Skill Acquisition Theory accounts for how people progress in learning a variety of skills, from initial learning to advanced proficiency. Skills studied include both cognitive and psychomotor skills, in domains that range from classroom learning to applications in sports and industry. Research in this area ranges from quite theoretical (computational modeling of skill acquisition, the place of skills in an architecture of the mind) to quite applied (how to sequence activities for maximal learning efficiency in areas as diverse as teaching high school algebra, tutoring college physics, coaching professional basketball, or training airplane pilots). 技能习得理论(Skill Acquisition Theory)阐述了人们在学习各种技能时,从初步学习到熟练掌握的过程。所研究的技能包括认知技能和心理运动技能,涉及的领域从课堂学习到体育和工业应用。该领域的研究既有理论性的(技能习得的计算建模、技能在心智结构中的位置),也有应用性的(在高中代数教学、大学物理辅导、职业篮球教练或飞机飞行员培训等不同领域,如何安排活动顺序以达到最高的学习效率)。
The scientific roots of Skill Acquisition Theory are to be found in various branches of psychology, but this research area has proven to be remarkably resilient through various developments in psychology, from behaviorism to cognitivism to connectionism. After all, the practical needs as well as the fundamental theoretical questions and the basic empirical facts remain, regardless of the continuous developments in psychological theory, methodology, and terminology. 技能习得理论的科学根源可在心理学的各个分支中找到,但事实证明,从行为主义到认知主义,再到联结主义,这一研究领域在心理学的各种发展中都具有惊人的生命力。毕竟,无论心理学理论、方法论和术语如何不断发展,实际需求以及基本理论问题和基本经验事实依然存在。
The Theory and Its Constructs 理论及其结构
The basic claim of Skill Acquisition Theory is that the learning of a wide variety of skills shows a remarkable similarity in development from initial representation of knowledge through initial changes in behavior to eventual fluent, spontaneous, largely effortless, and highly skilled behavior, and that this set of phenomena can be accounted for by a set of basic principles common to the acquisition of all skills. The terminology in the previous sentence was deliberately chosen to be nontechnical and theory-neutral; it will come as no surprise that a theory that has been applied to so many domains over such a long period of time has seen its share of technical terms, which have varied with the area of psychology researchers have 技能习得理论的基本主张是,从最初的知识表征到最初的行为变化,再到最终的流畅、自发、基本不费力和高度熟练的行为,各种技能的学习在发展过程中表现出显著的相似性,而这一系列现象都可以用所有技能习得的一套共同基本原则来解释。前一句话中的术语是特意选择的,目的是为了非技术性和理论中立性;对于一个在如此长的时间内被应用于如此多领域的理论来说,出现一些技术性术语并不奇怪,这些术语随着研究人员所研究的心理学领域的不同而有所不同。
worked in and the types of skills they have studied. Generally speaking, however, researchers have posited three stages of development, whether they called them cognitive, associative, and autonomous, as Fitts and Posner (1967); or declarative, procedural, and automatic, as Anderson (e.g., Anderson, 1982, 1993, 2007; Anderson et al., 2004; Taatgen, Huss, Dickison, & Anderson, 2008); or presentation, practice, and production, as Byrne (1986). 然而,一般来说,研究者们都假设了三个发展阶段。不过,一般来说,研究者们都假设了三个发展阶段,无论是像 Fitts 和 Posner(1967 年)那样称之为认知、联想和自主阶段,还是像 Anderson(例如,Anderson,1982 年,1993 年,2007 年;Anderson 等人,2004 年;Taatgen、Huss、Dickison 和 Anderson,2008 年)那样称之为陈述、练习和生产阶段,还是像 Byrne(1986 年)那样称之为呈现、练习和生产阶段。
These three stages are characterized by large differences in the nature of knowledge and its use, as reflected in various ways through introspection, verbalization, and most importantly various aspects of behavior especially under demanding conditions. Initially, a student, learner, apprentice, or trainee may acquire quite a bit of knowledge ABOUT a skill without ever even trying to use it. That knowledge may be acquired through perceptive observation and analysis of others engaged in skilled behavior (e.g., learning a new dance move), but most often is transmitted in verbal form from one who knows to one who does not (as in a parent or driving instructor teaching a teenager how to drive a car), and often through a combination of the two, when the “expert” demonstrates the behavior slowly while commenting on the relevant aspects (e.g., teaching a child how to swim or how to play tennis). 这三个阶段的特点是,知识的性质和知识的使用存在很大差异,这些差异通过内省、口头表达以及最重要的行为(尤其是在苛刻条件下的行为)的各个方面反映出来。起初,学生、学习者、学徒或受训者可能在没有尝试使用某种技能的情况下,就已经掌握了很多有关该技能的知识。这些知识可能是通过对他人技能行为的感知观察和分析而获得的(例如,学习新的舞蹈动作),但最常见的是以口头形式从知道的人传授给不知道的人(例如,父母或驾驶教练教青少年如何驾驶汽车),而且往往是通过两者的结合,当 "专家 "慢慢地演示行为,同时对相关方面进行评论(例如,教孩子如何游泳或如何打网球)。
Next comes the stage of “acting on” this knowledge, turning it into a behavior, turning “knowledge that” into “knowledge how,” or in more technical terms, turning declarative knowledge into procedural knowledge. This proceduralization of knowledge is not particularly arduous or time consuming. Provided that the relevant declarative knowledge is available and drawn on in the execution of the target behavior, proceduralization can be complete after just a few trials/ instances. Anderson et al. (2004, p. 1046), for instance, point out that, in a typical psychology experiment, the participant is converting from a declarative representation and a slow interpretation of the task (as set forth in the experimenter’s instructions) to a smooth, rapid, procedural execution of the task (for an example in second language learning, see DeKeyser, 1997, who argues that proceduralization was essentially complete after the first 16-item block of practice items). Yet, proceduralized knowledge has a big advantage over declarative knowledge: It no longer requires the individual to retrieve bits and pieces of information from memory to assemble them into a “program” for a specific behavior; instead, that “program” is now available as a ready-made chunk (as a result of production compilation; see Anderson, 2007; Taatgen & Lee, 2003) to be called up in its entirety each time the conditions for that behavior are met. 接下来是 "运用 "这些知识的阶段,将知识转化为行为,将 "知识是什么 "转化为 "知识怎么做",或者用更专业的术语来说,将陈述性知识转化为程序性知识。这种知识的程序化并不特别艰巨或耗时。只要有相关的陈述性知识,并在执行目标行为的过程中加以利用,程序化只需几次试验/实例就能完成。例如,安德森等人(2004 年,第 1046 页)指出,在一个典型的心理学实验中,被试正在从陈述性表征和对任务的缓慢解释(如实验者的指令所规定的)转换为顺利、快速、程序化地执行任务(关于第二语言学习中的一个例子,请参见 DeKeyser, 1997,他认为程序化在第一个 16 个项目的练习后基本上就完成了)。然而,与陈述性知识相比,程序化知识有一个很大的优势:它不再需要个人从记忆中检索零碎的信息,将其组合成特定行为的 "程序";相反,该 "程序 "现在可以作为一个现成的片段(作为生产编译的结果;见 Anderson, 2007; Taatgen & Lee, 2003),在每次满足该行为的条件时被完整地调用。
Once procedural knowledge has been acquired, there is still a long way to go before the relevant behavior can be consistently displayed with complete fluency or spontaneity, rarely showing any errors. In other words, the knowledge is not yet robust and fine-tuned. A large amount of practice is needed to decrease the time required to execute the task (“reaction time”), the percentage of errors (“error rate”), and the amount of attention required (and hence interference with/from other tasks, or more generally “robustness”; cf. Taatgen et al., 2008). This practice leads to 一旦掌握了程序性知识,要想完全流畅或自发地持续表现出相关行为,很少出现任何错误,还有很长的路要走。换句话说,这些知识还不够强大和完善。要减少执行任务所需的时间("反应时间")、错误的百分比("错误率")和所需的注意力(从而减少对其他任务的干扰,或更广泛地说 "稳健性";参见 Taatgen 等人,2008 年),就需要大量的练习。这种做法导致
gradual automatization of knowledge. Automaticity is not an all-or-nothing affair; even highly automatized behaviors are not 100%100 \% automatic, as becomes clear when we stumble walking down the stairs, when we realize we are driving too fast when engaged in an exciting conversation with a passenger, or when we stumble over our words while uttering a simple sentence in our native language. 知识逐渐自动化。自动性并不是全有或全无的事情;即使是高度自动化的行为也不是 100%100 \% 自动的,当我们跌跌撞撞地走下楼梯时,当我们在与乘客进行激动人心的谈话时意识到自己开得太快时,或者当我们在用母语说出一个简单的句子时磕磕绊绊地说话时,就会清楚地看到这一点。
