We present a self-organizing approach to sentence processing that sheds new light on notional plurality effects in agreement attraction, using pseudopartitive subject noun phrases. We first show that notional plurality ratings predict verb agreement choices in pseudopartitives, in line with the “Marking” component of the Marking and Morphing theory of agreement processing. However, no account to date has derived notional plurality values from independently needed principles of language processing. We argue on the basis of new experimental evidence and a dynamical (...) systems model that the theoretical black box of notional plurality can be unpacked into objectively measurable semantic features. With these semantic features driving structure formation, our model reproduces the human verb production patterns as a byproduct of normal processing. Finally, we discuss how the self-organizing approach might be extended to other agreement attraction phenomena. (shrink)

Noam Chomsky applies a rational, scientific approach to disciplines as diverse as linguistics, ethics, and politics. His best-known innovations involve a groundbreaking theory of generative grammar, the revolution it initiated in cognitive science, and a radical encounter with political theory and practice. In _Chomsky Notebook_, Cedric Boeckx and Norbert Hornstein tackle the evolution of Chomsky's linguistic theory. Akeel Bilgrami revisits Chomsky's work on freedom and truth, and Pierre Jacob analyzes his naturalism. Chomsky's own contributions include an interview with Jean Bricmont (...) and an essay each on Edward Said and the natural world. Altogether, these works reveal the penetrating insight of a remarkable intellectual whose thought extends into a number of fields within and outside of academia. For the uninitiated reader and longtime fan, this anthology attests to the power of Chomsky's rationalism and the dexterity of his critical investigations. (shrink)

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 Fibonacci grammar in a serial reaction time task. This deterministic grammar generates aperiodic but self-similar sequences. The combination of these two properties allowed us to evaluate hierarchical learning while controlling (...) for the use of low-level strategies like detecting recurring patterns. The deterministic aspect of the grammar allowed us to predict precisely which points in the sequence should be subject to anticipation. Results showed that participants’ pattern of anticipation could not be accounted for by “flat” statistical learning processes and was consistent with them anticipating upcoming points based on hierarchical assumptions. We also found that participants were sensitive to the structure constituency, suggesting that they organized the signal into embedded constituents. We hypothesized that the participants built this structure by merging recursively deterministic transitions. (shrink)