In the last few years, what can be characterized as an imminent emergentist turn has made inroads into several research programmes about cognition in SLA. Emergentism refers to a contemporary family of theories in cognitive science that have coalesced out of increasingly critical examinations of the tenets of information
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processing theories. Because they have evolved out of information processing, they share much common ground with it. However, because emergentism is critical of traditional cognitivist notions and goes well beyond them, it is sometimes characterized as a post-cognitivist paradigm (Potter, 2000; Wallace et al., 2007). In the field of SLA the pioneer of emergentism has been language psychologist Nick Ellis. For over a decade now, he has led other SLA scholars to think about the associative, probabilistic and usage-based nature of L2 acquisition, first from the University of Wales Bangor (e.g. Ellis, 1993, 1996) and currently from the University of Michigan (e.g. Ellis and Larsen-Freeman, 2006; Ellis, 2007; Robinson and Ellis, 2008b).
In a manifesto of emergentism in SLA, Ellis and Larsen-Freeman summarized the position as follows:
Emergentists believe that simple learning mechanisms, operating in and across the human systems for perception, motor-action and cognition as they are exposed to language data as part of a communicatively-rich human social environment by an organism eager to exploit the functionality of language, suffice to drive the emergence of complex language representations.
(2006, p. 577) The emergentist family of explanations for L2 learning is built on several nested assumptions that borrow from diverse contemporary schools in cognitive sci- ence, all sharing a post-cognitivist bent. Three important tenets on which emer- gentist approaches build are associative learning, probabilistic learning and rational contingency (Ellis, 2006a). From these three principles derive the ‘simple learning mechanisms’ to which Ellis and Larsen-Freeman (2006) refer in the quote above.
Associative learning, as we saw in section 5.15, means that learning happens as we form memories of instances or exemplars we experience in the input, in a process of automatic extraction of statistical information about the frequency and sequential properties of such instances. Ellis (2006a) explains that the human architecture of the brain is neurobiologically programmed to be sensitive to the statistical properties of the input and to learn from them. When processing stimuli, the brain engages in a continuous and mandatory (as well as implicit, in the sense of automatic and certainly unconscious) tally of overall frequency of each form and likelihood of co-occurrence with other forms. This statistical tallying is supported by neural structures in the neocortex (Ellis, 2006b). Probabilistic learning posits that learning is not categorical but graded and stochastic, that is, it proceeds by (subconscious) guesswork and inferences in response to experience that always involves ambiguity and uncertainty (Chater and Manning, 2006). However, this kind of probabilistic calculation is not a slave of whatever is experienced by the human brain as a contiguous temporal or spatial surface pattern. Instead, the probability calculations of the human mind are guided by principles of rational contingency, or automatically computed expectations of outcomes on the basis of best possible evidence (Chater and Manning, 2006; Ellis, 2006b). Specifically, the
processor makes best-evidence predictions about outcomes based upon (a) the overall statistics extracted by accumulated experience, (b) the most recent relevant evidence, (c) attention to cues detected to be present and (d) the clues provided by the context (Ellis, 2002a, 2006a, 2006b, 2007). Each time the outcome is confirmed or not in another relevant event, the processor adjusts to the new evidence and modifies its prediction so its predictive accuracy is better next time.
Additional important tenets in the emergentist family of theories are perhaps broader in scope. One is usage-based learning, or the position that language use and language knowledge are inseparable, because we come to know language from using it. Hence the specification in the earlier quote by Ellis and Larsen-Freeman (2006) that learning from exposure comes about ‘as part of a communicatively-rich human social environment’ and is experienced ‘by an organism eager to exploit the functionality of language’ (p. 577). Among others, US cognitive scientist Michael Tomasello, now at the Max Planck Institute in Germany, has been instrumental in advancing a view of language acquisition that is usage-based, where grammar concepts emerge out of communicative and social needs: ‘people construct relational and semantic categories in order to make sense of the world and in order to communicate with one another’ (Abbot-Smith and Tomasello, 2006, p. 282). Importantly, this commitment to usage-based learning means that two traditional distinctions in linguistics and information processing, respectively, are transcended: competence and performance, and representation and access. Furthermore, meaning (rather than rules) is held to be of primary importance in understanding the language faculty. For this reason, the linguistic schools that best suit the emergentist project are cognitive linguistics (Langacker, 2008) and corpus linguistics (Gries, 2008).
