Second language verb-argument constructions are sensitive to form, function, frequency, contingency, and prototypicality 第二语言的动宾结构对形式、功能、频率、或然性和原型性都很敏感
Nick C. Ellis, Matthew B. O'Donnell and Ute Römer Nick C. Ellis、Matthew B. O'Donnell、Ute RömerUniversity of Michigan / University of Pennsylvania/ Georgia State University 密歇根大学/宾夕法尼亚大学/佐治亚州立大学
Abstract 摘要
We used free association tasks to investigate second language (L2) verb-argument constructions (VACs) and the ways in which their access is sensitive to statistical patterns of usage (verb type-token frequency distribution, VAC-verb contingency, verb-VAC semantic prototypicality). 131 German, 131 Spanish, and 131 Czech advanced L2 learners of English generated the first word that came to mind to fill the V slot in 40 sparse VAC frames such as ‘he __ across the …’, ‘it _ of the …,’ etc. For each VAC, we compared these results with corpus analyses of verb selection preferences in 100 million words of usage and with the semantic network structure of the verbs in these VACs. For all language groups, multiple regression analyses predicting the frequencies of verb types generated for each VAC show independent contributions of (i) verb frequency in the VAC, (ii) VAC-verb contingency, and (iii) verb prototypicality in terms of centrality within the VAC semantic network. L2 VAC processing involves rich associations, tuned by verb type and token frequencies and their contingencies of usage, which interface syntax, lexis, and semantics. 我们使用自由联想任务来研究第二语言(L2)的动词-论据结构(VACs),以及它们的使用对统计使用模式(动词类型-标记词频率分布、VAC-动词或然性、动词-VAC 语义原型性)的敏感性。131 名德语、131 名西班牙语和 131 名捷克语高级英语后学习者在 40 个稀疏的 VAC 框架中产生了第一个想到的词来填补 V 槽,如 "he __ across the ..."、"it _ of the ... "等。对于每一个 VAC,我们都将这些结果与 1 亿词用法中动词选择偏好的语料库分析以及这些 VAC 中动词的语义网络结构进行了比较。对所有语言组而言,预测每个 VAC 生成的动词类型频率的多元回归分析表明,(i) VAC 中的动词频率,(ii) VAC-动词或然性,以及 (iii) VAC 语义网络中的中心性动词原型性,都有独立的贡献。L2 VAC 处理涉及丰富的关联,这些关联受动词类型和标记频率及其使用或然性的调整,它们将句法、词法和语义联系在一起。
Keywords: construction grammar, usage-based acquisition and processing, free association task, semantic networks, contingency, processing, transfer 关键词:构造语法;基于用法的习得和加工;自由联想任务;语义网络;或然性;加工;迁移
1. Constructing a second language 1.构建第二语言
Cognitive linguistic theories of construction grammar posit that language comprises many thousands of constructions - form-meaning mappings, conventionalized in the speech community, and entrenched as language knowledge in the learner’s mind (Goldberg, 1995; Robinson & Ellis, 2008b; Trousdale & Hoffmann, 2013). Usage-based approaches to language acquisition hold that schematic 认知语言学的构式语法理论认为,语言包含成千上万种构式--形式-意义映射,这些构式在语言社区中约定俗成,并作为语言知识根深蒂固地存在于学习者的头脑中(Goldberg,1995;Robinson & Ellis,2008b;Trousdale & Hoffmann,2013)。基于用法的语言习得方法认为,图式
constructions emerge as prototypes from the conspiracy of memories of particular exemplars that language users have experienced. There are many commonalities between first language (L1) and second language acquisition (L2A) that can thus be informed by corpus analyses of input and from cognitive- and psycho-linguistic investigation of construction acquisition following associative and cognitive principles of learning and categorization, hence increased attention to usage-based approaches within L2A research (Collins & Ellis, 2009; Ellis, 1998, 2003; Ellis & Cadierno, 2009; Robinson & Ellis, 2008b). This paper investigates L2 processing of abstract verb-argument constructions (VACs) and its sensitivity to the statistics of usage in terms of verb exemplar type-token frequency distribution, VAC-verb contingency, and VAC-verb semantic prototypicality. 构词法的原型来自语言使用者对特定范例的记忆。第一语言(L1)和第二语言习得(L2A)之间有许多共性,因此可以通过对输入的语料分析,以及根据学习和分类的联想和认知原则对构式习得进行的认知和语言心理学调查来了解这些共性,因此在 L2A 研究中越来越多地关注基于用法的方法(Collins & Ellis, 2009; Ellis, 1998, 2003; Ellis & Cadierno, 2009; Robinson & Ellis, 2008b)。本文研究了抽象动词-论据结构(VACs)的第二语言加工及其对动词示例类型-标记频率分布、VAC-动词或然性和VAC-动词语义原型性等用法统计的敏感性。
Our experience of language allows us to converge upon similar interpretations of novel utterances like “it mandooled across the floor” and “she spugged him the borg.” You know that mandool is a verb of motion and have some idea of how mandooling works - what its action semantics are. You know that spugging involves some sort of gifting, that she is the donor, he the recipient, and that the borg, whatever that is, is the transferred object. How is this possible, given that you have never heard these verbs before? One possibility is that there is a close relationship between the types of verbs that typically appear within constructions, hence their meaning as a whole is inducible from the lexical items experienced within them. So your reading of “it mandools across the …” is driven by an abstract ’ V across noun’ VAC which has inherited its schematic meaning from all the relevant examples you have heard, and your interpretation of mandool emerges from the echoes of the verbs that occupy this VAC - the ‘exemplar cloud’ of tokens including come, walk, move,…, scud, skitter and flit. 我们的语言经验使我们能够对 "它在地板上'蹭'了一下 "和 "她向他'蹭'了一下 "这样的新颖语句做出相似的解释。你知道 "mandool "是一个运动动词,也知道 "mandooling "的作用--它的动作语义是什么。你知道 "spugging "涉及某种赠与,她是赠与者,他是接受者,而 "borg",不管是什么,是被转移的对象。既然你们以前从未听说过这些动词,这怎么可能呢?一种可能是,通常出现在结构中的动词类型之间有着密切的关系,因此它们的意义作为一个整体是可以从其中的词项中诱导出来的。所以你对 "it mandools across the ... "的解读是由一个抽象的 "V across noun "VAC 驱动的,这个 VAC 从你听到的所有相关例子中继承了它的图式意义,而你对 mandool 的解释则来自占据这个 VAC 的动词的回声--由 come、walk、move......、scud、skitter 和 flit 等标记组成的 "典范云"。
The specific claim under examination in this paper is that L2 speakers, like L1 speakers, have schematic VAC meanings that are inherited from the constituency of all the verb exemplars experienced within them, weighted according to the frequency of their experience and the reliability of their association to that construction (their contingency), and their degree of prototypicality in the semantics of the VAC. 本文研究的具体主张是,与 L1 说话者一样,L2 说话者也有从他们经历过的所有动词范例的构成中继承下来的图式 VAC 意义,这些意义的权重取决于他们经历的频率、他们与该结构关联的可靠性(他们的或然性),以及他们在 VAC 语义中的原型程度。
Previous research that addressed this claim for L1 speakers involved two steps: (1) an analysis of VACs in a large corpus of representative usage, and (2) an analysis of the processing of these VACs by fluent native speakers. 以前针对母语为第一语言的人进行的研究涉及两个步骤:(1) 分析具有代表性用法的大型语料库中的 VAC,以及 (2) 分析流利母语使用者对这些 VAC 的处理。
In step one, Ellis and O’Donnell (2011,2012)(2011,2012) investigated the type-token distributions of 20 Verb-Locative (VL) VACs such as ‘V(erb) across n(oun phrase)’ in a 100-million-word corpus of English usage. The other locatives sampled were about, after, against, among, around, as, at, between, for, in, into, like, of, off, over, through, towards, under, and with. They searched a dependency-parsed version of the British National Corpus (BNC, 2007) for specific VACs previously identified 在第一步中,Ellis 和 O'Donnell (2011,2012)(2011,2012) 调查了 20 个动词性(VL)定位词(VAC)的类型标记分布情况,如 1 亿字的英语语料库中的 "V(erb) across n(oun phrase)"。其他取样的定位词有 about、after、anti、among、around、as、at、between、for、in、into、like、of、off、over、through、towards、under 和 with。他们搜索了英国国家语料库(BNC,2007 年)的依存词解析版本,以查找以前识别出的特定 VAC
in the Grammar Patterns volume resulting from the COBUILD corpus-based dictionary project (Francis, Hunston, & Manning, 1996). The details of the linguistic analyses, as well as subsequently modified search specifications in order to improve precision and recall, are described in Römer, O’Donnell, and Ellis (2014, in press). This corpus linguistic research demonstrated: 在 COBUILD 基于语料库的词典项目(Francis, Hunston, & Manning, 1996)中产生的《语法模式》卷中。Römer, O'Donnell, and Ellis (2014, in press) 对语言分析的细节以及为提高精确度和召回率而修改的搜索规范进行了描述。这项语料库语言学研究表明