It should be stressed that this intensive practice (sometimes called overlearning) after mastery over the task has been achieved is only useful if it takes learners from the proceduralization stage (where declarative and procedural knowledge are used) to the automatization stage (where knowledge is completely procedural already). In such cases, however, its impact is great, not only because of the obvious immediate advantages of reaching high levels of automaticity, but also because procedural knowledge is known to decay less with time. On the other hand, while some tasks can be carried out completely on the basis of procedural knowledge (esp. motor skills), others keep requiring access to at least some declarative information, and hence benefit less from overlearning (Kim, Ritter, & Koubek, 2013). 需要强调的是,只有在学习者从程序化阶段(陈述性知识和程序性知识并用)进入自动化阶段(知识已经完全程序化)时,这种在掌握任务之后的强化练习(有时称为过度学习)才是有用的。然而,在这种情况下,它的影响是巨大的,这不仅是因为达到高水平的自动化具有明显的直接优势,而且还因为程序性知识随着时间的推移衰减较少。另一方面,虽然有些任务可以完全依靠程序性知识来完成(尤其是运动技能),但有些任务仍然至少需要一些陈述性的信息,因此从过度学习中获益较少(Kim, Ritter, & Koubek, 2013)。
A central concept in the study of skill acquisition is the power law of learning (named this way because its mathematical formalization is a power function: an equation with an exponent, which in this case represents the amount of practice). This equation formalizes mathematically what has been observed many times, for skills as different as making cigars out of tobacco leaves or writing computer programs: that both reaction time and error rate decrease systematically as a consequence of practice. If the learning curves for reaction time and error rate for such a variety of skills share the very specific shape of a power function (and not even a quite similar one like that of an exponential function), then this shape must contain the key to some fundamental learning mechanisms. Since Newell and Rosenbloom’s (1981) seminal article on the power law, a variety of hypotheses have been formulated to explain this robust empirical phenomenon. This chapter is not the place to discuss the relative merit of these hypotheses (for more discussion, see DeKeyser, 2001; Segalowitz, 2010), but what they all have in common is that they posit a qualitative change over time, as a result of practice, in the basic cognitive mechanisms used to execute the same task. What superficially seems like a set of smooth quantitative changes (reaction time and error rate declining following a power function) in fact reflects a qualitative change in mechanisms of knowledge retrieval, quite radical for a while, and then gradually stabilizing without ever reaching an absolute endpoint (hence the learning curve in the specific shape of a power function illustrated in Figure 6.1). 技能学习研究的一个核心概念是学习幂律(之所以这样命名,是因为其数学形式化是一个幂函数:一个带有指数的等式,在这里指数代表练习量)。这个等式用数学形式表述了人们在用烟叶制作雪茄或编写计算机程序等不同技能中多次观察到的现象:反应时间和错误率都会随着练习的进行而系统地降低。如果对如此多种技能的反应时间和错误率的学习曲线都具有非常特殊的幂函数形状(而不是指数函数那样的非常相似的形状),那么这种形状一定包含了某些基本学习机制的关键。自纽厄尔和罗森布洛姆(1981)发表关于幂律的开创性文章以来,人们提出了各种假设来解释这一强大的经验现象。本章并不是讨论这些假说相对优劣的地方(更多讨论,请参见 DeKeyser, 2001;Segalowitz, 2010),但它们的共同点是,它们都认为随着时间的推移,用于执行相同任务的基本认知机制会随着练习而发生质的变化。表面上看似一系列平滑的量变(反应时间和错误率随幂函数下降),实际上反映了知识检索机制的质变,这种质变在一段时间内相当激进,然后逐渐稳定下来,但从未达到绝对终点(因此,学习曲线的具体形状如图 6.1 所示的幂函数)。
Probably the most widely accepted interpretation of this change is that it represents first a shift from declarative to procedural knowledge (achieved rather quickly, hence the rather steep initial section of the curve) followed by a much slower process of automatization of procedural knowledge. The term automatization itself can be interpreted in various ways, ranging from a mere speed-up of the same basic mechanisms to a speed-up of a broader task through a qualitative change in its components. Again, we are not taking a position here on this point 对这一变化最广为接受的解释可能是,它首先代表了从陈述性知识到程序性知识的转变(实现得相当快,因此曲线的最初部分相当陡峭),然后是一个缓慢得多的程序性知识自动化过程。自动化一词本身可以有多种解释,从仅仅加快相同基本机制的速度,到通过其组成部分的质变加快更广泛任务的速度,不一而足。同样,我们在此不对这一点采取立场
FIGURE 6.1 A sample graph of the power of learning curve. 图 6.1 学习力曲线示例图。
either (for more discussion, see DeKeyser, 2007a; Segalowitz, 2010), but we are using automatization in a more specific sense than just “improvement through practice,” because we are reserving the term for the latter, flatter part of the learning curve, after the steep decline due to rapid proceduralization has taken place (see Figure 6.1). 但我们使用自动化的意义比单纯的 "通过实践提高 "更为具体,因为我们将该术语保留在学习曲线的后半段,即由于快速程序化而急剧下降之后的较平缓部分(见图 6.1)。
Another point on which there is widespread agreement is that, regardless of the exact nature of the knowledge drawn on in the later stages of development, this knowledge is much more specific than at the beginning, and in fact, so highly specific that it does not transfer well, even to what may seem quite similar tasks. A well-known example from the skill acquisition literature is reading versus writing computer programs (see Singley & Anderson, 1989), and an obvious parallel in the domain of language learning is comprehension versus production (De Jong, 2005; DeKeyser, 1997; DeKeyser & Sokalski, 2001; Shintani, Li, & Ellis, 2013; Tanaka, 2001). Other examples, of course, would be transfer from speaking to writing, or from one situation to another (such as from orderly dialogue to argument with multiple interlocutors or from the kitchen table to the boardroom). The implication for training is that two kinds of knowledge need to be fostered, both highly specific procedural knowledge, highly automatized for efficient use in the situations that the learner is most likely to confront in the immediate future, and solid abstract declarative knowledge that can be called upon to be integrated into much 另一个得到广泛认同的观点是,无论在发展的后期阶段所汲取的知识的确切性质如何,这些知识都要比初期阶段具体得多,事实上,这些知识的具体性如此之高,以至于它们不能很好地迁移,甚至不能迁移到看似十分相似的任务中去。技能习得文献中一个著名的例子是阅读与编写计算机程序(见 Singley & Anderson, 1989),语言学习领域一个明显的平行例子是理解与制作(De Jong, 2005; DeKeyser, 1997; DeKeyser & Sokalski, 2001; Shintani, Li, & Ellis, 2013; Tanaka, 2001)。当然,其他例子还包括从口语到写作的转换,或从一种情境到另一种情境的转换(如从有序对话到与多个对话者的争论,或从厨房餐桌到会议室)。对培训的影响是,需要培养两种知识,一种是高度具体的程序性知识,这种知识高度自动化,可在学习者近期最有可能面对的情况下有效使用;另一种是扎实的抽象的陈述性知识,这种知识可被要求整合到更多的情况中。
broader, more abstract procedural rules, which are indispensable when confronting new contexts of use. 更广泛、更抽象的程序规则,在面对新的使用环境时是不可或缺的。
What is often overlooked is that this whole sequence of proceduralization and automatization cannot get started if the right conditions for proceduralization are not present (the declarative knowledge required by the task at hand and a task setup that allows for use of that declarative knowledge). Anderson, Fincham, and Douglass (1997), in particular, show convincingly that the combination of abstract rules and concrete examples is necessary to get learners past the declarative threshold into proceduralization. DeKeyser (2007b) argues that precisely this is often lacking in language teaching in general and in preparing students for maximum benefit from a stay abroad in particular. 经常被忽视的是,如果不具备程序化的适当条件(手头任务所需的陈述性知识和允许使用陈述性知识的任务设置),程序化和自动化的整个序列就无法启动。安德森、芬查姆和道格拉斯(1997)的研究尤其令人信服地表明,要使学习者跨越陈述性门槛,进入程序化阶段,就必须将抽象规则和具体实例结合起来。DeKeyser (2007b) 认为,在一般的语言教学中,尤其是在培养学生从海外学习中获得最大收益的过程中,往往缺乏的正是这一点。
What Counts as Evidence? 什么算证据?
