The goals of this study were to analyze the growth and stability of vocabulary, mean length of the three longest utterances (MLLUw), and sentence complexity in European Portuguese-speaking children aged 1;4–2;6, to explore differences in growth as a function of personal and family-related variables, and to investigate the inter-relationships among the three language dimensions. Fifty-one European Portuguese-speaking toddlers were longitudinally assessed at 1;4, 1;9, 2;1, and 2;6, through parent reports. Exponential growth models best described acquisition patterns during this period, but the vocabulary growth accelerated across the full age-range, whereas the growth of grammar dimensions accelerated mainly after 1;9. High variability was observed in the scores, but the toddlers’ relative positions were mostly stable over time. Gender approached significance as a predictor of vocabulary growth. Maternal educational level did not predict the growth of any of the three language dimensions. Both vocabulary and MLLUw predicted sentence complexity.

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables, and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural “blackboard” architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. Perspectives and potential developments of the architectures are discussed.