The frequency profile of the verbs in each VAC follows a Zipfian profile (Zipf, 1935) whereby the highest frequency types account for the most linguistic tokens. Zipf’s law states that in human language, the frequency of words decreases as a power function of their rank. 每个 VAC 中动词的频率分布遵循齐普夫曲线(Zipf, 1935 年),即频率最高的类型占语言标记数最多。齐普夫定律指出,在人类语言中,词的频率随其等级的幂函数递减。
The most frequent verb in each VAC is prototypical of that construction’s functional interpretation, albeit generic in its action semantics. 每个 VAC 中最常见的动词是该结构功能解释的原型,尽管其动作语义是通用的。
VACs are selective in their verb form family occupancy: individual verbs select particular constructions; particular constructions select particular verbs; there is high contingency between verb types and constructions. This means that the Zipfian profiles seen in (1) are not those of the verbs in English as a whole instead their constituency and rank ordering are special to each VAC. VAC 在其动词形式家族占有方面具有选择性:单个动词选择特定的结构;特定的结构选择特定的动词;动词类型和结构之间具有高度的偶然性。这意味着,(1) 中的 Zipfian 剖面并不是英语中动词的整体剖面,相反,它们的构成和等级排序是每个 VAC 所特有的。
VACs are coherent in their semantics. This was assessed using WordNet (Miller, 2009), a distribution-free semantic database based upon psycholinguistic theory, as an initial resource to investigate the similarity/distance between verbs. Then networks science, graph-based algorithms (de Nooy, Mrvar, & Batagelj, 2010) were used to build semantic networks in which the nodes represent verb types and the edges strong semantic similarity for each VAC. Standard measures of network density, average clustering, degree centrality, transitivity, etc. were then used to assess the cohesion of these semantic networks and verb type connectivity within the network. Betweenness centrality was used as a measure of a verb node’s centrality in the VAC network (McDonough & De Vleeschauwer, 2012). In semantic networks, central nodes are those which are prototypical of the network as a whole. VAC 在语义上是一致的。我们使用基于心理语言学理论的无分布语义数据库 WordNet(Miller,2009 年)作为初始资源来评估这一点,以研究动词之间的相似性/距离。然后使用网络科学、基于图的算法(de Nooy、Mrvar 和 Batagelj,2010 年)构建语义网络,其中节点代表动词类型,边代表每个 VAC 的强语义相似性。然后使用网络密度、平均聚类、度中心性、反转性等标准测量方法来评估这些语义网络的内聚性和网络中动词类型的连接性。Betweenness centrality 被用来衡量动词节点在 VAC 网络中的中心性(McDonough & De Vleeschauwer, 2012)。在语义网络中,中心节点是网络整体的原型。
In step two, Ellis, O’Donnell, and Römer (2014) used free association and verbal fluency tasks to investigate verb-argument constructions (VACs) and the ways in which their processing is sensitive to these statistical patterns of usage (verb typetoken frequency distribution, VAC-verb contingency, verb-VAC semantic prototypicality). In experiment 1,285 native speakers of English generated the first word that came to mind to fill the V slot in 40 sparse VAC frames such as ‘he _ across the …’, ‘it _ of the …’, etc. In experiment 2, 40 English speakers generated as many verbs that fit each frame as they could think of in one minute. For each VAC, they compared the results from the experiments with the corpus analyses of usage described above for step 1. For both experiments, multiple regression analyses predicting the frequencies of verb types generated for each VAC showed independent 在第二步中,Ellis、O'Donnell 和 Römer(2014 年)使用自由联想和言语流畅性任务研究了动词-论据结构(VAC)及其处理过程对这些统计使用模式(动词类型-发语频率分布、VAC-动词或然性、动词-VAC 语义原型性)的敏感性。在实验 1 中,285 名以英语为母语的人在 40 个稀疏的 VAC 框架(如 "he _ across the ..."、"it _ of the ... "等)中产生了第一个想到的词来填补 V 槽。在实验 2 中,40 位讲英语的人在一分钟内尽可能多地生成了符合每个框架的动词。对于每个 VAC,他们将实验结果与上述步骤 1 的语料库用法分析结果进行比较。在这两项实验中,预测每个 VAC 生成的动词类型频率的多元回归分析表明,每个 VAC 生成的动词类型频率是独立的。
contributions of (i) verb frequency in the VAC, (ii) VAC-verb contingency, and (iii) verb prototypicality in terms of centrality within the VAC semantic network. Ellis et al. (2014) contend that the fact that native-speaker VACs implicitly represent the statistics of language usage implies that they are learned from usage. Further, usage-based linguists (e.g., Boyd & Goldberg, 2009; Bybee, 2008, 2010; Ellis, 2008a; Goldberg, 2006; Goldberg, Casenhiser, & Sethuraman, 2004; Lieven & Tomasello, 2008; Ninio, 1999), influenced by psychological theory relating to the statistical learning of categories, have proposed that these three factors make concepts robustly learnable - that it is the Zipfian coming together of linguistic form and function that makes language learnable despite learners’ idiosyncratic experiences. Ellis等人(2014)认为,母语使用者的VAC隐含地代表了语言使用的统计数据,这意味着VAC是从使用中学来的。Ellis 等人(2014 年)认为,母语使用者的 VAC 隐含地代表了语言使用的统计数据,这意味着它们是从使用中学来的。此外,基于用法的语言学家(例如,Boyd & Goldberg, 2009; Bybee, 2008, 2010; Ellis, 2008a; Goldberg, 2006; Goldberg, Casenhiser, & Sethuraman, 2004; Lieven & Tomasello, 2008; Ninio, 1999)受有关类别统计学习的心理学理论的影响,提出这三个因素使概念具有很强的可学性--正是语言形式和功能的齐普菲式结合使语言具有可学性,尽管学习者的经验各不相同。
To test the generalizability of these phenomena to L2A, this paper extends the methods of step 2 to test German, Spanish, and Czech advanced learners for comparability with the native English speakers from Ellis et al. (2014). 为了测试这些现象在 L2A 中的普遍性,本文扩展了步骤 2 的方法,测试德语、西班牙语和捷克语高级学习者与 Ellis 等人(2014 年)的英语母语学习者的可比性。
In order to determine whether these factors affect L2 VAC processing, we used the same free-association tasks asking respondents to generate the verbs that come to mind when they see schematic VAC frames such as ‘he _ across the …’, 'it _ of the …, etc. Free-association tasks like this are standard in psychology for determining category representation (Battig & Montague, 1969; Rosch & Mervis, 1975; Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976). Similar methods have also been used in cognitive linguistic investigations of construction grammar (Dąbrowska, 2009). 为了确定这些因素是否会影响 L2 VAC 处理,我们使用了相同的自由联想任务,要求受访者在看到示意性 VAC 框架(如 "he _ across the ..."、"it _ of the ... "等)时生成他们想到的动词。类似的自由联想任务是心理学中确定类别表征的标准方法(Battig & Montague, 1969; Rosch & Mervis, 1975; Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976)。类似的方法也被用于认知语言学对构造语法的研究(Dąbrowska, 2009)。
2.1 Participants 2.1 参与者
The participants were predominantly university students recruited through emails sent by members or associates of the research team, either to the students directly or to one of their instructors. The L1 German, L1 Czech, and L1 Spanish learners were students enrolled at research universities in Germany, the Czech Republic, and Spain. ^(1){ }^{1} The mean number of years of English instruction was 10.04 years for German, 11.37 for Czech, and 12.68 for Spanish learners. L1 English speakers were mostly students enrolled at a large mid-western research university. The following numbers of participants volunteered to complete the VAC survey: 285 native English speakers, 276 L1 German learners of English, 185 L1 Czech learners of English, and 131 L1 Spanish learners of English. To ensure comparability across datasets, we based our analyses on only 131 responses from each of the four participant groups, including all of the L1 Spanish responses and 131 randomly selected responses each from the native speaker, L1 German, and L1 Czech groups. 参与者主要是大学生,由研究小组的成员或同事通过电子邮件直接发给学生或他们的导师之一。德语第一语言、捷克语第一语言和西班牙语第一语言的学习者分别就读于德国、捷克和西班牙的研究型大学。 ^(1){ }^{1} 德语学习者的平均英语教学年限为 10.04 年,捷克语学习者的平均英语教学年限为 11.37 年,西班牙语学习者的平均英语教学年限为 12.68 年。以英语为第一语言者大多是一所大型中西部研究型大学的在校学生。自愿完成 VAC 调查的参与者人数如下:其中母语为英语的有 285 人,母语为德语的英语学习者有 276 人,母语为捷克语的英语学习者有 185 人,母语为西班牙语的英语学习者有 131 人。为了确保不同数据集之间的可比性,我们仅根据四个参与者组中每个组的 131 个回答进行分析,其中包括所有以西班牙语为母语的回答,以及从母语为英语、以德语为母语和以捷克语为母语的参与者组中随机抽取的 131 个回答。
2.2 Method 2.2 方法