The oldest form of evidence in this area is behavioral in nature: reaction times, error rates, and differences in performance from one condition to another such as interference from a secondary task. Any overview of the behavioral data should start with Newell and Rosenbloom (1981), not because it was the first study in this area but because it was seminal in that it brought together empirical data from so many different studies about so many different forms of skill acquisition and proposed both a quantitative model (the power law) and a qualitative interpretation for this mountain of data. Some of the domains of learning included motor behavior, reading, decision making, and problem solving. For information on the individual studies included, see Newell and Rosenbloom’s article. Major empirical studies since then include Anderson et al. (1997) on the role of rules and examples in the proceduralization of a simple reasoning task, and Logan(1988,1992,2002)\operatorname{Logan}(1988,1992,2002) on the learning of a new form of arithmetic (with letters), and Taatgen et al. (2008) on robustness and flexibility of procedural knowledge as a function of the form of the production rules (with or without explicit statement of pre- and postconditions in the environment). 该领域最古老的证据形式是行为性质的:反应时间、错误率以及不同条件下的表现差异,如来自次要任务的干扰。对行为数据的任何概述都应从纽厄尔和罗森布洛姆(1981 年)的研究开始,这并不是因为该研究是这一领域的第一项研究,而是因为该研究具有开创性意义,因为它汇集了来自众多不同研究的经验数据,涉及众多不同形式的技能习得,并提出了一个定量模型(幂律)和对大量数据的定性解释。其中一些学习领域包括运动行为、阅读、决策制定和问题解决。有关个别研究的信息,请参阅 Newell 和 Rosenbloom 的文章。此后的主要实证研究包括安德森等人(1997 年)关于规则和示例在简单推理任务程序化中的作用的研究、 Logan(1988,1992,2002)\operatorname{Logan}(1988,1992,2002) 关于一种新算术形式(用字母)的学习的研究,以及塔特根等人(2008 年)关于程序性知识的稳健性和灵活性与生产规则形式(是否明确说明环境中的前置条件和后置条件)的函数的研究。
In the last 25 years, less direct evidence in the form of computational modeling has become very important in the study of skill acquisition, even more so than in other subfields of psychology. This line of evidence includes large amounts of work with a variety of computer models such as the various consecutive incarnations of ACT (see especially Anderson, 1993, 2007; Anderson & Lebiere, 1998; Anderson et al., 2004; Taatgen & Anderson, 2008), EPIC (Meyer & Kieras, 1997), SOAR (Newell, 1990) and 3CAPS (Just & Carpenter, 1992). In all such models, the aim is to show how a cognitive mechanism can work and with which implications for reaction time and error rate, but of course the model never proves that the processes taking place in the human mind are the same. 在过去的 25 年里,以计算模型为形式的不太直接的证据在技能习得的研究中变得非常重要,甚至比心理学的其他分支领域更为重要。这些证据包括各种计算机模型的大量工作,如 ACT 的各种连续化身(尤其见 Anderson, 1993, 2007; Anderson & Lebiere, 1998; Anderson 等人, 2004; Taatgen & Anderson, 2008)、EPIC (Meyer & Kieras, 1997)、SOAR (Newell, 1990) 和 3CAPS (Just & Carpenter, 1992)。在所有这些模型中,目的都是为了说明认知机制如何发挥作用,以及对反应时间和出错率的影响,但当然,这些模型从未证明在人类头脑中发生的过程是相同的。
During this same period, skill acquisition researchers have begun to draw on what some would see as data that are even more direct than the behavioral data themselves, that is, neuroimaging and other forms of neurological evidence such 在同一时期,技能习得研究人员开始利用一些人认为比行为数据本身更直接的数据,即神经影像学和其他形式的神经学证据,如
as evoked potentials (measures of electrophysiological activity in specific areas of the brain, experimentally linked to specific cognitive tasks). 如诱发电位(测量大脑特定区域的电生理活动,通过实验与特定的认知任务相联系)。
Increasing use of techniques from cognitive neuroscience has yielded studies such as Raichle et al. (1994) using PET (positron emission tomography) to trace the effect of practice on the relative involvement of different brain areas in the same task (word generation), and Qin, Anderson, Silk, Stenger, and Carter (2004) using fMRI (functional magnetic resonance imaging) to investigate the effect of children’s practice in algebra. (For discussion of the role of neuroimaging, and fMRI in particular, in the development of skill acquisition models, see esp. Anderson, 2007, Chapter 2 and pp. 169-181; see also Chein & Schneider, 2005; Hill & Schneider, 2006, for a broader discussion of neuroimaging of skill development.) 认知神经科学技术的使用日益增多,产生了一些研究成果,如 Raichle 等人(1994 年)使用 PET(正电子发射断层扫描)追踪练习对同一任务(单词生成)中不同脑区相对参与的影响,以及 Qin、Anderson、Silk、Stenger 和 Carter(2004 年)使用 fMRI(功能性磁共振成像)研究儿童练习代数的影响。(关于神经影像学,尤其是 fMRI 在技能习得模型开发中的作用的讨论,请参见 Anderson, 2007, 第 2 章和第 169-181 页;关于技能开发神经影像学的更广泛讨论,请参见 Chein & Schneider, 2005; Hill & Schneider, 2006)。
In sum, the behavioral data show the similarity in skill development across different cognitive domains (how reaction time and error rate develop as a result of practice); the neurological data show how different areas of the brain are involved to a different extent after different amounts of practice; and the computational models show the hypothetical inner workings of the mechanisms that cannot be observed directly through behavioral or neurological data. 总之,行为学数据显示了不同认知领域技能发展的相似性(反应时间和错误率是如何随着练习而发展的);神经学数据显示了不同练习量后大脑不同区域参与程度的不同;而计算模型则显示了无法通过行为学或神经学数据直接观察到的机制的假设内部运作。
As should be clear from the literature cited earlier, evidence for central constructs such as the power law, procedural knowledge, or automatization abounds in the psychological literature. What is harder to come by is empirical data that unambiguously point to a specific interpretation of these phenomena in terms of learning mechanisms. More importantly for our purposes here, very little research in the field of second language learning has explicitly set out to gather data from second language learners to test (a specific variant of) Skill Acquisition Theory. 从前面引用的文献中可以清楚地看到,心理学文献中关于幂律、程序性知识或自动化等核心概念的证据比比皆是。但更难得的是,有经验数据能明确指出这些现象在学习机制方面的具体解释。更重要的是,在第二语言学习领域,很少有研究明确地从第二语言学习者那里收集数据来检验技能习得理论的(特定变体)。
The same can be said about other directions in which skill acquisition research has expanded in recent years: the study of the forgetting of skills and the role of distributed versus massed practice in learning and forgetting. The longstanding topic of what constitutes ideal distribution of practice has been revived in the cognitive and educational psychology literature in the last decade, and the results of individual studies often appear contradictory, but a provisional conclusion from this literature as a whole (see esp. the meta-analysis in Cepeda et al., 2006; the literature review in Carpenter et al., 2012; and the studies by Cepeda et al., 2009; Rohrer & Pashler, 2007) is that the ideal spacing of practice is determined by the ratio of inter-session interval (the amount of time between different encounters with the same item) and retention interval (the amount of time between the end of practice and the beginning of testing). 近年来,技能习得研究的其他方向也在不断扩展:技能遗忘的研究,以及分布式练习与大规模练习在学习和遗忘中的作用。过去十年中,认知和教育心理学文献中重新出现了关于什么是理想的分布式练习这一由来已久的话题,单个研究的结果往往看似相互矛盾,但从这些文献的整体中可以得出一个临时结论(参见Cepeda等人的荟萃分析,2006年;Carnp等人的文献综述,2006年)、2006;Carpenter 等人的文献综述,2012;以及 Cepeda 等人的研究,2009;Rohrer & Pashler,2007),理想的练习间隔是由练习间隔(与同一项目不同练习之间的时间间隔)和保持间隔(练习结束到测试开始之间的时间间隔)的比率决定的。
