RESUMEN
Phylogeographic, population genetics and diversity analysis are crucial for local tea resource conservation and breeding programs. Lincang in Yunnan has been known as the possible place of domestication for tea worldwide, yet, its genetic makeup and unique Lincang origin are little understood. Here, we reported a large-scale whole-genome resequencing based population genomic analysis in eight main tea-producing areas of Lincang in Yunnan (1,350 accessions), and the first comprehensive map of tea genome variation in Lincang was constructed. Based on the population structure, tea sample in Lincang was divided into three subgroups, and inferred Xigui and Nahan Tea Mountain in Linxiang, Baiying Mountain Ancient Tea Garden in Yun, and Jinxiu Village of Xiaowan Town in Fengqing, which belong to the birthplace of the three subgroups, were all likely to be the origin center of Lincang tea. Meanwhile, the history population sizes analysis show that similar evolutionary patterns were observed for the three subgroups of Lincang. It also was observed that the hybrid among eight areas of Lincang was noticeable, resulting in insignificant genetic differentiation between geographical populations and low genetic diversity. The findings of this study clarified the genetic make-up and evolutionary traits of the local population of tea, which gave some insight into the development of Lincang tea.
RESUMEN
Vocabulary acquisition is a critical part of learning a new language. Yet, due to structural, historical, and individual variability associated with natural languages, isolating the impact of specific factors on word learning can be challenging. Artificial languages are versatile tools for addressing this problem, allowing researchers to systematically manipulate properties of the language and control for learners' past experiences. Here, we review how artificial languages have been used to study bilingual word learning, with a particular focus on the influences of language input (e.g., word properties) and language experience (e.g., bilingualism). We additionally discuss the advantages and limitations of artificial languages for bilingual research and suggest resources for researchers considering the use of artificial languages. Used and interpreted properly, artificial language studies can inform our understanding of a wide range of factors relevant to word learning.
RESUMEN
Language can influence cognition in domains as varied as temporal processing, spatial categorization, and color perception (Casasanto & Boroditsky, 2008; Levinson & Wilkins, 2006; Winawer et al., 2007). Here, we provide converging behavioral and neural evidence that bilingual experience can change semantic associations. In Experiment 1, Spanish- and English-speaking bilinguals rated semantically unrelated picture pairs (e.g., cloud-present) as significantly more related in meaning than English monolinguals. Experiment 2 demonstrated that bilinguals who were highly proficient in Spanish and English rated both semantically related (e.g., student-teacher) and unrelated picture pairs (e.g., wall-fruit) as more related than monolinguals and low-proficiency bilinguals. Experiment 3 added ERP measures to provide a more sensitive test of the bilingual effect on semantic ratings, which was assessed through the use of linguistic stimuli (related and unrelated words instead of pictures) and a different bilingual population (Korean-English bilinguals). Bilingualism was associated with a significantly smaller N400 effect (i.e., N400 for unrelated - related), suggesting that bilinguals processed related and unrelated pairs more similarly than monolinguals; this result was coupled with a non-significant behavioral trend of bilinguals judging unrelated words as more related than monolinguals did. Across the three experiments, results show that bilingual experience can influence perceived semantic associations. We propose that bilinguals' denser and more interconnected phonological, orthographic and lexical systems may change the links between semantic concepts. Such an account is consistent with connectionist models of language that allow for phonological and lexical influences on conceptual representations, with implications for models of bilingual language processing.