A survey was designed and delivered over the web using Qualtrics (http://www. qualtrics.com/). Participants were instructed: “We are going to show you a phrase with a verb missing, and ask you to fill in the gap with the first word that comes to your mind. For example, for the phrase: he _ her the … you might respond he gave her the … or he sent her the … And for the phrase: it __ down the … You might respond it rolls down the … Or it fell down the … On each page you will be presented with a phrase like one of these with a line indicating a missing word. In the text box type the first word you think of and press the [ENTER] key.” They then saw the 20 sentence frames shown in Table 1 shown once with the subject he/she and once with ii. These 40 trials were presented in a random order. We recorded their responses and the time they took on each sentence. The survey as a whole took between 5 and 15 minutes. Responses were lemmatized using the Natural Language Toolkit (Bird, Loper, & Klein, 2009). 我们设计了一份调查问卷,并使用 Qualtrics (http://www. qualtrics.com/) 通过网络发送。参与者被告知"我们将向您展示一个缺失了一个动词的短语,请您用脑海中第一个想到的词来填补空白。例如,对于短语:he _ her the ...,您可能会回答 he gave her the ...或者 he sent her the ...;对于短语:it __ down the ...,您可能会回答 it rolls down the ...或者 it fell down the ...。在文本框中键入你想到的第一个单词,然后按 [ENTER] 键。然后,他们会看到表 1 所示的 20 个句子框架,其中一次显示的是主题词他/她,另一次显示的是 ii 。这 40 次试验以随机顺序呈现。我们记录了他们的回答以及他们在每个句子上花费的时间。整个调查耗时 5 到 15 分钟。我们使用自然语言工具包(Bird、Loper 和 Klein,2009 年)对回答进行了词素化处理。
Table 1. The VAC prompts used here 表 1.此处使用的 VAC 提示
Learning, memory, and perception are all affected by frequency of usage: The more times we experience something, the stronger our memory for it, and the more fluently it is accessed, the relation between frequency of experience and entrenchment following a power law (e.g., Anderson, 2000; Ellis, 2002; Ellis & Schmidt, 1998; Newell, 1990). The more times we experience conjunctions of features or of cues and outcomes, the more they become associated in our minds and the more these subsequently affect perception and categorization (Harnad, 1987; Lakoff, 1987; Taylor, 1998). If constructions are acquired by general learning mechanisms, these general principles of cognition should apply to VACs, too. 学习、记忆和感知都会受到使用频率的影响:我们体验某事物的次数越多,我们对它的记忆就越深刻,对它的访问就越流畅,体验频率与巩固之间的关系遵循幂律(例如,Anderson, 2000; Ellis, 2002; Ellis & Schmidt, 1998; Newell, 1990)。我们经历的特征或线索与结果的结合次数越多,它们在我们头脑中的关联性就越强,随后对感知和分类的影响就越大(Harnad, 1987; Lakoff, 1987; Taylor, 1998)。如果构造是通过一般学习机制获得的,那么这些认知的一般原则也应适用于 VAC。
This leads to Analysis 1: The accessibility of verb types as VAC exemplars in the generative tasks should be a function of their token frequencies in those VACs in usage experience. 这就引出了分析 1:在生成任务中,动词类型作为 VAC 示例的可及性应该是它们在这些 VAC 中的标记频率在使用经验中的函数。
2.4 Analyzing effects of Contingency 2.4 分析突发事件的影响
Contingency/reliability of form-function mapping and associated aspects of predictive value, information gain, and statistical association are driving forces of learning. They are central in psycholinguistic theories of language acquisition (Ellis, 2006a, 2006b, 2008b; MacWhinney, 1987) and in cognitive/corpus linguistic analyses as well (Ellis & Cadierno, 2009; Ellis & Ferreira-Junior, 2009b; Evert, 2005; Gries, 2007, 2012; Gries & Stefanowitsch, 2004; Stefanowitsch & Gries, 2003). 形式-功能映射的权变/可靠性以及预测价值、信息增益和统计关联等相关方面是学习的驱动力。它们在语言习得的心理语言学理论(Ellis, 2006a, 2006b, 2008b; MacWhinney, 1987)和认知/语料语言学分析(Ellis & Cadierno, 2009; Ellis & Ferreira-Junior, 2009b; Evert, 2005; Gries, 2007, 2012; Gries & Stefanowitsch, 2004; Stefanowitsch & Gries, 2003)中占据核心地位。
This leads to Analysis 2: Verbs which are faithful to particular VACs in usage should be those which are more readily accessed by those VAC frames than verbs which are more promiscuous. To measure this, we use the one-way dependency statistic DeltaP\Delta \mathrm{P} (Allan, 1980) shown to predict cue-outcome learning in the associative learning literature (Shanks, 1995) as well as in psycholinguistic studies of formfunction contingency in construction usage, knowledge, and processing (Ellis, 2006a; Ellis & Ferreira-Junior, 2009b; Gries, 2013). 这就引出了分析 2:在使用中忠实于特定 VAC 的动词应该比那些更容易被 VAC 框架访问的动词。为了测量这一点,我们使用了单向依存性统计 DeltaP\Delta \mathrm{P} (Allan,1980 年),该统计在联想学习文献(Shanks,1995 年)以及结构使用、知识和加工中形式功能或然性的心理语言学研究(Ellis,2006a;Ellis & Ferreira-Junior,2009b;Gries,2013 年)中被证明可以预测线索-结果学习。
Consider the contingency table shown in Table 2. DeltaP\Delta \mathrm{P} is the probability of the outcome given the cue minus the probability of the outcome in the absence of the cue. When these are the same, when the outcome is just as likely when the cue is present as when it is not, there is no covariation between the two events and DeltaP=0.DeltaP\Delta \mathrm{P}=0 . \Delta \mathrm{P} approaches 1.0 as the presence of the cue increases the likelihood of the 请看表 2 中的或然率表。 DeltaP\Delta \mathrm{P} 是在有提示的情况下出现结果的概率减去在没有提示的情况下出现结果的概率。当这两个概率相同时,即有提示和没有提示时出现结果的可能性一样大,这两个事件之间就不存在协变, DeltaP=0.DeltaP\Delta \mathrm{P}=0 . \Delta \mathrm{P} 接近 1.0,因为提示的存在会增加出现结果的可能性。
Table 2. A contingency table showing the four possible combinations of events showing the presence or absence of a target cue and an outcome 表 2.显示目标线索和结果出现或不出现的四种可能事件组合的或然率表
Outcome 成果
No outcome 无结果
Cue 提示
a
b
No cue 无提示
c
d
Outcome No outcome
Cue a b
No cue c d| | Outcome | No outcome |
| :--- | :--- | :--- |
| Cue | a | b |
| No cue | c | d |
a,b,c,d\mathrm{a}, \mathrm{b}, \mathrm{c}, \mathrm{d} represent frequencies, so, for example, a is the frequency of conjunction of the cue and the outcome, and c is the number of times the outcome occurred without the cue. The effects of conjoint frequency, verb frequency, and VAC frequency are illustrated for three cases below: a,b,c,d\mathrm{a}, \mathrm{b}, \mathrm{c}, \mathrm{d} 表示频率,例如,a 是提示和结果的连词频率,c 是结果在没有提示的情况下出现的次数。下面用三种情况来说明连接频率、动词频率和 VAC 频率的影响:
DeltaP\Delta \mathrm{P} Construction rarr\rightarrow Word DeltaP\Delta \mathrm{P} 建筑 rarr\rightarrow 文字
DeltaP\Delta \mathrm{P} Word rarr\rightarrow Construction DeltaP\Delta \mathrm{P} Word rarr\rightarrow 建筑
联合频率 a
Conjoint
Frequency
a
Conjoint
Frequency
a| Conjoint |
| :--- |
| Frequency |
| a |
VAC 频率 a+ba+b
VAC
Frequency a+ba+b
VAC
Frequency a+b| VAC |
| :--- |
| Frequency $a+b$ |
动词频率 a+ca+c
Verb
Frequency a+ca+c
Verb
Frequency a+c| Verb |
| :--- |
| Frequency $a+c$ |
Delta\Delta Pcw Delta\Delta PCW
联合频率 a
Conjoint
Frequency
a
Conjoint
Frequency
a| Conjoint |
| :--- |
| Frequency |
| a |
动词频率 a+ba+b
Verb
Frequency a+ba+b
Verb
Frequency a+b| Verb |
| :--- |
| Frequency $a+b$ |
VAC 频率 a+ca+c
VAC
Frequency a+ca+c
VAC
Frequency a+c| VAC |
| :--- |
| Frequency $a+c$ |
Delta\Delta Pwc Delta\Delta Pwc
lie across 横亘
44
5,261
13,190
0.0076
44
13,190
5,261
0.0030
stride across 跨
44
5,261
1,049
0.0083
44
1,049
5,261
0.0416
crowd into 挤入
44
50,070
749
0.0008
44
749
50,070
0.0559
DeltaP Construction rarr Word DeltaP Word rarr Construction
"Conjoint
Frequency
a" "VAC
Frequency a+b" "Verb
Frequency a+c" Delta Pcw "Conjoint
Frequency
a" "Verb
Frequency a+b" "VAC
Frequency a+c" Delta Pwc
lie across 44 5,261 13,190 0.0076 44 13,190 5,261 0.0030
stride across 44 5,261 1,049 0.0083 44 1,049 5,261 0.0416
crowd into 44 50,070 749 0.0008 44 749 50,070 0.0559| | $\Delta \mathrm{P}$ Construction $\rightarrow$ Word | | | | $\Delta \mathrm{P}$ Word $\rightarrow$ Construction | | | |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| | Conjoint <br> Frequency <br> a | VAC <br> Frequency $a+b$ | Verb <br> Frequency $a+c$ | $\Delta$ Pcw | Conjoint <br> Frequency <br> a | Verb <br> Frequency $a+b$ | VAC <br> Frequency $a+c$ | $\Delta$ Pwc |
| lie across | 44 | 5,261 | 13,190 | 0.0076 | 44 | 13,190 | 5,261 | 0.0030 |
| stride across | 44 | 5,261 | 1,049 | 0.0083 | 44 | 1,049 | 5,261 | 0.0416 |
| crowd into | 44 | 50,070 | 749 | 0.0008 | 44 | 749 | 50,070 | 0.0559 |
outcome and approaches -1.0 as the cue decreases the chance of the outcome - a negative association. 当线索降低结果出现的几率时,负联想的结果就会接近-1.0。