On this point, too, the SLA literature is still rather limited. On one hand, studies on complete foreign language programs (Collins et al., 1999; Lightbown & Spada, 1994;Serrano, 2011;Serrano & Muñoz, 2007; White & Turner, 2005) have shown massed practice to be more effective. Much more narrowly focused studies, on the other hand, have come to divergent conclusions. Bird (2010) found distributed practice to be superior for past tense practice in ESL, and Nakata (2012) obtained similar results for vocabulary learning in ESL. Suzuki and DeKeyser (in press), 在这一点上,SLA 文献也仍然相当有限。一方面,关于完整外语课程的研究(Collins 等人,1999 年;Lightbown & Spada,1994 年;Serrano,2011 年;Serrano & Muñoz,2007 年;White & Turner,2005 年)表明,大规模练习更为有效。另一方面,范围更窄的研究却得出了不同的结论。伯德(Bird,2010 年)发现,在 ESL 中进行过去式练习时,分散练习更有优势;中田(Nakata,2012 年)在 ESL 中进行词汇学习时也得出了类似的结果。Suzuki 和 DeKeyser(出版中)、
however, in a study which was narrowly focused on the “gerund” in Japanese SL but still required integration of grammatical skills and vocabulary knowledge, found that massed practice was best for the acquisition of procedural skill; they also found that memory was more important in massed practice and analytical ability more in distributed practice. Finally, long-term studies on forgetting of L2 skills among foreign language learners (as opposed to heritage learners or fluent second language speakers) are rare (see, however, Bahrick, Hall, Goggin, Bahrick, & Berger, 1994; Bahrick, Hall, & Baker, 2013); none have taken a skill acquisition perspective. 然而,在一项以日语 SL 中的 "动名词 "为研究对象,但仍要求整合语法技能和词汇知识的研究中,他们发现,大量练习最有利于程序性技能的习得;他们还发现,记忆在大量练习中更为重要,而分析能力在分散练习中更为重要。最后,关于外语学习者(相对于传统语言学习者或流利的第二语言使用者)第二语言技能遗忘的长期研究并不多见(参见Bahrick, Hall, Goggin, Bahrick, & Berger, 1994; Bahrick, Hall, & Baker, 2013);这些研究都没有从技能习得的角度出发。
One of the reasons why research from a skill acquisition perspective is so rare in the field of second language acquisition is the methodology required. Experiments on skill acquisition typically involve rather large numbers of participants over rather long periods of time, yielding very large amounts of data for statistical analysis. Moreover, the collection of these data, and the control required over the treatments and practice conditions requires a certain amount of investment in hardware and software. 在第二语言习得领域,从技能习得角度进行的研究如此罕见,原因之一是所需的方法。技能习得实验通常需要大量的参与者和较长的时间,从而产生大量的数据用于统计分析。此外,这些数据的收集以及对处理和练习条件的控制都需要一定的硬件和软件投资。
This methodological challenge, combined with the fact that focus on form was out of fashion for a number of years in applied linguistics research, explains the small volume of directly relevant empirical research so far. The studies that have tested the predictions of skill acquisition most directly are DeKeyser (1997), Robinson (1997), de Jong (2005), De Jong and Perfetti (2011), and Rodgers (2011). The first two each test one of two competing theories of skill acquisition with L2 data: DeKeyser (1997) found that the concepts of proceduralization, automatization, and specificity of procedural rules accounted well for the learning curves for reaction time and error rate during a semester of practice of a small number of grammar rules. Robinson (1997), on the other hand, found that his data on the learning of an ESL grammar rule did not fit the predictions of Logan’s competing theory of automatization through retrieval of specific instances from memory instead of rules. 这种方法论上的挑战,再加上应用语言学研究多年来对形式的关注已经过时,这就是迄今为止直接相关的实证研究数量较少的原因。最直接检验技能习得预测的研究有 DeKeyser (1997)、Robinson (1997)、de Jong (2005)、De Jong 和 Perfetti (2011) 以及 Rodgers (2011)。前两者分别用 L2 数据检验了两种相互竞争的技能习得理论中的一种:DeKeyser (1997) 发现,程序规则的程序化、自动化和特异性概念很好地解释了在一个学期的练习中对少量语法规则的反应时间和错误率的学习曲线。另一方面,Robinson(1997)发现,他学习 ESL 语法规则的数据并不符合 Logan 的竞争理论的预测,即通过从记忆中检索具体实例而不是规则来实现自动化。
De Jong (2005), with learners of Spanish as a second language, provides further evidence for the skill specificity documented by DeKeyser (1997). She showed that extensive aural comprehension training, while increasing processing speed in comprehension, did not preempt a substantial number of errors in production and that, conversely, early production did not hinder acquisition. Rodgers (2011) worked with learners of Italian L2 to show that automatization of verbal morphology developed as a function of practice but that it was less advanced in production than in comprehension, providing further evidence still for the specificity of procedural knowledge. Zooming in on proceduralization, De Jong and Perfetti (2011) showed in detail how indices of proceduralization such as length of runs and phonation/time ratio develop as a result of repeated but gradually sped-up practice with a task, either identical or similar. (This study is described in more detail in the boxed inset.) De Jong(2005 年)以西班牙语作为第二语言的学习者为研究对象,进一步证明了 DeKeyser(1997 年)所记录的技能特殊性。她的研究表明,大量的听力理解训练在提高理解处理速度的同时,并不能避免大量的发音错误,反之,早期的发音并不会阻碍学习。罗杰斯(Rodgers,2011 年)通过对意大利语第二语言学习者的研究表明,动词形态的自动化是随着练习的进行而发展起来的,但在制作中的自动化程度不如在理解中的自动化程度高,这为程序性知识的特殊性提供了进一步的证据。De Jong 和 Perfetti(2011 年)对程序化进行了深入研究,详细说明了程序化的指标,如运行长度和发音/时间比,是如何通过重复但逐渐加快的相同或相似任务练习而发展起来的。(这项研究的更多细节见方框内插图)。
Given the increasing sophistication of the technology as well as the research methodology at the disposal of second language researchers, along with a return 鉴于第二语言研究人员所掌握的技术和研究方法日趋成熟,加上
to focus on form and explicit learning in recent years (see, e.g., DeKeyser, 2003; Doughty, 2001; Doughty & Williams, 1998; R. Ellis, 2012; Norris & Ortega, 2000; Spada & Tomita, 2010), one can expect this area of research to pick up, especially as many researchers have begun to at least interpret existing findings from the second language literature within the framework of Skill Acquisition Theory (de Bot, 1996; Healy et al., 1998; Lyster, 2004; Lyster & Sato, 2013; Macaro, 2003; O’Malley & Chamot, 1990; Ranta & Lyster, 2007; Sato & Lyster, 2012; Towell & Hawkins, 1994; Towell, Hawkins, & Bazergui, 1996). Researchers do not need to be trained in computational modeling or neuroscience at all to contribute to research on skill acquisition; with a sophisticated approach to design, data collection, and data analysis, using technology that is fairly easily available at research institutions, behavioral data still have much to contribute to this area. 近年来,随着对形式和显性学习的关注(参见 DeKeyser, 2003; Doughty, 2001; Doughty & Williams, 1998; R. Ellis, 2012; Norris & Ortega, 2000; Spada & Tomita, 2010),我们可以预期这一领域的研究将有所加强,特别是许多研究人员已经开始至少在技能习得理论的框架内解释第二语言文献中的现有研究成果(de Bot, 1996; Healy et al、1998; Lyster, 2004; Lyster & Sato, 2013; Macaro, 2003; O'Malley & Chamot, 1990; Ranta & Lyster, 2007; Sato & Lyster, 2012; Towell & Hawkins, 1994; Towell, Hawkins, & Bazergui, 1996)。研究人员根本不需要接受过计算建模或神经科学方面的培训,就可以为技能习得研究做出贡献;只要采用复杂的方法进行设计、数据收集和数据分析,并使用研究机构相当容易获得的技术,行为数据仍然可以为这一领域做出很多贡献。
Common Misunderstandings 常见误解
Two kinds of misunderstanding about the contribution of Skill Acquisition Theory to second language acquisition research are very common: the idea that skill acquisition either explains everything about second language acquisition or nothing, in other words, that it competes with other theories to be the one and only valid explanation of the set of phenomena we call “second language acquisition,” and the idea that it is incompatible with a variety of empirical findings in the field. These two misunderstandings are, of course, related, as we see later. 关于技能习得理论对第二语言习得研究的贡献,有两种误解非常普遍:一是认为技能习得理论要么能解释第二语言习得的一切,要么什么都不能解释,换句话说,它与其他理论竞争,成为对我们称之为 "第二语言习得 "的一系列现象的唯一有效解释;二是认为它与该领域的各种实证研究结果不相容。当然,这两种误解是相互关联的,我们稍后会看到。
