Distributional learning of subcategories in an artificial grammar: Category generalization and subcategory restrictions
Publication date: December 2017 Source:Journal of Memory and Language, Volume 97 Author(s): Patricia A. Reeder, Elissa L. Newport, Richard N. Aslin There has been significant recent interest in clarifying how learners use distributional information during language acquisition. Many researchers have suggested that distributional learning mechanisms play a major role during grammatical category acquisition, since linguistic form-classes (like noun and verb) and subclasses (like masculine and feminine grammatical gender) are primarily defined by the ways lexical items are distributed in syntactic contexts. Though recent experimental work has affirmed the importance of distributional information for category acquisition, there has been little evidence that learners can acquire linguistic subclasses based only on distributional cues. Across two artificial grammar-learning experiments, we demonstrate that subclasses can be acquired from distributional cues alone. These results add to a body of work demonstrating rational use of distributional information to acquire complex linguistic structures.
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ConclusionsA MEL‐A2 with novel composition and surface activities was efficiently produced from a novel MEL producer. This is the first report on production of MEL‐A2 as the major product and from soybean oil. The biosurfactant has potential application as a wetting agent and oil‐in‐water emulsifier. Significance and Impact of the StudyDiscovery of novel structures and novel strains is valuable for further commercial development and application of MELs. Sporisorium sp. aff. sorghi SAM20 can be considered as a potential candidate for commercial production of biosurfactants.This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved. The phosphodiesterase VieA regulates gene expression by modifying the intracellular cyclic diguanylate pool. This article reveals the differential regulation of VieA in Vibrio cholerae O1 biotypes. Expression of VieA is repressed by the nucleoid‐associated protein H‐NS and the LysR‐type regulator LeuO in classical biotype V. cholerae, and by H‐NS and the quorum sensing regulator HapR in the El Tor biotype.
We describe approaches for back‐calculating model parameter estimates and their standard errors from available summary statistics with various techniques, including approximate Bayesian computation. We propose to use a quadratic approximation to the log‐likelihood for each historical trial based on 2 independent terms for the log mean rate and the log of the dispersion parameter. A Bayesian hierarchical meta‐analysis model then provides the posterior predictive distribution for these parameters. Simulations show this approach with back‐calculated parameter estimates results in very similar inference as using parame...