In this article, we explore the extraction of recursive nested structure in the processing of binary sequences. Our aim was to determine whether humans learn the higher-order regularities of a highly simplified input where only sequential-order information marks the hierarchical structure. To this end, we implemented a sequence generated by the…

Interpreting a seemingly simple function word like "or," "behind," or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network-based visual question…

It has been suggested that external and/or internal limitations paradoxically may lead to superior learning, that is, the concepts of "starting small" and "less is more" (Elman, 1993; Newport, 1990). In this paper, we explore the type of incremental ordering during training that might help learning, and what mechanism explains…

Natural language contains many examples of sound-symbolism, where the form of the word carries information about its meaning. Such systematicity is more prevalent in the words children acquire first, but arbitrariness dominates during later vocabulary development. Furthermore, systematicity appears to promote learning category distinctions, which…

Inflectional affixes expressing the same grammatical category (e.g., subject agreement) tend to appear in the same morphological position in the word. We hypothesize that this cross-linguistic tendency toward "category clustering" is at least partly the result of a learning bias, which facilitates the transmission of morphology from one…

How do speakers learn the social meaning of different linguistic variants, and what factors influence how likely a particular social-linguistic association is to be learned? It has been argued that the social meaning of more salient variants should be learned faster, and that learners' pre-existing experience of a variant will influence its…

Children acquiring languages with noun classes (grammatical gender) have ample statistical information available that characterizes the distribution of nouns into these classes, but their use of this information to classify novel nouns differs from the predictions made by an optimal Bayesian classifier. We use rational analysis to investigate the…

Across languages of the world, some grammatical patterns have been argued to be more common than expected by chance. These are sometimes referred to as (statistical) "language universals." One such universal is the correlation between constituent order freedom and the presence of a case system in a language. Here, we explore whether this…

This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space--a concept language of logical rules. This article compares the model predictions to human generalization judgments in several…

Challenges Smolensky's theory that human intuitive/nonconscious cognitive processes can only be accurately explained in terms of subsymbolic computations in artificial neural networks. Symbolic learning models of two cognitive tasks involving nonconscious acquisition of information are presented: learning production rules and artificial finite…