Related to overgeneralization of Skill Acquisition Theory to the situations where it does not apply well is the tendency to see the theory as incompatible with a number of empirical findings as well as theoretical positions in the field. Some will overinterpret the theory as predicting that any kind of construction can be learned, practiced, and automatized by anybody in any order and that therefore it is incompatible with the literature on the natural order of acquisition (summarized, e.g., in Dulay, Burt, & Krashen, 1982; Goldschneider & DeKeyser, 2001; Luk & Shirai, 2009; see also Chapter 2). This reasoning actually combines a misreading of both Skill Acquisition Theory and research on the natural order of acquisition, because the latter never found an ordering for all or even most structures in the language, only for a few morphemes in some studies or for a few closely related syntactic patterns in others, and because most studies of order of acquisition were carried out with learners who had massive exposure to the language and/or were young learners, which means that they were largely implicit learners and that the skill acquisition model (going from declarative/explicit to procedural/implicit knowledge) did not apply to them. 与技能习得理论过度泛化到其不适用的情况有关的是,人们倾向于认为该理论与该领域的一些经验性发现和理论立场不相容。有些人会过度解读该理论,认为任何人都可以按照任何顺序学习、练习和自动化任何类型的结构,因此该理论与关于习得的自然顺序的文献不符(例如,Dulay, Burt, & Krashen, 1982; Goldschneider & DeKeyser, 2001; Luk & Shirai, 2009; 另见第2章)。这种推理实际上是对技能习得理论和自然习得顺序研究的误读,因为自然习得顺序研究从未发现过语言中所有甚至大多数结构的习得顺序,只是在某些研究中发现了少数语素的习得顺序,或在另一些研究中发现了少数密切相关的句法模式的习得顺序、而且,大多数关于习得顺序的研究都是在学习者大量接触语言和/或年轻学习者的情况下进行的,这意味着他们大多是内隐学习者,技能习得模式(从陈述性/显性知识到程序性/隐性知识)并不适用于他们。
Similarly, Skill Acquisition Theory should not be seen as being in competition with the theory underlying processing instruction (see esp. VanPatten, 2004), as long as the latter is not seen as implying that practice in production is not important for full-fledged skill acquisition or the fine-tuning of declarative knowledge; in fact, processing instruction does for comprehension skills exactly what Skill Acquisition Theory suggests should be done: taking students from explicitly taught (or induced) declarative knowledge, through careful proceduralization by engaging in the relevant task while the declarative knowledge is maximally activated, to (very initial stages of) automatization. Skill Acquisition Theory is not incompatible either with other contemporary tendencies in the way focus on form is implemented, such as task-based learning (see esp. R. Ellis, 2003; Long & Norris 2000; Long & Robinson, 1998; Robinson, 2011; Van den Branden, 2006), because engaging in carefully sequenced tasks (from a psycholinguistic perspective) will again lead to proceduralization and potentially some degree of automatization provided that the requisite declarative knowledge is at the disposal of the learner during the task. Nor does Skill Acquisition Theory contradict the notion that implicit learning is important (leading directly to implicit knowledge, that is, knowledge that one is not aware of, which is stressed both in the universal grammar approach [see Chapter 3] and the usage-based approach to learning [see Chapter 5]). While stressing the importance of implicit learning in general and frequency in particular, N. Ellis (see esp. N. Ellis, 2002, 2005; see also Chapter 5) makes it very clear that “many aspects of a second language are unlearnable-or at best acquired very slowly—from implicit processes alone” (N. Ellis, 2005, p. 307), and that “slot-and-frame patterns, drills, mnemonics, and declarative statements 同样,技能习得理论也不应被视为与加工教学的基础理论相冲突(参见 VanPatten, 2004)。事实上,加工教学对理解技能的作用正是技能习得理论所建议的:将学生从明确传授(或诱导)的陈述性知识,通过在陈述性知识被最大限度激活时参与相关任务的谨慎程序化,带入(非常初始的)自动化阶段。技能习得理论与当代其他注重形式的学习方式,如基于任务的学习(参见 R. Ellis, 2003; Long & Norris 2000; Long & Robinson, 1998; Robinson, 2011; Van den Branden, 2006)并不冲突,因为只要学习者在任务过程中掌握了必要的陈述性知识,那么(从心理语言学的角度来看)参与精心排序的任务将再次导致程序化和潜在的某种程度的自动化。技能习得理论与内隐学习非常重要(直接导致内隐知识,即一个人没有意识到的知识,这一点在通用语法方法[见第 3 章]和基于用法的学习方法[见第 5 章]中都得到了强调)这一概念也并不矛盾。在强调内隐学习的重要性,特别是频率的重要性的同时,N. Ellis(见 esp. Ellis, 2002, 2005;另见第 5 章)明确指出,"第二语言的许多方面是无法学习的,或者说,仅从内隐过程中就能非常缓慢地习得"(N. Ellis, 2005, p.)。307),以及 "槽框模式、练习、记忆法和陈述性语句
of pedagogical grammar . . . all contribute to the conscious creation of utterances that then partake in subsequent implicit learning and proceduralization” (p. 308). 教学语法......都有助于有意识地创造语篇,然后参与随后的内隐学习和程序化"(第 308 页)。
Finally, perhaps the most common misunderstanding concerns the concept of declarative knowledge “turning into” procedural knowledge. This is not meant to suggest that any mysterious transformation or move happens in the brain (for more about the declarative/procedural distinction and the brain, see Ullman, 2004; see also Chapter 8), not even that the more procedural knowledge there is, the less declarative knowledge. The phrase “turning into” is a bit misleading on that point; all that is claimed is that existing declarative knowledge, via practice, plays a causal role in the development of procedural knowledge (see, e.g., DeKeyser, 2009). 最后,也许最常见的误解涉及陈述性知识 "转变为 "程序性知识这一概念。这并不是说大脑中发生了什么神秘的转变或移动(关于陈述性/程序性区别和大脑的更多信息,请参见 Ullman, 2004;另见第 8 章),甚至也不是说程序性知识越多,陈述性知识就越少。在这一点上,"转化为 "一词有点误导;我们所声称的是,现有的陈述性知识通过实践在程序性知识的发展中起到了因果作用(参见 DeKeyser, 2009)。
An Exemplary Study: De Jong and Perfetti (2011) 一项典范研究:De Jong 和 Perfetti(2011 年)
I have chosen this article as exemplary study because it addresses the crucial problem in skill acquisition head-on: how best to proceduralize declarative knowledge of grammar and vocabulary. An additional advantage is that this is a study carried out with regular students in a regular classroom, while at the same time the computerized delivery provided much more control over the treatment than is typically the case in classroom research. 我之所以选择这篇文章作为研究范例,是因为它直接解决了技能学习中的关键问题:如何最好地将语法和词汇的陈述性知识程序化。另一个优点是,这是一项在普通课堂上对普通学生进行的研究,同时,与通常的课堂研究相比,计算机化教学为治疗提供了更多的控制。
Participants in this study were 20 ESL students in the United States who were given practice in oral production with the 4-3-2 procedure (Arevart & Nation, 1991): They had to tell a story first in 4 minutes, then in 3 , then in 2 , to improve fluency; they did this three times in as many training sessions. There were two conditions: One group told the same story three times within a training session; the other told three different stories. The researchers wanted to know whether the 4-3-2 sequences would lead to proceduralization, whether this learning effect would last until a delayed test several weeks later, whether it would transfer to new stories, and whether the two conditions would yield different results. Proceduralization was operationalized as increase in (a) mean length of runs (without corresponding change in length of pauses or in phonation/time ratio, that is, actual sound production as a percentage of speaking time) or in (b) stable length of runs (with improving length of pauses and in phonation/time ratio). 这项研究的参与者是美国的 20 名 ESL 学生,他们接受了 4-3-2 程序(Arevart 和 Nation,1991 年)的口语练习:他们必须先在 4 分钟内讲完一个故事,然后在 3 分钟内讲完,最后在 2 分钟内讲完,以提高流利程度。训练分为两种情况:其中一组在一次训练中讲了三次相同的故事;另一组讲了三个不同的故事。研究人员想知道 4-3-2 顺序是否会导致程序化,这种学习效果是否会持续到几周后的延迟测试,是否会转移到新故事中,以及两种条件是否会产生不同的结果。程序化的具体表现为:(a) 平均运行长度的增加(停顿长度或发音/时间比(即实际发音占说话时间的百分比)没有相应变化)或(b) 运行长度的稳定(停顿长度和发音/时间比有所改善)。
