In just a few years, children achieve a stable state of linguistic competence, making them effectively adults with respect to: understanding novel sentences, discerning relations of paraphrase and entailment, acceptability judgments, etc. One familiar account of the language acquisition process treats it as an induction problem of the sort that arises in any domain where the knowledge achieved is logically underdetermined by experience. This view highlights the cues that are available in the input to children, as well as childrens skills (...) in extracting relevant information and forming generalizations on the basis of the data they receive. Nativists, on the other hand, contend that language-learners project beyond their experience in ways that the input does not even suggest. Instead of viewing language acqusition as a special case of theory induction, nativists posit a Universal Grammar, with innately specified linguistic principles of grammar formation. The nature versus nurture debate continues, as various poverty of stimulus arguments are challenged or supported by developments in linguistic theory and by findings from psycholinguistic investigations of child language. In light of some recent challenges to nativism, we rehearse old poverty-of stimulus arguments, and supplement them by drawing on more recent work in linguistic theory and studies of child language. (shrink)

A recent hypothesis in empirical brain research on language is that the fundamental difference between animal and human communication systems is captured by the distinction between finite-state and more complex phrase-structure grammars, such as context-free and context-sensitive grammars. However, the relevance of this distinction for the study of language as a neurobiological system has been questioned and it has been suggested that a more relevant and partly analogous distinction is that between non-adjacent and adjacent dependencies. Online memory resources are central (...) to the processing of non-adjacent dependencies as information has to be maintained across intervening material. One proposal is that an external memory device in the form of a limited push-down stack is used to process non-adjacent dependencies. We tested this hypothesis in an artificial grammar learning paradigm where subjects acquired non-adjacent dependencies implicitly. Generally, we found no qualitative differences between the acquisition of non-adjacent dependencies and adjacent dependencies. This suggests that although the acquisition of non-adjacent dependencies requires more exposure to the acquisition material, it utilizes the same mechanisms used for acquiring adjacent dependencies. We challenge the push-down stack model further by testing its processing predictions for nested and crossed multiple non-adjacent dependencies. The push-down stack model is partly supported by the results, and we suggest that stack-like properties are some among many natural properties characterizing the underlying neurophysiological mechanisms that implement the online memory resources used in language and structured sequence processing. (shrink)

Levelt, reviewing the AGL field from a psycholinguistic perspective, identifies various gaps and makes a number of concrete suggestions for improving several currently used experimental designs. He raises the question whether artificial (and natural) grammar learning is about detecting ‘rules’, as is commonly assumed, or rather the detection of a set of ‘constraints’. He cautions the community to not ignore ‘semantics’, and recommends to consider less artificial tasks, that may be needed for learning more complex rules by human or nonhuman (...) animals. (shrink)

Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of (...) memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks. (shrink)