Skewing the evidence: The effect of input structure on child and adult learning of lexically based patterns in an artificial language
Publication date: August 2017 Source:Journal of Memory and Language, Volume 95 Author(s): Elizabeth Wonnacott, Helen Brown, Kate Nation Successful language acquisition requires both generalization and lexically based learning. Previous research suggests that this is achieved, at least in part, by tracking distributional statistics at and above the level of lexical items. We explored this learning using a semi-artificial language learning paradigm with 6-year-olds and adults, looking at learning of co-occurrence relationships between (meaningless) particles and English nouns. Both age groups showed stronger lexical learning (and less generalization) given “skewed” languages where a majority particle co-occurred with most nouns. In addition, adults, but not children, were affected by overall lexicality, showing weaker lexical learning (more generalization) when some input nouns were seen to alternate (i.e. occur with both particles). The results suggest that restricting generalization is affected by distributional statistics above the level of words/bigrams. Findings are discussed within the framework offered by models capturing generalization as rational inference, namely hierarchical-Bayesian and simplicity-based models.
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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...