The researchers found that for the repetition condition either mean length or stable length of runs increased, while for the no repetition condition, there was no change over time. The gains for the repetition group occurred mostly from pretest to posttest but were maintained on the delayed posttest (after 4 weeks), and for stories on different topics. Therefore, the gains in fluency cannot be attributed to increased lexical access, or more generally to effects of priming and planning. These findings are far from trivial: One may expect that the group that practiced different stories all the time would do better on the new stories in the posttests and delayed posttests, but the opposite was found. Repeating the same story a couple of times, each time at a faster rate, was more effective for the proceduralization of the knowledge that students had to drawn on for the posttests. The design of 研究人员发现,在重复条件下,跑步的平均长度或稳定长度都有所增加,而在不重复条件下,跑步的平均长度或稳定长度随着时间的推移没有变化。复述组的进步主要发生在前测和后测之间,但在延迟后测(4 周后)和不同主题的故事中保持不变。因此,流利程度的提高不能归因于词汇量的增加,或更广泛地说,不能归因于引物和计划的影响。这些发现绝非微不足道:我们可能会认为,一直练习不同故事的小组在后测和延迟后测中对新故事的表现会更好,但结果却恰恰相反。同样的故事重复几遍,每次都以更快的速度进行,对学生在后测中必须借鉴的知识的程序化更为有效。设计
the study does not allow us, however, to pinpoint exactly what knowledge was proceduralized. 然而,这项研究并不能让我们准确地确定哪些知识被程序化了。
Explanation of Observed Findings in SLA SLA 观察结果的解释
Observation 7: There are limits on the effects of frequency on SLA; Observation 9: There are limits on the effects of instruction on SLA; Observation 10: There are limits on the effects of output (learner production) on language acquisition. The findings that there are limits on the effects of frequency, on the effects of instruction, and on the effects of output are very easily explained in this framework: factors such as whether students receive instruction, produce output, and are exposed to certain structures frequently play little role if (explicit) instruction and practice with input and output are not integrated in a way that makes sense according to this theory. Automatization requires procedural knowledge. Proceduralization requires declarative knowledge and slow deliberate practice. The acquisition of declarative knowledge of a kind that can be proceduralized requires the judicious use of rules and examples. These stages cannot be skipped, reversed, or rushed. Unfortunately, however, just about any kind of existing teaching methodology tends to do at least one of the latter three. 观察结果 7:频率对 SLA 的影响是有限的;观察结果 9:教学对 SLA 的影响是有限的;观察结果 10:输出(学习者的产出)对语言习得的影响是有限的。频率的影响、教学的影响和输出的影响都有局限性,这一结论在这一框架下很容易解释:如果(明确的)教学和输入输出练习没有按照这一理论以合理的方式结合起来,那么学生是否接受教学、是否进行输出和是否接触某些结构等因素往往起不到什么作用。自动化需要程序性知识。程序化需要陈述性知识和缓慢的刻意练习。要获得可程序化的陈述性知识,就必须明智地使用规则和范例。这些阶段不能跳过、颠倒或操之过急。然而,不幸的是,现有的任何一种教学方法都倾向于至少完成后三个阶段中的一个。
Observation 5: Second language learning is variable in its outcome; Observation 6: Second language learning is variable across linguistic subsystems. The findings that second language learning is variable in its outcome and variable across linguistic subsystems are equally easy to explain in this framework. Different learners achieve very different levels of proficiency in a given area because of their different levels of ability to grasp the declarative knowledge, the widely differing amounts of practice of specific kinds that individual learners receive for specific structures, and most importantly, the different sequencing of various kinds of explicit information, implicit input, and practice with input and output that different learners receive or create for themselves (which are influenced in turn by motivation, personality, and social context). Learners also show a large amount of intraindividual variation between the different linguistic domains because of differential aptitude, instruction, and practice. Even more importantly, Skill Acquisition Theory easily explains the differences in performance from task to task that are so often observed for the same subcomponent of language in the same individual learner. Performance draws on procedural knowledge, which we saw is very specific, and unevenly developed depending on the amount of practice of various elements of the language under various task conditions. In the same vein, Skill Acquisition Theory explains a factor that is not often addressed in the more linguistically oriented literature, but that is of tremendous importance in the more applied literature: the importance of learning activities and their sequencing and spacing. No amount of any activity means much if it does not fit into the right point of development of skill for a given individual. 观点 5:第二语言学习的结果是可变的; 观点 6:第二语言学习在不同的语言子系统中是可变的。第二语言学习的结果是可变的,不同语言子系统的学习结果也是可变的。不同的学习者在某一领域所达到的熟练程度大相径庭,这是因为他们掌握陈述性知识的能力水平不同,每个学习者在特定结构上所接受的特定类型的练习量也大相径庭,最重要的是,不同的学习者所接受或为自己创造的各种显性信息、隐性输入以及输入和输出练习的顺序不同(这些又受到学习动机、个性和社会环境的影响)。由于能力、教学和练习的不同,学习者在不同语言领域之间也会表现出大量的个体内部差异。更重要的是,技能习得理论很容易解释任务与任务之间的表现差异,这种差异在同一个学习者的同一个语言子成分中经常可以观察到。我们看到,程序性知识是非常具体的,而且发展不平衡,这取决于在不同任务条件下各种语言要素的练习量。同样,"技能习得理论 "解释了一个在语言学方面的文献中并不常见的因素,但在应用方面的文献中却非常重要:学习活动及其顺序和间隔的重要性。 任何活动,如果不符合特定个人技能发展的正确点,都没有多大意义。
Observation 4: Learners’ output (speech) often follows predictable paths with predictable stages in the acquisition of a given structure. The fact that learners follow a predictable 观察结果 4:学习者的输出(语音)往往遵循可预测的路径,并在特定结构的习得过程中经历可预测的阶段。学习者遵循可预测的
path in their development for a given structure also fits well with Skill Acquisition Theory, especially if it is understood somewhat more broadly than in merely linguistic terms. Learners who are exposed to little or no instruction may learn different variants of a structure in a certain order through implicit mechanisms, and show little task variation at a given point in time, but learners who are carefully guided through the stages of skill acquisition for a given structure may show less developmental variation in that kind of structure, but more developmental variation in speed and systematicity of use of this structure, including variation due to (even small variations in) task conditions. When such learners are forced to perform beyond the level of skill they have reached, they may or may not fall back on the same variants of structures used by implicit learners, depending on factors such as how much exposure they have received along with their systematic instruction and what age they are (these two factors influence their opportunity for and their relative susceptibility to implicit and explicit learning). 这也符合技能习得理论(Skill Acquisition Theory)的观点,尤其是如果对技能习得理论的理解比仅仅从语言学的角度来理解更为宽泛的话。很少或根本没有接受过任何指导的学习者可能会通过内隐机制按一定的顺序学会一种结构的不同变体,并在某一特定时间点表现出很少的任务变异,但在某一特定结构的技能习得阶段受到精心指导的学习者可能会在这种结构上表现出较少的发展变异,但在使用这种结构的速度和系统性上却表现出较多的发展变异,包括由于任务条件的(甚至是微小的)变化而产生的变异。当这些学习者被迫超越他们已经达到的技能水平时,他们可能会、也可能不会回到内隐学习者所使用的结构的相同变体上,这取决于他们在接受系统教学时的接触程度以及他们的年龄等因素(这两个因素会影响他们进行内隐和外显学习的机会以及他们对内隐和外显学习的相对敏感性)。
