benchmark phenomena, such as the effects of fast phonology, masked onset priming and syllabic neighbourhood. Altogether, the present special issue provides a critical analysis and synthesis of current computational models of reading and cutting edge research concerning the next generation of computational models of word recognition and reading aloud.