DeltaP\Delta \mathrm{P} is affected by the conjoint frequency of construction and verb in the corpus (a), but also by the frequency of the verb in the corpus, the frequency of the VAC in the corpus, and the number of verbs in the corpus. For illustration, the lower part of Table 2 considers three exemplars, lie across, stride across, and crowd into, which all have the same conjoint frequency of 44 in a corpus of 17,408,901 VAC instances. This is the value that Analysis 1 would consider. However, while DeltaP\Delta \mathrm{P} Construction rarr\rightarrow Word ( DeltaPcw\Delta \mathrm{Pcw} ) for lie across and stride across are approximately the same, the one for crowd into is an order of magnitude less. DeltaPwc\Delta \mathrm{Pwc} shows a different pattern - the values for stride across and crowd into are over ten times greater than for lie across. In this experiment, we are giving people the construction and asking them to generate the word, and DeltaPcw\Delta \mathrm{Pcw} is the relevant metric. DeltaP\Delta \mathrm{P} 不仅受结构和动词在语料库中的连用频率(a)的影响,还受动词在语料库中的频率、VAC 在语料库中的频率以及语料库中动词数量的影响。为便于说明,表 2 下半部分考虑了三个示例,即 lie across、stride across 和 crowd into,它们在 17 408 901 个 VAC 实例的语料库中的连词频率都是 44。这就是分析 1 所考虑的值。然而,虽然 DeltaP\Delta \mathrm{P} 建筑 rarr\rightarrow Word ( DeltaPcw\Delta \mathrm{Pcw} )lie across 和 stride across 的连词频率大致相同,但 crowd into 的连词频率却低了一个数量级。 DeltaPwc\Delta \mathrm{Pwc} 显示了不同的模式--跨步和挤入的数值是横躺的十倍以上。在这个实验中,我们给人们提供了结构,并要求他们生成单词,而 DeltaPcw\Delta \mathrm{Pcw} 就是相关的度量标准。
2.5 Assessing the effects of Semantic Prototypicality 2.5 评估语义原型的影响
In our analyses of VAC semantics in usage, we determined prototypicality in terms of the centrality of the verb in the semantic network connecting the verb types that feature in that VAC (Ellis & O’Donnell, 2011, 2012). We used the measure ‘betweenness centrality’, which was developed to quantify the control of a human on the communication between other humans in a social network (McDonough & De Vleeschauwer, 2012). 在我们对使用中的 VAC 语义进行分析时,我们根据动词在连接该 VAC 中的动词类型的语义网络中的中心性来确定原型性(Ellis & O'Donnell, 2011, 2012)。我们使用了 "间度中心性 "测量方法,该方法用于量化人类对社交网络中其他人类之间交流的控制(McDonough & De Vleeschauwer, 2012)。
This leads to Analysis 3: The verb types that are produced more frequently in the generative tasks should be more prototypical of the VAC semantics as indexed by their degree in the semantic network of the VAC in our usage analyses. 这就引出了分析 3:在生成任务中产生频率较高的动词类型应该是更典型的 VAC 语义,这一点可以从我们的用法分析中 VAC 语义网络中的动词类型程度得到体现。
2.6 Results 2.6 结果
The verb types generated for each VAC were aggregated across participants and the s//hes / h e or it prompt variants. Scrutiny of our corpus analyses demonstrated that we were unable to achieve sufficient precision in our searching for the after, at, and in VACs because these occur in a wide variety of temporal references as well as locatives. They were therefore removed from subsequent analyses, leaving 17 VACs for the correlations and regressions. 为每个 VAC 生成的动词类型在参与者和 s//hes / h e 或 it 提示变体之间进行了汇总。对语料库的分析表明,我们在搜索 after、at 和 in VAC 时无法达到足够的精确度,因为这些动词会出现在各种时间指代和定位词中。因此,我们在随后的分析中剔除了它们,只留下 17 个 VAC 进行相关分析和回归分析。
We restrict analysis to the verb types that cover the top 95% of verb tokens in English usage. In the BNC, the most frequent 961 verbs in English cover this range. This threshold is necessary to avoid the long tail of the BNC frequency distribution (very low frequency types and hapax legomena) dominating the analyses. Without 我们将分析范围限制在英语使用中占前 95% 的动词类型。在 BNC 中,英语中出现频率最高的 961 个动词都在这个范围内。为了避免 BNC 频率分布的长尾(频率极低的类型和合体词)主导分析,这一阈值是必要的。无
this step, results of such research are over-influenced simply by the size of the reference corpus - the larger the corpus, the longer the tail (Malvern, Richards, Chipere, & Duran, 2004; Tweedie & Baayen, 1998). 在这一步骤中,参考语料库的规模会对此类研究的结果产生过度影响--语料库越大,尾部越长(Malvern, Richards, Chipere, & Duran, 2004; Tweedie & Baayen, 1998)。
VAC n verb types "r log
BNC VAC
freq" "r log
DeltaPcw" p of r "r log
VACSEM
centrality" "r log
BNC verb freq" p" of "r
V about 31 0.53 ** 0.68 ** 0.13 ns 0.37 *
V across 24 0.52 ** 0.50 * 0.37 ns 0.44 *
V against 26 0.57 ** 0.51 ** 0.29 ns 0.48 **
V among 27 0.69 ** 0.64 ** 0.53 ** 0.71 **
V around 28 0.52 ** 0.32 ns 0.58 ** 0.62 **
V as 41 0.20 ns -0.09 ns 0.30 ns 0.33 *
V between 30 0.63 ** 0.37 * 0.49 ** 0.49 **
V for 41 0.67 ** 0.74 ** 0.62 ** 0.52 **
V into 27 0.51 ** 0.54 ** 0.55 ** 0.42 *
V like 38 0.58 ** 0.54 ** 0.55 ** 0.24 ns
V of 26 0.77 ** 0.68 ** 0.49 ** 0.61 **
V off 25 0.58 ** 0.60 ** 0.50 ** 0.41 *
V over 29 0.27 ns 0.10 ns 0.16 ns 0.25 ns
V through 32 0.62 ** 0.66 ** 0.59 ** 0.48 **
V towards 26 0.61 ** 0.67 ** 0.70 ** 0.41 *
V under 29 0.59 ** 0.42 * 0.55 ** 0.43 *
V with 33 0.51 ** 0.38 * 0.48 ** 0.48 **
MEAN 30.1 0.55 0.48 0.46 0.45 | VAC | $n$ verb types | $r \log$ <br> BNC VAC <br> freq | | $r \log$ <br> $\Delta \mathrm{Pcw}$ | $p$ of $r$ | $r \log$ <br> VACSEM <br> centrality | | $r \log$ <br> BNC verb freq | $p \text { of } r$ |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| V about | 31 | 0.53 | ** | 0.68 | ** | 0.13 | ns | 0.37 | * |
| V across | 24 | 0.52 | ** | 0.50 | * | 0.37 | ns | 0.44 | * |
| V against | 26 | 0.57 | ** | 0.51 | ** | 0.29 | ns | 0.48 | ** |
| V among | 27 | 0.69 | ** | 0.64 | ** | 0.53 | ** | 0.71 | ** |
| V around | 28 | 0.52 | ** | 0.32 | ns | 0.58 | ** | 0.62 | ** |
| V as | 41 | 0.20 | ns | -0.09 | ns | 0.30 | ns | 0.33 | * |
| V between | 30 | 0.63 | ** | 0.37 | * | 0.49 | ** | 0.49 | ** |
| V for | 41 | 0.67 | ** | 0.74 | ** | 0.62 | ** | 0.52 | ** |
| V into | 27 | 0.51 | ** | 0.54 | ** | 0.55 | ** | 0.42 | * |
| V like | 38 | 0.58 | ** | 0.54 | ** | 0.55 | ** | 0.24 | ns |
| V of | 26 | 0.77 | ** | 0.68 | ** | 0.49 | ** | 0.61 | ** |
| V off | 25 | 0.58 | ** | 0.60 | ** | 0.50 | ** | 0.41 | * |
| V over | 29 | 0.27 | ns | 0.10 | $n s$ | 0.16 | ns | 0.25 | ns |
| V through | 32 | 0.62 | ** | 0.66 | ** | 0.59 | ** | 0.48 | ** |
| V towards | 26 | 0.61 | ** | 0.67 | ** | 0.70 | ** | 0.41 | * |
| V under | 29 | 0.59 | ** | 0.42 | * | 0.55 | ** | 0.43 | * |
| V with | 33 | 0.51 | ** | 0.38 | * | 0.48 | ** | 0.48 | ** |
| MEAN | 30.1 | 0.55 | | 0.48 | | 0.46 | | 0.45 | |
2.6.1 Analysis 1 2.6.1 分析 1
We plot the lemmatized verb types for each VAC in the space defined by log token generation frequency against log token frequency in that VAC in the BNC. The plot for ’ V of n ’ is shown in Figure 1 for each of the language groups. Items appear on the graph if the lemma both appears as a response in the generation task for that VAC and it also appears in the BNC. The font size for each verb plotted is proportional to the frequency of that verb in the BNC as a whole. It can be seen 我们在对数标记生成频率与 BNC 中该 VAC 的对数标记频率所定义的空间中绘制了每个 VAC 的词法化动词类型。图 1 显示了每个语组的 "V of n "图。如果词目在该 VAC 的生成任务中同时作为应答出现,并且也出现在 BNC 中,则词目会出现在图中。图中每个动词的字体大小与该动词在整个 BNC 中出现的频率成正比。可以看出
Figure 1. English and German, Spanish and Czech L2 English log10 verb generation frequency against log10 verb frequency in that VAC in the BNC for VAC ‘V of n’. Verb font size is proportional to overall verb token frequency in the BNC as a whole. 图 1.英语、德语、西班牙语和捷克语 L2 英语对数 10 动词生成频率与 BNC 中 VAC "V of n "对数 10 动词频率的对比。动词字体大小与整个 BNC 中的总体动词符号频率成正比。
Figure 2. L1 English and German, Spanish and Czech L2 English log10 verb generation frequency against log 10\log 10 verb frequency in that VAC in the BNC for VAC ‘V about n’. Verb font size is proportional to overall verb token frequency in the BNC as a whole. 图 2.VAC 'V about n' 的 L1 英语和德语、西班牙语和捷克语 L2 英语的 log10 动词生成频率与 BNC 中该 VAC 中 log 10\log 10 动词频率的对比。动词字体大小与整个 BNC 中的总体动词符号频率成正比。
that for the L1 English group, generation frequency follows verb frequency in that VAC in the BNC with a correlation of r=0.77r=0.77. After the copula be, cognition verbs (think and know) are the most frequent types, followed by communication verbs (speak, say, talk, ask), and also perception verbs (smell, hear). Thus the semantic sets of the VAC frame in usage are all sampled in the free association task, and the sampling follows the frequencies of usage. The responses for the three ESL groups pattern in a similar fashion: generation frequency follows verb frequency in that 就第一语言英语组而言,在 BNC 的 VAC 中,生成频率紧随动词频率,相关性为 r=0.77r=0.77 。在副词 be 之后,认知动词(think 和 know)是使用频率最高的类型,其次是交际动词(speak、say、talk、ask)和感知动词(smell、hear)。因此,在自由联想任务中,使用中的 VAC 框架语义集都被采样,采样遵循使用频率。三个 ESL 群体的回答模式与此类似:生成频率与动词频率一致,即