Skill Acquisition Theory and the Explicit/Implicit Debate 技能习得理论与显性/隐性辩论
As stated in the previous sections, Skill Acquisition Theory stresses the importance of the distinction between declarative and procedural knowledge and sees the transition from mostly declarative to mostly procedural as the norm in skill development (cf. Anderson, 2007). The declarative/procedural and explicit/ implicit distinctions do not quite coincide, but for our purposes here, they are equivalent (for more in-depth discussion, see DeKeyser, 2009). It is important to realize, however, that Skill Acquisition Theory by no means denies a role for implicit learning. There can even be “synergy” between the two types of learning for a particular rule or a distribution of roles between the two when a variety of different rules, patterns, or regularities need to be learned. Research on skill acquisition outside of the language domain, as well as research with artificial languages and research with regular second/foreign language learning is increasingly concerned with such synergies or role distributions of implicit and explicit learning processes. 如前几节所述,技能习得理论强调陈述性知识和程序性知识之间区别的重要性,并认为从陈述性知识为主向程序性知识为主的过渡是技能发展的常态(参见 Anderson, 2007)。陈述性知识/程序性知识和显性知识/隐性知识的区别并不完全一致,但就我们的目的而言,它们是等同的(更深入的讨论,参见 DeKeyser, 2009)。然而,重要的是要认识到,技能习得理论绝不否认内隐学习的作用。当需要学习各种不同的规则、模式或规律时,两种类型的学习甚至可以对某一特定规则产生 "协同作用",或在两者之间分配作用。对语言领域之外的技能习得的研究,以及对人工语言和常规第二语言/外语学习的研究,越来越关注内隐和外显学习过程的协同作用或角色分配。
Early work with serial reaction time tasks (Cohen, Ivry, & Keele, 1990) or artificial grammars (Mathews et al., 1989) already hinted at such synergies. More recently and again with artificial grammars, Sallas, Mathews, Lane, and Sun (2007) showed that while chunk learning may lead to better approximation, structure learning through animated model presentation leads to a much higher number of perfect letter strings. Ferman, Olshtain, Schechtman, and Karni (2009) showed that there may be a role distribution in the sense that the simpler rules tend to be learned explicitly and the complex or probabilistic ones-being hard to induce, comprehend, or proceduralize-tend to fare very poorly in explicit learning, to the extent that implicit learning, slow and probabilistic as it may be, yields better results. The latter study with an artificial grammar (letter strings without meaning) is reminiscent of earlier research with 早期在序列反应时间任务(Cohen、Ivry 和 Keele,1990 年)或人工语法(Mathews 等人,1989 年)方面的研究已经暗示了这种协同作用。最近,Sallas、Mathews、Lane 和 Sun(2007 年)再次使用人工语法表明,虽然块状学习可能导致更好的近似,但通过动画模型演示进行的结构学习会导致更多的完美字母串。Ferman、Olshtain、Schechtman 和 Karni(2009 年)的研究表明,可能存在一种角色分布,即较简单的规则倾向于显式学习,而复杂或概率性的规则--难以诱导、理解或程序化--在显式学习中往往表现很差,以至于隐式学习(尽管可能是缓慢和概率性的)会产生更好的结果。后一项关于人工语法(无意义的字母串)的研究让人联想到早先关于以下方面的研究
a miniature linguistic system constituting a made-up natural language, that is, with a meaning component (DeKeyser, 1995), which also showed that explicit learning worked significantly better for the abstract, but simple and categorical rules of morphology, while implicit learning yielded at least descriptively better results for the concrete, but complex and probabilistic patterns of allomorphs. (For thorough reviews of the implicit-explicit learning issue in SLA, see, e.g., DeKeyser, 2003, 2009; Williams 2009; for a discussion of the potential interaction in SLA, see esp. N. Ellis, 2005.) 该研究还表明,显性学习在抽象但简单和分类的词形规则方面效果显著,而隐性学习在具体但复杂和概率性的同形异构模式方面至少在描述性上效果更好(关于 SLA 中隐性-显性学习问题的深入评论,见 DeKeyser, 2003, 2009; Williams 2009;关于 SLA 中潜在互动的讨论,见 DeKeyser, 2003, 2009; Williams 2009)。(有关 SLA 中内隐-外显学习问题的详尽评论,请参见 DeKeyser, 2003, 2009; Williams 2009;有关 SLA 中潜在交互作用的讨论,请参见 N. Ellis, 2005)。
Skill Acquisition Theory, then, does not reject the possibility or usefulness of implicit learning, but focuses on how explicit learning (which is often the only realistic possibility for specific learning problems because of time constraints or logistic issues) can, via proceduralization and automatization of explicitly learned knowledge, lead to knowledge that is functionally equivalent to implicit knowledge. From a purely psycholinguistic point of view, it is important to stress, as does Paradis (2009), that explicit knowledge never becomes implicit through practice; from an applied point of view, however, it is equally important to stress that what matters is fast, accurate, and robust use, the hallmark of automatized procedural knowledge. Given how difficult it is to determine whether knowledge is implicit or explicit (and even more whether learning was implicit or explicit), even under controlled laboratory conditions, it stands to reason that the implicit/ explicit distinction in this narrow sense should be of little concern to second language learners and teachers. Proceduralization, however, as well as a certain degree of automatization of explicitly acquired knowledge, are necessary conditions for practically useful levels of proficiency. How exactly to get to that point is what Skill Acquisition Theory is all about. 因此,技能习得理论并不否定内隐学习的可能性或有用性,而是关注显性学习(由于时间限制或后勤问题,显性学习往往是特定学习问题的唯一现实可能性)如何通过显性学习知识的程序化和自动化,导致与内隐知识功能等同的知识。从纯粹的心理语言学角度来看,正如 Paradis(2009)所强调的那样,显性知识永远不会通过练习变成隐性知识,这一点很重要;然而,从应用的角度来看,同样重要的是要强调快速、准确和稳健的使用,这是自动化程序化知识的标志。鉴于即使在受控的实验室条件下,也很难确定知识是内隐的还是外显的(更难确定学习是内隐的还是外显的),因此,狭义上的内隐/外显之分对第二语言学习者和教师来说应该是无关紧要的。然而,程序化以及显性知识一定程度的自动化是达到实际有用的熟练程度的必要条件。如何达到这一点,正是技能习得理论所要研究的。
Conclusion 结论
In this chapter, I have presented both major findings and methodological aspects of skill acquisition research, illustrated them with a study from the second language domain, and explained how Skill Acquisition Theory is quite compatible with many of the major findings from second language acquisition research and even explains some phenomena better than other theories. In closing, however, it is only fitting to take a somewhat broader view of how well explanations of second language acquisition phenomena based on Skill Acquisition Theory fit into the larger enterprise of cognitive science; in our case, that means trying to understand how the same mind that learns how to recognize the neighbors, play chess, appreciate music, ride a bicycle, program a computer, or use a native language also learns to understand and produce a second language. 在本章中,我介绍了技能习得研究的主要发现和方法论方面的问题,用第二语言领域的一项研究加以说明,并解释了技能习得理论如何与第二语言习得研究的许多主要发现相当吻合,甚至比其他理论更好地解释了某些现象。最后,我们应该从更广阔的视角来看待基于技能习得理论的第二语言习得现象的解释与认知科学这一更广阔的事业的契合程度;就我们而言,这意味着我们要努力理解,学习如何认识邻居、下棋、欣赏音乐、骑自行车、编写计算机程序或使用母语的同一个头脑是如何学习理解和创造第二语言的。
An advantage of the approach illustrated in this chapter is definitely that it fits in very well with other aspects of cognitive science. The same mechanisms, whether couched in psychological or neurological terms, are invoked to explain second language learning and a wide variety of other skills. Second, this approach 本章所阐述的方法的一个优点无疑是,它与认知科学的其他方面非常契合。无论是从心理学还是从神经学的角度来看,同样的机制都可以用来解释第二语言学习和其他各种技能。其次,这种方法
to skill learning has itself proven to be quite robust over the decades, despite the obvious changes in emphases, methodology, and terminology. 几十年来,尽管重点、方法和术语发生了明显的变化,但事实证明,技能学习本身是相当稳健的。
