M. A. Just and P. A. Carpenter's (1992) capacity theory of comprehension posits a linguistic working memory functionally separated from the representation of linguistic knowledge. G. S. Waters and D. Caplan's (1996) critique of this approach retained the notion of a separate working memory. In this article, the authors present an alternative account motivated by a connectionist approach to language comprehension. In their view, processing capacity emerges from network architecture and experience and is not a primitive that can vary independently. Individual differences in comprehension do not stem from variations in a separate working memory capacity; instead they emerge from an interaction of biological factors and language experience. This alternative is argued to provide a superior account of comprehension results previously attributed to a separate working memory capacity.

It is widely assumed that human learning and the structure of human languages are intimately related. This relationship is frequently suggested to derive from a language-specific biological endowment, which encodes universal, but communicatively arbitrary, principles of language structure (a Universal Grammar or UG). How might such a UG have evolved? We argue that UG could not have arisen either by biological adaptation or non-adaptationist genetic processes, resulting in a logical problem of language evolution. Specifically, as the processes of language change are much more rapid than processes of genetic change, language constitutes a "moving target" both over time and across different human populations, and, hence, cannot provide a stable environment to which language genes could have adapted. We conclude that a biologically determined UG is not evolutionarily viable. Instead, the original motivation for UG--the mesh between learners and languages--arises because language has been shaped to fit the human brain, rather than vice versa. Following Darwin, we view language itself as a complex and interdependent "organism," which evolves under selectional pressures from human learning and processing mechanisms. That is, languages themselves are shaped by severe selectional pressure from each generation of language users and learners. This suggests that apparently arbitrary aspects of linguistic structure may result from general learning and processing biases deriving from the structure of thought processes, perceptuo-motor factors, cognitive limitations, and pragmatics.

The authors investigated the extent to which touch, vision, and audition mediate the processing of statistical regularities within sequential input. Few researchers have conducted rigorous comparisons across sensory modalities; in particular, the sense of touch has been virtually ignored. The current data reveal not only commonalities but also modality constraints affecting statistical learning across the senses. To be specific, the authors found that the auditory modality displayed a quantitative learning advantage compared with vision and touch. In addition, they discovered qualitative learning biases among the senses: Primarily, audition afforded better learning for the final part of input sequences. These findings are discussed in terms of whether statistical learning is likely to consist of a single, unitary mechanism or multiple, modality-constrained ones.

Many explanations of the difficulties associated with interpreting object relative clauses appeal to the demands that object relatives make on working memory. MacDonald and Christiansen (2002) pointed to variations in reading experience as a source of differences, arguing that the unique word order of object relatives makes their processing more difficult and more sensitive to the effects of previous experience than the processing of subject relatives. This hypothesis was tested in a largescale study manipulating reading experiences of adults over several weeks. The group receiving relative clause experience increased reading speeds for object relatives more than for subject relatives, whereas a control experience group did not. The reading time data were compared to performance of a computational model given different amounts of experience. The results support claims for experience-based individual differences and an important role for statistical learning in sentence comprehension processes. George Miller's (1956) landmark description of the nature of short term memory was a characterization of both its limits (7 ± 2 units) and the modulation of these limits through learning, in that the units were chunks, the size of which could change through a person's experience with the material being processed. In discussions of computational capacity since that time, different research paradigms have tended to vary in their attention to the claim of capacity limits vs. the claim that capacity changes through learning. For example, within adult sentence comprehension, many accounts have invoked capacity limits to explain people's difficulties in relative clause comprehension (e.g., Gibson, 1998;Just & Carpenter, 1992;Lewis, Vasishth & VanDyke, 2006). All of these accounts have noted that experience could affect processing abilities, but the focus in these accounts has been on showing how a characterization of capacity limits explains certain aspects of sentence comprehension

Networks of interconnected nodes have long played a key role in cognitive science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system--scale properties in contexts as diverse as the Internet, metabolic reactions or collaborations among scientists. Today, the inclusion of network theory into cognitive sciences, and the expansion of complex systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the cognitive sciences.

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Although statistical learning and language have been assumed to be intertwined, this theoretical presupposition has rarely been tested empirically. The present study investigates the relationship between statistical learning and language using a within-subject design embedded in an individual-differences framework. Participants were administered separate statistical learning tasks involving adjacent and nonadjacent dependencies, along with a language comprehension task and a battery of other measures assessing verbal working memory, short-term memory, vocabulary, reading experience, cognitive motivation, and fluid intelligence. Strong interrelationships were found among statistical learning, verbal working memory, and language comprehension. However, when the effects of all other factors were controlled for, performance on the two statistical learning tasks was the only predictor for comprehending relevant types of natural language sentences.