Figure 3. L1 English and German, Spanish and Czech L2 English log10 verb generation frequency against log10 verb frequency in that VAC in the BNC for VAC ‘V between n’. Verb font size is proportional to overall verb token frequency in the BNC as a whole. 图 3.L1 英语、德语、西班牙语和捷克语 L2 英语的对数 10 动词生成频率与 BNC 中 VAC 'V between n' 的对数 10 动词频率的对比。动词字体大小与整个 BNC 中的总体动词符号频率成正比。
VAC in the BNC with a correlation of r=0.60r=0.60 for the L1 German group, r=0.68r=0.68 for the L1 Spanish group, and r=0.58r=0.58 for the L1 Czech group. BNC 中的 VAC 与德语第一语言组、西班牙语第一语言组和捷克语第一语言组的相关性分别为 r=0.60r=0.60 、 r=0.68r=0.68 和 r=0.58r=0.58 。
Illustrative plots of the responses for the VACs ‘V about n’, ‘V between n’, and ’ V against n ’ against frequencies of the verbs in that VAC in the BNC are shown in Figures 2, 3, and 4 where it can be seen that the advanced L2 English speakers generated a similar set of verb types for these VACs with similar token frequencies. 图 2、图 3 和图 4 是 VAC "V about n"、"V between n "和 "V against n "的反应与 BNC 中该 VAC 中动词频率的示意图。
Figure 4. L1 English and German, Spanish and Czech L2 English log10 verb generation frequency against log10 verb frequency in that VAC in the BNC for VAC ‘V against n’. Verb font size is proportional to overall verb token frequency in the BNC as a whole. 图 4L1 英语、德语、西班牙语和捷克语 L2 英语的对数 10 动词生成频率与 BNC 中 VAC "V 对 n "的对数 10 动词频率的对比。动词字体大小与整个 BNC 中的总体动词符号频率成正比。
For each VAC we correlate verb generation frequency against verb frequency in the VAC in the BNC. These correlations are shown in the third column of Table 3, their significance levels in column 4 of Table 3 for the native English respondents. These are non-trivial correlations. Their mean is 0.55 . The same data are shown for the German respondents in Table 4 where the mean correlation is 0.59 , for the Spanish respondents in Table 5 where the mean correlation is 0.65 , and for the Czech respondents in Table 6 where the mean correlation is 0.59 . The 对于每个 VAC,我们将动词生成频率与 BNC 中 VAC 中的动词频率相关联。表 3 第 3 栏显示了这些相关性,表 3 第 4 栏显示了这些相关性对英语为母语的受访者的显著性水平。这些相关性并不小。其平均值为 0.55。表 4 显示了德国受访者的相同数据,其平均相关系数为 0.59;表 5 显示了西班牙受访者的数据,其平均相关系数为 0.65;表 6 显示了捷克受访者的数据,其平均相关系数为 0.59。表 5
responses of all language groups, L1 and L2 alike, are sensitive to verb usage frequency in the VAC across the 17 constructions sampled. 在抽样调查的 17 种结构中,所有语言组(包括第一语言组和第二语言组)对 VAC 中动词使用频率的反应都很敏感。
2.6.2 Analysis 2 2.6.2 分析 2
To assess whether frequency of verb generation is associated with VAC-verb contingency, we correlate this with /_\Pcw\triangle \mathrm{Pcw} in the BNC. These correlations and their significance levels are shown in columns 5 and 6 of Tables 3-6. Again they are non-trivial. Their mean is 0.48 for English L1, 0.53 for German, 0.63 for Spanish, and 0.56 for Czech. Across the 17 constructions, the responses of all language groups, L1 and L2 alike, are sensitive to VAC-verb contingency. 为了评估动词生成频率是否与 VAC-动词或然性相关,我们将其与 BNC 中的 /_\Pcw\triangle \mathrm{Pcw} 相关联。表 3-6 第 5 列和第 6 列显示了这些相关性及其显著性水平。同样,它们也不是微不足道的。它们的平均值分别为:英语 L1 0.48,德语 0.53,西班牙语 0.63,捷克语 0.56。在这 17 种结构中,所有语言组,无论是 L1 还是 L2,对 VAC-动词或然性都很敏感。
VAC n verb types "r log
BNC VAC
freq" p" of "r "r log
Delta Pcw" p of r "r log
VACSEM
centrality" p" of "r "r log
BNC verb
freq" p of r
V about 29 0.58 ** 0.75 ** 0.37 * 0.34 ns
V across 22 0.73 ** 0.72 ** 0.44 * 0.56 *
V against 28 0.56 ** 0.53 ** 0.39 * 0.43 *
V among 25 0.77 ** 0.55 ** 0.51 * 0.69 **
V around 23 0.63 ** 0.43 * 0.65 ** 0.64 **
V as 56 0.26 ns 0.03 ns 0.33 ** 0.35 **
V between 28 0.64 ** 0.42 * 0.20 ns 0.35 ns
V for 36 0.60 ** 0.74 ** 0.16 ns 0.35 *
V into 27 0.60 ** 0.61 ** 0.60 ** 0.29 ns
V like 34 0.57 ** 0.58 ** 0.54 ** 0.33 ns
V of 36 0.60 ** 0.65 ** 0.25 ns 0.39 *
V off 31 0.69 ** 0.71 ** 0.62 ** 0.59 **
V over 33 0.47 ** 0.37 * 0.26 ns 0.32 ns
V through 33 0.53 ** 0.77 ** 0.62 ** 0.18 ns
V towards 28 0.69 ** 0.64 ** 0.64 ** 0.58 **
V under 25 0.52 ** 0.28 ns 0.30 ns 0.37 ns
V with 34 0.53 ** 0.31 ns 0.52 ** 0.46 **
MEAN 31.1 0.59 0.53 0.44 0.42 | VAC | $n$ verb types | $r \log$ <br> BNC VAC <br> freq | $p \text { of } r$ | $r \log$ <br> $\Delta$ Pcw | $p$ of $r$ | $r \log$ <br> VACSEM <br> centrality | $p \text { of } r$ | $r \log$ <br> BNC verb <br> freq | $p$ of $r$ |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| V about | 29 | 0.58 | ** | 0.75 | ** | 0.37 | * | 0.34 | ns |
| V across | 22 | 0.73 | ** | 0.72 | ** | 0.44 | * | 0.56 | * |
| V against | 28 | 0.56 | ** | 0.53 | ** | 0.39 | * | 0.43 | * |
| V among | 25 | 0.77 | ** | 0.55 | ** | 0.51 | * | 0.69 | ** |
| V around | 23 | 0.63 | ** | 0.43 | * | 0.65 | ** | 0.64 | ** |
| V as | 56 | 0.26 | ns | 0.03 | ns | 0.33 | ** | 0.35 | ** |
| V between | 28 | 0.64 | ** | 0.42 | * | 0.20 | ns | 0.35 | ns |
| V for | 36 | 0.60 | ** | 0.74 | ** | 0.16 | ns | 0.35 | * |
| V into | 27 | 0.60 | ** | 0.61 | ** | 0.60 | ** | 0.29 | ns |
| V like | 34 | 0.57 | ** | 0.58 | ** | 0.54 | ** | 0.33 | ns |
| V of | 36 | 0.60 | ** | 0.65 | ** | 0.25 | ns | 0.39 | * |
| V off | 31 | 0.69 | ** | 0.71 | ** | 0.62 | ** | 0.59 | ** |
| V over | 33 | 0.47 | ** | 0.37 | * | 0.26 | ns | 0.32 | ns |
| V through | 33 | 0.53 | ** | 0.77 | ** | 0.62 | ** | 0.18 | ns |
| V towards | 28 | 0.69 | ** | 0.64 | ** | 0.64 | ** | 0.58 | ** |
| V under | 25 | 0.52 | ** | 0.28 | ns | 0.30 | ns | 0.37 | ns |
| V with | 34 | 0.53 | ** | 0.31 | ns | 0.52 | ** | 0.46 | ** |
| MEAN | 31.1 | 0.59 | | 0.53 | | 0.44 | | 0.42 | |
2.6.3 Analysis 3 2.6.3 分析 3
To determine whether frequency of verb generation is associated with semantic prototypicality of the VAC verb usage in the BNC, we correlate frequency of verb generation with the betweenness centrality of that verb in the semantic network of the verb types occupying that VAC in the BNC. These correlations and their significance levels are shown in columns 7 and 8 of Tables 3-6. Their mean is 0.46 for English L1, 0.44 for German, 0.43 for Spanish, and 0.35 for Czech. These associations are more modest: 12/17 are significant in the L1 group and 27/51 in the L2 samples. 为了确定动词生成频率是否与 BNC 中 VAC 动词用法的语义原型相关,我们将动词生成频率与该动词在 BNC 中占据该 VAC 的动词类型语义网络中的间度中心性相关联。表 3-6 第 7 和第 8 列显示了这些相关性及其显著性水平。其平均值分别为:英语 L1 0.46,德语 0.44,西班牙语 0.43,捷克语 0.35。这些相关性比较适中:12/17 的相关性在 L1 样本中显著,27/51 的相关性在 L2 样本中显著。
VAC n verb types "r log
BNC VAC
freq" p of rr log DeltaPcw p of r "r log
VACSEM
centrality" "r log
BNC verb freq" p" of "r
V about 26 0.64 ** 0.74 ** 0.33 ns 0.31 ns
V across 23 0.61 ** 0.59 ** 0.46 ns 0.53 **
V against 28 0.65 ** 0.61 ** 0.15 ns 0.30 ns
V among 22 0.63 ** 0.65 ** 0.36 ns 0.73 **
V around 23 0.63 ** 0.46 * 0.68 ** 0.76 **
V as 42 0.46 ** 0.36 * 0.23 ns 0.32 *
V between 30 0.47 ** 0.16 ns 0.37 * 0.53 **
V for 36 0.60 ** 0.74 ** 0.27 ns 0.47 **
V into 23 0.70 ** 0.79 ** 0.46 * 0.68 **
V like 23 0.77 ** 0.86 ** 0.40 ns 0.29 ns
V of 30 0.69 ** 0.61 ** 0.35 ns 0.48 **
V off 22 0.65 ** 0.69 ** 0.47 * 0.40 ns
V over 29 0.73 ** 0.72 ** 0.54 ** 0.48 **
V through 21 0.79 ** 0.84 ** 0.59 ** 0.54 **
V towards 23 0.73 ** 0.69 ** 0.71 ** 0.35 ns
V under 31 0.60 ** 0.49 ** 0.35 ns 0.38 *
V with 25 0.76 ** 0.72 ** 0.59 ** 0.56 **
MEAN 26.9 0.65 0.63 0.43 0.48 | VAC | $n$ verb types | $r \log$ <br> BNC VAC <br> freq | $p$ of $r r \log$ $\Delta \mathrm{Pcw}$ | | $p$ of $r$ | $r \log$ <br> VACSEM <br> centrality | | $r \log$ <br> BNC verb freq | $p \text { of } r$ |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| V about | 26 | 0.64 | ** | 0.74 | ** | 0.33 | ns | 0.31 | ns |
| V across | 23 | 0.61 | ** | 0.59 | ** | 0.46 | ns | 0.53 | ** |