Furthermore, research on skill acquisition, whether carried out with behavioral data or through neuroimaging or computer modeling, is tremendously explicit in its procedures and claims. Power curves, computer programs, and brain scanners give precise answers to precise questions (even though interpreting the answers can still leave a lot of room for discussion). Most important of all, perhaps, research in this area is truly developmental. It does not take snapshots of learners at two or three points between initial learning and near-native proficiency and speculate on how learners got from point a to point b . It can document learning day after day and show how rapid acquisition of declarative knowledge about some structures, rapid proceduralization of knowledge about others, and automatization of some elements of knowledge for specific uses all happen in parallel, while other elements never get automatized, or maybe not even proceduralized, or perhaps not even learned. It may have less to say about which elements of language are going to be learned in what order than other, more (psycho-)linguistically oriented approaches, but it is painstakingly precise and explicit about the big and small steps a learner takes in acquiring (a specific use of) a specific structure. 此外,关于技能习得的研究,无论是通过行为数据,还是通过神经成像或计算机建模,其程序和主张都非常明确。功率曲线、计算机程序和大脑扫描仪都能对精确的问题给出精确的答案(尽管对答案的解释仍有很大的讨论空间)。也许最重要的是,这一领域的研究是真正发展性的。它不会在初始学习到接近母语水平之间的两三个阶段对学习者进行快照,也不会推测学习者是如何从 a 点到达 b 点的。它可以日复一日地记录学习过程,并展示如何快速获得有关某些结构的陈述性知识,快速将有关其他结构的知识程序化,以及将某些知识元素自动化以用于特定用途,而其他元素却从未被自动化,甚至可能没有被程序化,甚至可能根本没有学到。与其他更多以(心理)语言学为导向的方法相比,这种方法可能较少提及哪些语言要素将以何种顺序被学习,但它却非常精确和明确地指出了学习者在掌握特定结构(特定用途)时所采取的大小步骤。
Discussion Questions 讨论问题
Central to Skill Acquisition Theory are the constructs of declarative knowledge, proceduralization, and automatization. Discuss each, paying particular attention to the difference in proceduralization and automatization as well as the context(s) in which automatization may occur. 技能习得理论的核心是陈述性知识、程序化和自动化。请分别讨论这两个概念,尤其要注意程序化和自动化的区别,以及自动化可能发生的背景。
Both De Keyser and Ellis and Wulff offer approaches that are cognitive in nature, that is, built on models/theories from psychology rather than, say, linguistics. How are the two approaches similar or different? De Keyser 和 Ellis 与 Wulff 所提供的方法都是认知性的,即建立在心理学而非语言学的模型/理论之上。这两种方法有何异同?
It is clear that Skill Acquisition Theory is concerned with language behavior. Do you think that such an approach is incompatible with an approach that focuses on competence (e.g., Chapter 3)? 显然,技能习得理论关注的是语言行为。您认为这种方法与注重能力的方法(如第 3 章)不相容吗?
One interpretation of Skill Acquisition Theory is that it is better suited to explain tutored language acquisition as compared to nontutored language acquisition. Another is that it is better suited to explain adult SLA but not child L1A or child SLA. Do you agree? 对技能习得理论的一种解释是,与非辅导语言习得相比,它更适合解释辅导语言习得。另一种解释是,该理论更适合解释成人的 SLA,但不适合解释儿童的 L1A 或儿童的 SLA。您同意这种观点吗?
As you read in Chapter 1, a perennial issue in SLA concerns the roles of explicit and implicit learning and knowledge. Now that you have read about four different theories and models (Chapters 3-6), compare and contrast what each has to say about this issue. 正如您在第 1 章中所读到的,SLA 中一个长期存在的问题涉及显性和隐性学习与知识的作用。现在,您已经阅读了四种不同的理论和模型(第 3-6 章),请比较和对比每种理论和模型对这一问题的看法。
Read the exemplary study presented in this chapter and prepare a discussion for class in which you describe how you would conduct a replication study. Be sure to explain any changes you would make and what motivates such changes. 阅读本章介绍的示范研究,准备课堂讨论,描述你将如何进行复制研究。请务必解释您将做出的任何改变以及做出这些改变的动机。
Suggested Further Reading 建议进一步阅读
Anderson, J. R. (2007). How can the human mind occur in the physical universe? New York, NY: Oxford University Press. Anderson, J. R. (2007).人类思维如何在物理宇宙中发生?纽约州纽约市:牛津大学出版社。
This book provides a more thorough and at the same time more readable account of what was covered in the 2004 article, with ample discussion of how modeling skill acquisition fits into the broader psychological currents of the last three decades. 本书对 2004 年文章中涉及的内容进行了更全面、同时也更具可读性的阐述,并充分讨论了建模技能习得如何与过去三十年更广泛的心理学潮流相契合。
Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological Review, 111, 1036-1060. Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004).心灵的综合理论。Psychological Review, 111, 1036-1060.
An overview of ACT-R theory, with new emphases on neuro-imaging data and the issue of modularity of the mind. Parts are very technical; others are very readable. 概述了 ACT-R 理论,并对神经成像数据和心智模块化问题进行了新的强调。部分内容技术性很强,而其他部分则具有很强的可读性。
DeKeyser, R. (Ed.). (2007). Practice in a second language: Perspectives from applied linguistics and cognitive psychology. New York, NY: Cambridge University Press. DeKeyser, R. (Ed.).(2007).Practice in a second language:Perspectives from applied linguistics and cognitive psychology.纽约州纽约市:剑桥大学出版社。
A book that takes a broad view of practice, with many chapters drawing on Skill Acquisition Theory, applying it to issues from error correction in the classroom to interaction with native speakers during study abroad. 本书从广阔的实践角度出发,许多章节都借鉴了技能习得理论,并将其应用于从课堂纠错到留学期间与母语使用者互动等问题。
DeKeyser, R. M., & Criado-Sánchez, R. (2012). Automatization, skill acquisition, and practice in second language acquisition. In C. A. Chapelle (Ed.), The encyclopedia of applied linguistics (pp. 323-331). Oxford, England: Wiley-Blackwell. DeKeyser, R. M., & Criado-Sánchez, R. (2012).第二语言习得中的自动化、技能习得和练习。In C. A. Chapelle (Ed.), The encyclopedia of applied linguistics (pp. 323-331).Oxford, England:Wiley-Blackwell.
A discussion of what Skill Acquisition Theory means for practice activities in a second language. 讨论技能习得理论对第二语言练习活动的意义。
Hulstijn, J. H., van Gelderen, A., & Schoonen, R. (2009). Automatization in second language acquisition: What does the coefficient of variation tell us? Applied Psycholinguistics, 30(4), 555-582. Hulstijn, J. H., van Gelderen, A., & Schoonen, R. (2009).第二语言习得中的自动化:变异系数告诉我们什么?应用心理语言学》,30(4),555-582。
An interesting discussion of the coefficient-of-variation criterion for automaticity introduced by Segalowitz and Segalowitz (1993) and used (by, e.g., Rodgers, 2011). 对 Segalowitz 和 Segalowitz(1993 年)提出的自动性变异系数标准进行了有趣的讨论(如 Rodgers,2011 年)。
Lyster, R., & Sato, M. (2013). Skill acquisition theory and the role of practice in L2 development. In M. García Mayo, J. Gutierrez-Mangado, & M. Martínez Adrián (Eds.), Contemporary approaches to second language acquisition (pp. 71-91). Amsterdam, Netherlands: John Benjamins. Lyster, R., & Sato, M. (2013)。技能习得理论与实践在 L2 发展中的作用。In M. García Mayo, J. Gutierrez-Mangado, & M. Martínez Adrián (Eds.), Contemporary approaches to second language acquisition (pp. 71-91).荷兰阿姆斯特丹:John Benjamins.
A thorough discussion of Skill Acquisition Theory and practice in L2, with some emphasis on the role of feedback. Very useful to read in conjunction with this chapter. Segalowitz, N. (2010). Cognitive bases of second language fluency. London, England: Routledge. The most thorough discussion to date of automaticity and the process of automatization as they apply to second language learning and bilingualism. 《全面论述了技能习得理论和 L2 的实践,其中强调了反馈的作用。结合本章阅读非常有用。Segalowitz, N. (2010).第二语言流利性的认知基础》。英国伦敦:Routledge.迄今为止对第二语言学习和二语习得中的自动性和自动化过程进行的最透彻的讨论。
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