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.
PPAR-γ Agonists and Their Role in Primary Cicatricial Alopecia. PPAR Res. 2017;2017:2501248 Authors: Harnchoowong S, Suchonwanit P Abstract Peroxisome proliferator-activated receptor γ (PPAR-γ) is a ligand-activated nuclear receptor that regulates the transcription of various genes. PPAR-γ plays roles in lipid homeostasis, sebocyte maturation, and peroxisome biogenesis and has shown anti-inflammatory effects. PPAR-γ is highly expressed in human sebaceous glands. Disruption of PPAR-γ is believed to be one of the mechanisms of primary cicatricial alopecia (PCA) pathoge...
The president scored a 30 out of 30 on a cognitive assessment during his physical last week, his doctor told reporters
Conclusions: The hierarchical structure of the CART model provides a diagnostic algorithm linked with the risk of malignancy at every step of the procedure. It also provides guidance on the use of ancillary examinations as it goes by simple, human understandable rules.Acta Cytologica
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The risk for type 2 diabetes was reduced by almost half throughout their childbearing years for women who breastfed for 6 months or longer, a study found.Medscape Medical News
WASHINGTON (Reuters) - U.S. President Donald Trump passed a cognitive test and is in overall excellent health, but needs to lose 10 to 15 pounds (4.5 to 6.8 kg) by eating better and starting to exercise, the White House physician said on Tuesday.
CONCLUSION: Our findings do not support epicardial fat's beneficial effects on bone health, whereas general adiposity has an osteotropic effect. The association between EFT and BMC is through common genetic component factors. PMID: 29332861 [PubMed - as supplied by publisher]
CONCLUSIONS: Affected infants and toddlers in Fukushima suggested some growth disturbances and early adiposity rebound, which can cause obesity. The future growth of children affected by disasters should be followed carefully. PMID: 29332860 [PubMed - as supplied by publisher]
Los Angeles-based model Lauren Wasser, 29, lost her right leg in 2012 and had the other one amputated last week. 'Life is about to be so different, again!' she said in a post before the surgery.
In this study, eleven volunteers (aged 37-52) took six early-afternoon naps (30 minutes) in their occupational workplace, under two different conditions: control 'Naps' or 'Naps + RT' with a within-subjects design. Our results demonstrate that adding RT to naps changes sleep architecture, with a significant increase in the TST, mostly due to N2 sleep stage (and N3, to a lesser extent). Therefore, the deepening of short naps with RT involving hypnosis might be a successful non-pharmacological way to extend sleep duration and to deepen sleep in occupational settings. PMID: 29332862 [PubMed - as supplied by publisher]