| V against | 28 | 0.65 | ** | 0.61 | ** | 0.15 | ns | 0.30 | ns |
| V among | 22 | 0.63 | ** | 0.65 | ** | 0.36 | ns | 0.73 | ** |
| V around | 23 | 0.63 | ** | 0.46 | * | 0.68 | ** | 0.76 | ** |
| V as | 42 | 0.46 | ** | 0.36 | * | 0.23 | ns | 0.32 | * |
| V between | 30 | 0.47 | ** | 0.16 | ns | 0.37 | * | 0.53 | ** |
| V for | 36 | 0.60 | ** | 0.74 | ** | 0.27 | ns | 0.47 | ** |
| V into | 23 | 0.70 | ** | 0.79 | ** | 0.46 | * | 0.68 | ** |
| V like | 23 | 0.77 | ** | 0.86 | ** | 0.40 | ns | 0.29 | ns |
| V of | 30 | 0.69 | ** | 0.61 | ** | 0.35 | ns | 0.48 | ** |
| V off | 22 | 0.65 | ** | 0.69 | ** | 0.47 | * | 0.40 | ns |
| V over | 29 | 0.73 | ** | 0.72 | ** | 0.54 | ** | 0.48 | ** |
| V through | 21 | 0.79 | ** | 0.84 | ** | 0.59 | ** | 0.54 | ** |
| V towards | 23 | 0.73 | ** | 0.69 | ** | 0.71 | ** | 0.35 | ns |
| V under | 31 | 0.60 | ** | 0.49 | ** | 0.35 | ns | 0.38 | * |
| V with | 25 | 0.76 | ** | 0.72 | ** | 0.59 | ** | 0.56 | ** |
| MEAN | 26.9 | 0.65 | | 0.63 | | 0.43 | | 0.48 | |
2.6.4 Combined analyses 2.6.4 综合分析
These analyses VAC by VAC and variable by variable have shown that each of our potential causal variables is associated with verb generation frequency. Nevertheless, within each analysis the sample sizes are rather low. Sampling 131 tokens from a Zipfian distribution, where the lead item gets the lion’s share, results in variability in the lower frequency items which a respondent might, or might not, generate. Ideally such research would involve larger samples of respondents, or, as in Ellis, O’Donnell, & Römer (2014) Experiment 2, more responses per participant. 这些逐个 VAC 和逐个变量的分析表明,我们的每个潜在因果变量都与动词生成频率有关。然而,每项分析的样本量都很低。从 Zipfian 分布中抽取 131 个标记,其中主要项目占绝大部分,这就导致了受访者可能产生或不产生的低频项目的差异性。理想情况下,此类研究会涉及更多的受访者样本,或者像 Ellis、O'Donnell 和 Römer (2014) 的实验 2 一样,每个参与者做出更多的回答。
However, we can obtain more power of analysis, as well as assess generalization, by looking across the constructions to assess the degree to which these 然而,我们可以通过观察不同的结构来评估这些结构在多大程度上具有普遍性,从而获得更强的分析能力。
VAC n verb types "r log
BNC VAC
freq" p of r "r log
Delta Pcw" p of r "r log
VACSEM
centrality" "rr log
BNC verb
freq" p of r
V about 22 0.65 ** 0.68 ** 0.28 ns 0.35 ns
V across 27 0.67 ** 0.66 ** 0.44 * 0.49 **
V against 23 0.67 ** 0.63 ** 0.02 ns 0.25 ns
V among 25 0.47 * 0.56 ** 0.08 ns 0.37 ns
V around 27 0.66 ** 0.54 ** 0.65 ** 0.60 **
V as 38 0.40 ** 0.30 ns 0.13 ns 0.22 ns
V between 25 0.58 ** 0.38 ns 0.45 * 0.50 **
V for 33 0.70 ** 0.80 ** 0.23 ns 0.17 ns
V into 24 0.59 ** 0.57 ** 0.29 ns 0.44 *
V like 23 0.72 ** 0.80 ** 0.65 ** 0.22 ns
V of 31 0.58 ** 0.51 ** 0.25 ns 0.28 ns
V off 30 0.42 * 0.51 ** 0.35 * 0.23 ns
V over 27 0.32 ns 0.22 ns 0.32 ns 0.22 ns
V through 21 0.71 ** 0.70 ** 0.70 ** 0.61 **
V towards 18 0.76 ** 0.78 ** 0.68 ** 0.32 ns
V under 24 0.55 ** 0.40 * 0.22 ns 0.32 ns
V with 36 0.50 ** 0.49 ** 0.26 ns 0.30 ns
MEAN 26.7 0.59 0.56 0.35 0.35 | VAC | $n$ verb types | $r \log$ <br> BNC VAC <br> freq | $p$ of $r$ | $r \log$ <br> $\Delta$ Pcw | $p$ of $r$ | $r \log$ <br> VACSEM <br> centrality | | $r r \log$ <br> BNC verb <br> freq | $p$ of $r$ |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| V about | 22 | 0.65 | ** | 0.68 | ** | 0.28 | ns | 0.35 | ns |
| V across | 27 | 0.67 | ** | 0.66 | ** | 0.44 | * | 0.49 | ** |
| V against | 23 | 0.67 | ** | 0.63 | ** | 0.02 | ns | 0.25 | ns |
| V among | 25 | 0.47 | * | 0.56 | ** | 0.08 | ns | 0.37 | ns |
| V around | 27 | 0.66 | ** | 0.54 | ** | 0.65 | ** | 0.60 | ** |
| V as | 38 | 0.40 | ** | 0.30 | ns | 0.13 | ns | 0.22 | ns |
| V between | 25 | 0.58 | ** | 0.38 | ns | 0.45 | * | 0.50 | ** |
| V for | 33 | 0.70 | ** | 0.80 | ** | 0.23 | ns | 0.17 | ns |
| V into | 24 | 0.59 | ** | 0.57 | ** | 0.29 | ns | 0.44 | * |
| V like | 23 | 0.72 | ** | 0.80 | ** | 0.65 | ** | 0.22 | ns |
| V of | 31 | 0.58 | ** | 0.51 | ** | 0.25 | ns | 0.28 | ns |
| V off | 30 | 0.42 | * | 0.51 | ** | 0.35 | * | 0.23 | ns |
| V over | 27 | 0.32 | ns | 0.22 | ns | 0.32 | ns | 0.22 | ns |
| V through | 21 | 0.71 | ** | 0.70 | ** | 0.70 | ** | 0.61 | ** |
| V towards | 18 | 0.76 | ** | 0.78 | ** | 0.68 | ** | 0.32 | ns |
| V under | 24 | 0.55 | ** | 0.40 | * | 0.22 | ns | 0.32 | ns |
| V with | 36 | 0.50 | ** | 0.49 | ** | 0.26 | ns | 0.30 | ns |
| MEAN | 26.7 | 0.59 | | 0.56 | | 0.35 | | 0.35 | |
patterns hold across the VACs analyzed here, and the degree to which each causal variable makes an independent contribution. Therefore we stacked the generation data for the different VACs into a combined data set. We included cases where the verb appeared in the language group generations for that VAC and in the BNC in that VAC. If we look within a construction, since the construction frequency remains constant, words with similar conjoint frequencies have similar DeltaPcw\Delta \mathrm{Pcw}, hence the similar sizes of correlation for frequency and DeltaPcw\Delta \mathrm{Pcw} in Tables 3-6. However when, as here, we compare across VACs of very different frequencies in the corpus (from lows of 1459 for off, 2551 among, up to 84,648 for and 89,745 with), verbs with the same conjoint frequency will have markedly different DeltaPcw\Delta \mathrm{Pcw} (as in the cases of stride across and crowd into in Table 2). 在此分析的所有 VAC 中,我们是否发现了相同的模式,以及每个因果变量的独立贡献程度。因此,我们将不同 VAC 的世代数据堆叠成一个综合数据集。我们将动词出现在该 VAC 的语言组世代和该 VAC 的 BNC 中的情况包括在内。如果我们在一个构词内部进行研究,由于构词频率保持不变,具有相似联合频率的词具有相似的 DeltaPcw\Delta \mathrm{Pcw} ,因此表 3-6 中频率和 DeltaPcw\Delta \mathrm{Pcw} 的相关性大小相似。然而,当我们对语料库中不同频率的 VAC 进行比较时(最低频率为 1459 off、2551 among,最高频率为 84 648 for 和 89 745 with),具有相同连词频率的动词将具有明显不同的 DeltaPcw\Delta \mathrm{Pcw} (如表 2 中的 stride across 和 crowd into)。
The next step is to use these data sets to perform, for each language group, a multiple regression of log generation frequency against log BNC verb frequency in that VAC, log DeltaPcw\log \Delta \mathrm{Pcw}, and log verb betweenness centrality in that VAC usage in the BNC, entering all three independent variables into the regression using glm in R. We also used the R package relaimpo (Grömping, 2006) to calculate the relative importance of their contributions. The resultant coefficients are shown in Table 7. 下一步是使用这些数据集,对每个语组进行对数生成频率与该 VAC 中对数 BNC 动词频率、 log DeltaPcw\log \Delta \mathrm{Pcw} 和该 VAC 在 BNC 中使用的对数动词间中心度的多元回归,使用 R 中的 glm 将所有三个自变量输入回归。我们还使用 R 软件包 relaimpo(Grömping,2006 年)计算它们贡献的相对重要性。结果系数见表 7。
Consider first the English L1 group. Each of the three predictors makes a highly significant independent contribution in explaining the generation data at p < .01p<.01. The major predictor is DeltaPcw\Delta \mathrm{Pcw} (Relative Importance 0.40 ), followed by verb betweenness centrality in the semantic network for VAC usage in the BNC ( 1 mg 0.31 and BNC verb frequency in that VAC ( 1 mg 0.29 .) Tests for collinearity of the independent variables produce low variance inflation factors well within acceptable limits. All three predictors also make significant independent contributions using rlm robust regression in R (Fox, 2002). The R effects library (Fox, 2003) was used to graph the effects of each of the predictors. The left column of Figure 5 shows these with confidence intervals for English log L1 frequencies of verb types generated for a VAC frame against (i) log frequencies of that verb type in that VAC 首先考虑英语 L1 组。在 p < .01p<.01 处,三个预测因子中的每一个都对解释生成数据做出了非常显著的独立贡献。主要预测因子是 DeltaPcw\Delta \mathrm{Pcw} (相对重要性 0.40),其次是 BNC 中 VAC 使用语义网络中的动词间中心度(1 mg 0.31)和该 VAC 中 BNC 动词频率(1 mg 0.29)。使用 R(Fox,2002 年)中的 rlm 稳健回归法,所有三个预测因子也都有显著的独立贡献。我们使用 R 效果库(Fox,2003 年)绘制了每个预测因子的效果图。图 5 左栏显示了为 VAC 框架生成的动词类型的英语对数 L1 频率与 (i) 该动词类型在该 VAC 中的对数频率的置信区间。
Table 7. Multiple regression summary statistics for the analyses of 131 L1 English respondents and 131 German, Spanish and Czech L2 English respondents 表 7.对 131 名英语为第一语言的受访者和 131 名英语为第二语言的德语、西班牙语和捷克语受访者进行分析的多元回归汇总统计数据
