Neurolinguistic accounts of sentence comprehension identify a network of relevant brain regions, but do not detail the information flowing through them. We investigate syntactic information. Does brain activity implicate a computation over hierarchical grammars or does it simply reflect linear order, as in a Markov chain? To address this question, we quantify the cognitive states implied by alternative parsing models. We compare processing-complexity predictions from these states against fMRI timecourses from regions that have been implicated in sentence comprehension. We find that hierarchical grammars independently predict timecourses from left anterior and posterior temporal lobe. Markov models are predictive in these regions and across a broader network that includes the inferior frontal gyrus. These results suggest that while linear effects are wide-spread across the language network, certain areas in the left temporal lobe deal with abstract, hierarchical syntactic representations.

A basic idea of the transformational tradition is that constituents move. More recently, there has been a trend towards the view that all features are lexical features. And in recent "minimalist" grammars, structure building operations are assumed to be feature driven. A simple grammar formalism with these properties is presented here and briefly explored. Grammars in this formalism can define languages that are not in the "mildly context sensitive" class defined by Vijay-Shanker and Weir (1994).

The goal of this paper is to give a precise, formal account of certain fundamental notions in minimalist syntax. Particular attention is given to the comparison of token‐based (multidominance) and chain‐based perspectives on Merge. After considering a version of Transfer that violates the No‐Tampering Condition (NTC), we sketch an alternative, NTC‐compliant version.

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Minimalist grammars (MGs) and multiple context-free grammars (MCFGs) are weakly equivalent in the sense that they define the same languages, a large mildly context-sensitive class that properly includes context-free languages. But in addition, for each MG, there is an MCFG which is strongly equivalent in the sense that it defines the same language with isomorphic derivations. However, the structure-building rules of MGs but not MCFGs are defined in a way that generalizes across categories. Consequently, MGs can be exponentially more succinct than their MCFG equivalents, and this difference shows in parsing models too. An incremental, top-down beam parser for MGs is defined here, sound and complete for all MGs, and hence also capable of parsing all MCFG languages. But since the parser represents its grammar transparently, the relative succinctness of MGs is again evident. Although the determinants of MG structure are narrowly and discretely defined, probabilistic influences from a much broader domain can influence even the earliest analytic steps, allowing frequency and context effects to come early and from almost anywhere, as expected in incremental models.Keywords: Grammar; Parsing; Minimalist grammar; Succinctness; Multiple context-free grammar A psychological model is not adequate if a response, any response really due to the mechanism being modeled, is simply not in the range of the model. But we compare two models that agree on the range of behaviors to be modeled; in fact, suppose their input/ output behaviors are provably identical. Then can there be a reason to prefer one over the other? Yes. It is a familiar fact that very different algorithms, with very different data structures, can compute exactly the same function. And in such cases, it can matter which one is implemented. Since recent mathematical work on grammars has established a wide range of equivalence results, comparisons of models that are in some relevant sense Correspondence should be sent to Edward. P. Stabler, UCLA Linguistics,