Does high-level intellectual performance depend on practice alone? Debunking the Polgar sisters case

The famous Polgar sisters started chess very young, undertook extensive study, and two became grandmasters. This case often is cited as decisive evidence that practice alone is key in development of expertise, that innate talent is unimportant or non-existent, and that almost anyone can become a grandmaster. But on close examination these claims do not hold up. Performance differences among the sisters cannot be accounted for by practice differences. Furthermore, two sisters reached peak performance levels comparable to those of many far less-practiced players entering the international domain around the same time and only one sister ever reached the top ten. Contrary to a popular view, the case does not show that becoming a grandmaster requires only practice and raises innate talent as a plausible contributor.     

口语加工中的词语切分线索

词是语言的基本结构单位, 对词语进行切分是语言加工的重要步骤。口语语流中的切分线索来自于语音、语义和语法三个方面。语音线索包括概率信息、音位配列规则和韵律信息, 韵律信息中还包括词重音、时长和音高等内容, 这些线索的使用在接触语言的早期阶段就逐渐被个体所掌握, 而且在不同的语言背景下有一定的特异性。语法和语义线索属于较高级的线索机制, 主要作用于词语切分过程的后期。后续研究应从语言的毕生发展和语言的特异性两个方面考察口语语言加工中的词语切分线索。     

人工语法学习模型述评

述评了近年来影响较大的四个人工语法学习模型——THIYOS分类器系统、竞争组块模型、范例模型和联系者模型。它们都认为,人工语法学习所获得的知识中,一部分以规则、组块、总体类似性以及激活模式等形式到达意识层面,另一部分则残存在力量模式中,确定什么内容适合到达意识层面;后者即为内隐知识。这四种模型各抒己见,各有特点,并都为内隐学习的机理提供了重要依据。     

What infants know about syntax but couldn't have learned: experimental evidence for syntactic structure at 18 months

Generative linguistic theory stands on the hypothesis that grammar cannot be acquired solely on the basis of an analysis of the input, but depends, in addition, on innate structure within the learner to guide the process of acquisition. This hypothesis derives from a logical argument, however, and its consequences have never been examined experimentally with infant learners. Challenges to this hypothesis, claiming that an analysis of the input is indeed sufficient to explain grammatical acquisition, have recently gained attention. We demonstrate with novel experimentation the insufficiency of this countervailing view. Focusing on the syntactic structures required to determine the antecedent for the pronoun one, we demonstrate that the input to children does not contain sufficient information to support unaided learning. Nonetheless, we show that 18-month-old infants do have command of the syntax of one. Because this syntactic knowledge could not have been gleaned exclusively from the input, infants' mastery of this aspect of syntax constitutes evidence for the contribution of innate structure within the learner in acquiring a grammar.     

选择性注意在人工语法学习中的作用——回应Eitam，Schul和Hassin(2009)中的两点疑问

从语法规则复杂性（复杂语法vs.简单语法）的角度考察选择性注意在人工语法学习中的必要性,并且比较两种非法序列下的成绩差异来检验被选择忽视的语法规则是否能被习得却未能在测验阶段体现出来。结果表明纵使降低被选择注意的语法的复杂性,被忽视的语法也未被成功习得,只有被选择注意的语法才能被习得;两种非法序列下的正确率无显著差异,即非法序列b所遵循的被忽略的语法未能在分类判断中起作用。选择性注意是语法规则被习得的关键。     

Social Construction of Intimate Partner Violence: A Brief Report on Quantitative Grammatical Analysis

This pilot study uses a quantitative linguistic analysis to assess evidence of agency and control, as well as treatment engagement, in letters written by men (N = 46, 37% Latino) participating in a batterer intervention program. At the program’s conclusion, men were asked to imagine their situation in 5 years and write a letter from their “future self” to their “present self.” Letters were scored for grammatical representations of agency and objectification, and temporal complexity. Results showed that the men tended to grant themselves the subject position (conferring agency) and to grant others the object position (deferring or denying agency) by almost equal margins, both rhetorical maneuvers suggesting an objectifying stance toward others. Moreover, they employed several strategies to address temporality, with more complex strategies suggesting greater treatment engagement. Findings suggest that quantitative linguistic analysis is a promising tool for assessing participants’ progress in and engagement with treatment.     

On the learnability of frequent and infrequent word orders: An artificial language learning study

Most languages have a basic or “canonical” word order, which determines the relative positions of the subject (S), the verb (V), and the object (O) in a typical declarative sentence. The frequency of occurrence of the six possible word orders among world languages is not distributed uniformly. While SVO and SOV represent around 85% of world languages, orders like VSO (9%) or OSV (0.5%) are much less frequent or extremely rare. One possible explanation for this asymmetry is that biological and cognitive constraints for structured sequence processing make some word orders easier to be processed than others. Therefore, the high frequency of these word orders would be related to their higher learnability. The aim of the present study was to compare the learnability of different word orders between groups of adult subjects. Four artificial languages with different word orders were trained: two frequent (SVO, SOV) and two infrequent (VSO, OSV). In a test stage, subjects were asked to discriminate between new correct sentences and syntax or semantic violations. Higher performance rates and faster responses were observed for more frequent word orders. The results support the hypothesis that more frequent word orders are more easily learned.     

Modelling unsupervised online-learning of artificial grammars: Linking implicit and statistical learning

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies.