A central goal of modern generative grammar has been to discover invariant properties of human languages that reflect ''the innate schematism of mind that is applied to the data of experience'' and that ''might reasonably be attributed to the organism itself as its contribution to the task of the acquisition of knowledge '' (Chomsky, 1971). Candidates for such invariances include the structure dependence of grammatical rules, and in particular, certain constraints on question formation. Various ''poverty of stimulus'' (POS) arguments suggest that these invariances reflect an innate human endowment, as opposed to common experience: Such experience warrants selection of the grammars acquired only if humans assume, a priori, that selectable grammars respect substantive constraints. Recently, several researchers have tried to rebut these POS arguments. In response, we illustrate why POS arguments remain an important source of support for appeal to a priori structure-dependent constraints on the grammars that humans naturally acquire.

In their recent paper, Lau, Clark, and Lappin explore the idea that the probability of the occurrence of word strings can form the basis of an adequate theory of grammar (Lau, Jey H., Alexander Clark & 15 Shalom Lappin. 2017. Grammaticality, acceptability, and probability: A prob- abilistic view of linguistic knowledge. Cognitive Science 41(5):1201–1241). To make their case, they present the results of correlating the output of several probabilistic models trained solely on naturally occurring sentences with the gradient acceptability judgments that humans report for ungrammatical sentences derived from roundtrip machine translation errors. In this paper, we first explore the logic of the Lau et al. argument, both in terms of the choice of evaluation metric (gradient acceptability), and in the choice of test data set (machine translation errors on random sentences from a corpus). We then present our own series of studies intended to allow for a better comparison between LCL’s models and existing grammatical theories. We evaluate two of LCL’s probabilistic models (trigrams and recurrent neural network) against three data sets (taken from journal articles, a textbook, and Chomsky’s famous colorless-green-ideas sentence), using three evaluation metrics (LCL’s gradience metric, a categorical version of the metric, and the experimental-logic metric used in the syntax literature). Our results suggest there are very real, measurable cost-benefit tradeoffs inherent in LCL’s models across the three evaluation metrics. The gain in explanation of gradience (between 13% and 31% of gradience) is offset by losses in the other two metrics: a 43%-49% loss in coverage based on a categorical metric of explaining acceptability, and a loss of 12%-35% in explaining experimentally-defined phenomena. This suggests that anyone wishing to pursue LCL’s models as competitors with existing syntactic theories must either be satisfied with this tradeoff, or modify the models to capture the phenomena that are not currently captured.

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and 3), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.

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