This paper presents an algorithm for learning the construction grammar of a language from a large corpus. This grammar induction algorithm has two goals: first, to show that construction grammars are learnable without highly specified innate structure; second, to develop a model of which units do or do not constitute constructions in a given dataset. The basic task of construction grammar induction is to identify the minimum set of constructions that represents the language in question with maximum descriptive adequacy. These constructions must (1) generalize across an unspecified number of units while (2) containing mixed levels of representation internally (e.g., both item-specific and schematized representations), and (3) allowing for unfilled and partially filled slots. Additionally, these constructions may (4) contain recursive structure within a given slot that needs to be reduced in order to produce a sufficiently schematic representation. In other words, these constructions are multi-length, multi-level, possibly discontinuous co-occurrences which generalize across internal recursive structures. These co-occurrences are modeled using frequency and the ΔP measure of association, expanded in novel ways to cover multi-unit sequences. This work provides important new evidence for the learnability of construction grammars as well as a tool for the automated corpus analysis of constructions.

This article argues that mirror neurons originate in sensorimotor associative learning and therefore a new approach is needed to investigate their functions. Mirror neurons were discovered about 20 years ago in the monkey brain, and there is now evidence that they are also present in the human brain. The intriguing feature of many mirror neurons is that they fire not only when the animal is performing an action, such as grasping an object using a power grip, but also when the animal passively observes a similar action performed by another agent. It is widely believed that mirror neurons are a genetic adaptation for action understanding; that they were designed by evolution to fulfill a specific socio-cognitive function. In contrast, we argue that mirror neurons are forged by domain-general processes of associative learning in the course of individual development, and, although they may have psychological functions, they do not necessarily have a specific evolutionary purpose or adaptive function. The evidence supporting this view shows that (1) mirror neurons do not consistently encode action “goals”; (2) the contingency- and context-sensitive nature of associative learning explains the full range of mirror neuron properties; (3) human infants receive enough sensorimotor experience to support associative learning of mirror neurons (“wealth of the stimulus”); and (4) mirror neurons can be changed in radical ways by sensorimotor training. The associative account implies that reliable information about the function of mirror neurons can be obtained only by research based on developmental history, system-level theory, and careful experimentation.