Figure 5. Effect sizes for log 10\log 10 frequencies of verb generated for a VAC frame against (i) log 10\log 10 frequencies of that verb type in that VAC frame in the BNC, (ii) log 10 DeltaPcw\log 10 \Delta \mathrm{Pcw} association strength of that verb given that VAC in the BNC, (iii) log 10\log 10 betweenness centrality of that verb in that VAC semantic network from the BNC data, pooled across the 17 VACs analyzed, for L1 English (left column) and German L2 English (right column). 图 5.一个 VAC 框架产生的动词 log 10\log 10 频率与 (i) BNC 中该 VAC 框架中该动词类型的 log 10\log 10 频率,(ii) BNC 中给定该 VAC 的该动词的 log 10 DeltaPcw\log 10 \Delta \mathrm{Pcw} 关联强度,(iii) BNC 数据中该 VAC 语义网络中该动词的 log 10\log 10 间度中心性的效应大小。
Figure 6. Effect sizes for log10 frequencies of verb generated for a VAC frame against (i) log 10\log 10 frequencies of that verb type in that VAC frame in the BNC, (ii) log 10 DeltaPcw\log 10 \Delta \mathrm{Pcw} association strength of that verb given that VAC in the BNC, (iii) log 10\log 10 betweenness centrality of that verb in that VAC semantic network from the BNC data, pooled across the 17 VACs analyzed, for L1 Spanish (left column) and Czech L2 English (right column). 图 6.一个 VAC 框架生成的动词频率 log10 与(i)BNC 中该 VAC 框架中该动词类型的 log 10\log 10 频率,(ii)BNC 中该 VAC 中该动词的 log 10 DeltaPcw\log 10 \Delta \mathrm{Pcw} 关联强度,(iii)BNC 数据中该 VAC 语义网络中该动词的 log 10\log 10 间度中心性的对比效果大小(17 个 VAC 的分析结果汇总)。
frame in the BNC , (ii) log DeltaPcw\log \Delta \mathrm{Pcw} association strength of that verb given that VAC in the BNC, and (iii) log betweenness centrality of that verb in that VAC semantic network from the BNC data, pooled across the 17 VACs analyzed. (ii)该动词与 BNC 中该 VAC 的 log DeltaPcw\log \Delta \mathrm{Pcw} 关联强度,以及(iii)该动词在 BNC 数据中该 VAC 语义网络中的对数关联度中心度(在所分析的 17 个 VAC 中汇总)。
Now consider the L2 learner groups. Each of the three predictors makes a highly significant independent contribution in explaining the generation data for each language group, German, Spanish, and Czech, at p < .01p<.01. The patterns of Relative Importance are of the same order: Contingency >> Frequency >> Prototypicality. The right column of Figure 5 shows the effects plots for L1 German. The effects plots for L1 Spanish and Czech groups are shown in Figure 6. The influences of the three causal variables are all significant, and are of a similar magnitude, in each of the language groups. 现在来看看 L2 学习者群体。在 p < .01p<.01 时,德语、西班牙语和捷克语这三个预测因子中的每一个都对解释每个语言组的生成数据做出了非常显著的独立贡献。相对重要性模式的顺序相同:情景 >> 频率 >> 原型。图 5 右栏显示的是 L1 德语的效果图。图 6 显示了 L1 西班牙语组和捷克语组的效果图。在每个语言组中,三个因果变量的影响都很显著,且影响程度相似。
3. Discussion 3.讨论
These findings demonstrate that for L1 and advanced L2 speakers alike, particular verbs are associated with skeletal schematic syntactic VAC frames like s/he … of, it … on, etc. Which verbs come to mind when these fluent language users consider these prompts is determined by three factors: 这些研究结果表明,无论是对于第一语言使用者还是高级第二语言使用者,特定的动词都与骨架式句法 VAC 框架相关联,如 s/he ... of、it ... on 等。这些流利的语言使用者在考虑这些提示时会想到哪些动词取决于三个因素:
Entrenchment - verb token frequencies in those VACs in usage experience; 根深蒂固 - 在使用经验中,这些 VAC 中的动词标记频率;
Contingency - how faithful verbs are to particular VACs in usage experience; 应然性--动词在使用经验中对特定 VAC 的忠实程度;
Semantic prototypicality - the centrality of the verb meaning in the semantic network of the VAC in usage experience. 语义原型性--在使用经验中,动词意义在 VAC 语义网络中的中心地位。
We take this as evidence for common processes of construction learning from usage in both first and second language acquisition. Not only do these factors show strong and significant zero-order correlations with productivity in the generative task, but multiple regression analyses also show that they make significant independent contributions. These factors have been implicated in usage-based approaches to SLA (e.g., Ellis, 2002, 2008b), although they have not been properly addressed within the same empirical study: 我们将此视为第一语言和第二语言习得中从用法中学习构词的共同过程的证据。这些因素不仅与生成任务中的生产率有着强烈而显著的零阶相关性,而且多元回归分析也表明它们有着显著的独立贡献。这些因素与基于用法的 SLA 方法(例如,Ellis,2002,2008b)有关,尽管它们尚未在同一项实证研究中得到适当处理:
Effects of frequency of usage upon language learning, ENTRENCHMENT, and subsequent fluency of linguistic processing are well documented and understood in terms of Hebbian learning (Bybee, 2010; Bybee & Hopper, 2001; Ellis, 2002; MacWhinney, 2001). 使用频率对语言学习、"入门 "和后续语言处理流畅性的影响,在希比学习(Hebbee, 2010;Bybee & Hopper, 2001;Ellis, 2002;MacWhinney, 2001)方面已得到充分的记录和理解(Bybee, 2010;Bybee & Hopper, 2001;Ellis, 2002;MacWhinney, 2001)。
Effects of CONTINGENCY of association are also standard fare in the psychology of learning (Rescorla & Wagner, 1972; Shanks, 1995), in the psychology of language learning (Ellis, 2006a, 2006b; MacWhinney, 1987; MacWhinney, Bates, & Kliegl, 1984), and in the particular cases of English VAC acquisition (Ellis & 在学习心理学(Rescorla 和 Wagner,1972 年;Shanks,1995 年)、语言学习心理学(Ellis,2006a,2006b;MacWhinney,1987 年;MacWhinney、Bates 和 Kliegl,1984 年)以及英语 VAC 习得的特殊案例中,联想的 CONTINGENCY 效应也是标准要素(Ellis 和 Wagner,1972 年;Shanks,1995 年;MacWhinney,1987 年;MacWhinney、Bates 和 Kliegl,1984 年)。
Ferreira-Junior, 2009a, 2009b; Ellis & Larsen-Freeman, 2009) and German L2 English learners’ verb-specific knowledge of VACs as demonstrated in priming experiments (Gries & Wulff, 2005, 2009). Ferreira-Junior,2009a,2009b;Ellis & Larsen-Freeman,2009),以及在引物实验(Gries & Wulff,2005,2009)中证明的德国英语中级学习者对动词的特定 VAC 知识。
3. We interpret the effects of semantic prototypicality in terms of the spreading activation theory of semantic memory (Anderson, 1983). The prototype has two advantages. The first is a frequency factor: the greater the token frequency of an exemplar, the more it contributes to defining the category, and the greater the likelihood it will be considered the prototype (Rosch & Mervis, 1975; Rosch et al., 1976). Thus it is the response that is most associated with the VAC in its own right. But beyond that, it gets the network centrality advantage. When any response is made, it spreads activation and reminds other members in the set. The prototype is most connected at the center of the network and, like Rome, all roads lead to it. Thus it receives the most spreading activation. We discuss this further in Ellis et al. (2014). 3.我们根据语义记忆的扩散激活理论(Anderson,1983 年)来解释语义原型的效果。原型有两个优点。首先是频率因素:示例的标记频率越高,它对定义类别的贡献就越大,被视为原型的可能性也就越大(Rosch & Mervis, 1975; Rosch et al.)因此,它本身就是与 VAC 联系最紧密的反应。除此之外,它还具有网络中心性优势。当做出任何反应时,它都会激活并提醒集合中的其他成员。原型在网络中心的连接性最强,就像罗马一样,条条大路通罗马。因此,它得到的传播激活最多。我们将在 Ellis 等人(2014 年)中对此进行进一步讨论。
In the present paper, we investigate L2 constructions in order to relate them to prior work with fluent L1 speakers (Ellis et al., 2014). Like the L1 speakers, and to a similar extent, German, Czech, and Spanish L1 advanced learners of English as an L2 showed independent effects of frequency, contingency, and prototypicality. These findings suggest that the learning of constructions as form-meaning pairs, like the associative learning of cue-outcome contingencies, are affected by factors relating to the form such as type and token frequency; factors relating to the interpretation such as prototypicality and generality of meaning, and factors relating to the contingency of form and function. Language acquisition involves the distributional analysis of the language stream and the parallel analysis of contingent perceptual activity, with abstract constructions being learned from the conspiracy of concrete exemplars of usage following statistical learning mechanisms (Christiansen & Chater, 2001; Rebuschat & Williams, 2012) relating input and learner cognition. 在本文中,我们对 L2 构建进行了研究,以便将其与之前针对流利 L1 说话者的研究(Ellis 等人,2014 年)联系起来。与 L1 说话者一样,德语、捷克语和西班牙语作为 L2 的 L1 高级英语学习者也在类似程度上表现出频率、或然性和原型性的独立效应。这些研究结果表明,作为形式-意义对的结构的学习,就像线索-结果或然性的联想学习一样,受到与形式有关的因素(如类型和标记频率)、与解释有关的因素(如原型性和意义的普遍性)以及与形式和功能的或然性有关的因素的影响。语言习得包括对语言流的分布分析和对或然知觉活动的平行分析,抽象结构是根据统计学习机制(Christiansen 和 Chater,2001 年;Rebuschat 和 Williams,2012 年)从具体的使用范例中学习到的。
However, despite these fundamental similarities with L1A, there are differences, too. Languages lead their speakers to experience different ‘thinking for speaking’ and thus to construe experience in different ways (Slobin, 1996). Learning another language involves learning how to construe the world like natives of the L2, i.e., learning alternative ways of thinking for speaking (Brown & Gullberg, 2008; Brown & Gullberg, 2010; Cadierno, 2008) or learning to ‘rethink for speaking’ (Robinson & Ellis, 2008a). Transfer theories such as the Contrastive Analysis Hypothesis (Gass & Selinker, 1983; James, 1980; Lado, 1957, 1964) hold that L2 learning can be easier where languages use these attention-directing devices in the same way, and more difficult when they use them differently. To the extent that the constructions in L2 are similar to those of L1, L1 constructions can serve as the basis for the L2 constructions, but, because even similar constructions across languages differ in 然而,尽管与 L1A 有这些基本相似之处,但也有不同之处。语言会使说话者经历不同的 "说话思维",从而以不同的方式解释经验(Slobin,1996)。学习另一种语言涉及学习如何像 L2 的本地人一样理解世界,即学习另一种 "说话思维"(Brown & Gullberg, 2008; Brown & Gullberg, 2010; Cadierno, 2008)或学习 "说话时重新思考"(Robinson & Ellis, 2008a)。对比分析假说(Gass & Selinker, 1983; James, 1980; Lado, 1957, 1964)等迁移理论认为,当语言以相同的方式使用这些注意力引导手段时,L2 学习会更容易;而当语言以不同的方式使用这些手段时,L2 学习会更困难。如果 L2 中的构词法与 L1 中的构词法相似,那么 L1 的构词法就可以作为 L2 构词法的基础。
detail, the acquisition of the L2 pattern in all its detail is hindered by the L1 pattern (Cadierno, 2008; Odlin, 1989, 2008; Robinson & Ellis, 2008b). 在细节方面,学习 L2 模式的所有细节都会受到 L1 模式的阻碍(Cadierno, 2008;Odlin, 1989, 2008;Robinson & Ellis, 2008b)。
There is good reason to expect that there will be L1 effects upon L2 VAC acquisition. Languages differ in the ways in which verb phrases express motion events. According to Talmy, 我们有充分的理由相信,L1 会对 L2 的 VAC 习得产生影响。语言在动词短语表达运动事件的方式上存在差异。Talmy 认为
“the world’s languages generally seem to divide into a two-category typology on the basis of the characteristic pattern in which the conceptual structure of the macro-event is mapped onto syntactic structure. To characterize it initially in broad strokes, the typology consists of whether the core schema is expressed by the main verb or by the satellite” (Talmy, 2000, p.221). "根据宏观事件的概念结构映射到句法结构的特征模式,世界上的语言似乎一般分为两类类型。概括地说,这种类型学包括核心模式是由主要动词还是由附属动词来表达"(Talmy,2000 年,第 221 页)。
The “core schema” here refers to the framing event, i.e. the expression of the path of motion. Languages that characteristically map the core schema onto the verb are known as verb-framed languages, those that map the core schema onto the satellite are satellite-framed languages. Included in the former group are Romance and Semitic languages, Japanese, and Tamil. Languages in the latter group include Germanic, Slavic, and Finno-Ugric languages, and Chinese. This means that a Germanic language such as English often uses a combination of verb plus particle (go into, jump over) where a Romance language like Spanish uses a single form (entrar, saltar). 这里的 "核心图式 "指的是框架事件,即运动路径的表达。把核心图式映射到动词上的语言称为动词框架语言,把核心图式映射到卫星上的语言称为卫星框架语言。前一类语言包括罗曼语、闪米特语、日语和泰米尔语。后一类语言包括日耳曼语、斯拉夫语、芬兰乌戈尔语和汉语。这意味着日耳曼语(如英语)通常使用动词加微词的组合形式(go into, jump over),而罗曼语(如西班牙语)则使用单一形式(entrar, saltar)。
Römer, O’Donnell, and Ellis (2014) present detailed quantitative and qualitative analyses of the L2 responses residualized against English native speaker L1 responses (rather than the BNC usage analyses reported here), in order to demonstrate additionally that there are differences in the representation of these VACs in L 2 speakers that result from L1=>L2\mathrm{L} 1 \Rightarrow \mathrm{~L} 2 transfer or “learned attention”. These were particularly apparent in L1 speakers of typologically distinct verb-framed Spanish as opposed to German and Czech, which, like English, are satellite-framed. The German learner responses most closely and the Spanish learner responses least closely match the native speaker responses, with the Czech learner responses falling somewhere between these two groups. This was particularly true for the VACs ’ V against n,’ ‘V among n’, ‘V as n’, ‘V between n’, ‘V in n’, ‘V off n’, ‘V over n’, and ‘V with n’. Römer、O'Donnell 和 Ellis(2014 年)针对英语母语者的 L1 反应(而非此处报告的 BNC 使用分析),对 L2 反应残差进行了详细的定量和定性分析,以额外证明这些 VAC 在 L2 说话者中的表征存在差异,这些差异是由 L1=>L2\mathrm{L} 1 \Rightarrow \mathrm{~L} 2 转移或 "习得性注意 "造成的。这些差异在第一语言为类型学上不同的动词框架的西班牙语学习者中尤为明显,而德语和捷克语则与英语一样,为卫星框架。德语学习者的回答与母语使用者的回答最接近,西班牙语学习者的回答与母语使用者的回答最不接近,而捷克语学习者的回答则介于这两组之间。这种情况在 VAC "V against n"、"V among n"、"V as n"、"V between n"、"V in n"、"V off n"、"V over n "和 "V with n "中尤为明显。
Our findings reflect L2 knowledge of language that comes from usage. The analyses reported here show effects of L2 usage: independent contributions of (i) L2 verb frequency in the VAC, (ii) L2 VAC-verb contingency, and (iii) verb prototypicality in terms of centrality within the L2 VAC semantic network. L2 VAC processing involves rich associations, tuned by L2 verb type and token frequencies and their contingencies of usage, which interface syntax, lexis, and semantics. Yet L2 learners are distinguished from infant L1 acquirers by the fact that they have previously devoted considerable resources to the estimation of the characteristics of another language - the native tongue in which they have considerable fluency. 我们的研究结果反映了来自使用的第二语言知识。本文报告的分析显示了 L2 使用的影响:(i) VAC 中的 L2 动词频率,(ii) L2 VAC-动词或然性,(iii) L2 VAC 语义网络中的中心性动词原型。L2 VAC 处理涉及丰富的关联,由 L2 动词类型和标记频率及其使用的或然性调整,将句法、词汇和语义联系起来。然而,L2 学习者与 L1 初学者的不同之处在于,他们先前已将大量资源用于估计另一种语言的特征--即他们已相当流利使用的母语。
Since they are using the same cognitive apparatus to survey their L 2 too, their inductions are often affected by transfer, with L1-tuned expectations and selective attention (Ellis, 2006b) blinding the computational system to aspects of L2 form and meaning, thus rendering biased estimates from naturalistic usage. So second language constructions reflect usage of L2 and L1 both. 由于他们也在使用相同的认知设备来调查他们的 L2,他们的归纳往往会受到迁移的影响,L1 调整的期望和选择性注意(Ellis,2006b)会使计算系统对 L2 形式和意义的各个方面视而不见,从而对自然用法的估计产生偏差。因此,第二语言结构反映了 L2 和 L1 的用法。
Acknowledgements 致谢
We thank contacts at the following universities who helped with participant recruitment by distributing the survey link: University of Cologne (Germany), University of Giessen (Germany), University of Hanover (Germany), University of Heidelberg (Germany), University of Oldenburg (Germany), University of Trier (Germany), Masaryk University (Czech Republic), Charles University (Czech Republic), University of Extremadura (Spain), University of Granada (Spain), University of Jaen (Spain), University Jaume I of Castellon (Spain), University of Salamanca (Spain), University of Zaragoza (Spain). 我们感谢以下大学的联系人,他们通过分发调查链接帮助我们招募参与者:科隆大学(德国)、吉森大学(德国)、汉诺威大学(德国)、海德堡大学(德国)、奥尔登堡大学(德国)、特里尔大学(德国)、马萨里克大学(捷克共和国)、查尔斯大学(捷克共和国)、埃斯特雷马杜拉大学(西班牙)、格拉纳达大学(西班牙)、哈恩大学(西班牙)、卡斯特利翁豪梅一世大学(西班牙)、萨拉曼卡大学(西班牙)、萨拉戈萨大学(西班牙)。
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Authors' addresses 作者地址
Nick C. Ellis 尼克-C-埃利斯
University of Michigan 密歇根大学
Department of Psychology 心理学系
530 Church St.
Ann Arbor, MI 48109 密歇根州安阿伯市 48109
USA 美国 ncellis@umich.edu
Mathew B. O’Donell 马修-B-奥多内尔
University of Michigan 密歇根大学
Communication Neuroscience Lab, Institute for Social Research 社会研究所传播神经科学实验室
426 Thompson St. 汤普森街 426 号
Ann Arbor, MI 48106 密歇根州安阿伯市 48106
USA 美国 mbod@umich.edu
Ute Römer 乌特-罗默
Georgia State University 佐治亚州立大学
Department of Applied Linguistics and ESL 应用语言学和 ESL 系
34 Peachtree St., Suite 1200 桃树街 34 号 1200 室
Atlanta, GA 30303 佐治亚州亚特兰大 30303
USA 美国 uroemer@gsu.edu