Another broad-scope tenet of emergentism is that cognition is grounded, and therefore language is too. By this, it is meant that our species’ experience in the world and the knowledge that we abstract from such experience is always structured by human bodies and neurological functions (Evans et al., 2007; Barsalou, 2008). This is why Ellis and Larsen-Freeman (2006) described learning mechanisms as ‘operating in and across the human systems for perception, motor- action and cognition’ (p. 577). Perception and action, and not only abstract or symbolic information, are believed to shape cognition (Wilson, 2002). Hence, perceptual and sensory-motor functions of the brain must also be implicated in language acquisition. They contribute to the emergence of language abstractions and they also constrain and guide many of the simple learning mechanisms of associative, probabilistic and rational contingent learning.
The final tenet that is worth highlighting in this synoptic examination of emer- gentism is that language acquisition, like the acquisition of other forms of cognition, is a self-organizing dynamical system. This entails viewing the phenomenon to be explained (e.g. language learning) as a system (or ecology) composed of many inter- connected parts that self-organize on the basis of multiple influences outside the system; these influences provide constraints that afford self-organization, but no single cause has priority over others, as explicated in developmental psychology by Linda Smith and the late Esther Thelen at Indiana University (Smith and Thelen,
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2003) and by Paul van Geert (1998) at the University of Groningen. In addition, a change in any given part of the system will result in changes in other parts, but the two need not be commensurate in size or importance. As Smith and Thelen describe, ‘Development can be envisioned, then, as a series of evolving and dissolving patterns of varying dynamic stability, rather than an inevitable march towards maturity’ (p. 344). In SLA, an early call of attention to dynamical systems was made by Diane Larsen-Freeman (1997), and a recent research programme has been more explicitly proposed by Kees de Bot and his colleagues at the University of Groningen (de Bot et al., 2007; de Bot, 2008). Dynamicity in self-organizing systems also places vari- ability at the heart of investigation (de Bot et al., 2007). Much of the theory behind the tenet of dynamic systems originated from insights in meteorology sciences (Larsen-Freeman, 1997). It is no wonder, then, that Ellis (2002a) comments: ‘The multiplicity of interacting elements in any system that nontrivially represents lan- guage makes the prediction of the patterns that will eventually emerge as difficult as forecasting the weather, the evolution of an ecological system, or the outcome of any other complex system’ (p. 178).
It would also be an uncertain weather forecast exercise to predict how long it will take for usage-based, dynamic systems, emergentist SLA to really come to fruition. Certainly, emergentist thinking about L2 learning has been vibrant, but the conse- quences have been less noticeable in terms of empirical activity. Thus, for instance, the implications of connectionism for L2 learning have been discussed frequently, beginning in the 1990s (Ellis, 1998; Gasser, 1990), but empirical applications of con- nectionism to SLA have been rare (e.g. Ellis and Schmidt, 1998). Full empirical deployment is gradually on the rise, however, and may become a reality in the not- so-distant future, judging from the impressive array of not only theoretical but also empirical knowledge around cognitive linguistics and emergentist SLA that Robinson and Ellis (2008a) have gathered. However long it takes, what is certain is that emergentism in SLA will flourish, as it has in a number of other fields.
Second language learning under an emergentist perspective has the potential to look less like development that proceeds teleologically towards the ultimate attainment of a so-called native grammar, and more like a complex deployment of human multi-language capacities as a function of experience in the world. The picture of L2 development they offer resonates with what we know about learner language, which we will discuss in the next chapter. The new approach, by redefining cognition as emergent, helps envision additional language acquisition less as a formal, deterministic and symbolic feat and more as an ecological phenomenon, ‘a dynamic process in which regularities and system emerge from the interaction of people, their conscious selves, and their brains, using language in their societies, cultures, and world’ (Ellis, 2007, p. 85).
5.17 SUMMARY
● Information processing theories assume that: (a) the human cognitive architecture is made of representation and access; (b) mental processing is
comprised of automatic or fluent (unconscious) operations and voluntary or controlled (conscious) operations; (c) cognitive resources such as attention and memory are limited; and (d) performance is variable and vulnerable to stressors, as shown during dual-task performance.
● A particular kind of information processing theory called skill acquisition theory explains L2 learning as the process of gradual transformation of performance from controlled to automatic, via proceduralization, or meaningful practice that is sustained over time.
● Proceduralization leads to automatization, which is a quality of fluent, automatic performance that goes beyond sheer speed.
● The benefit of practice drops off at some point in the learning curve due to the power law of learning; the benefits of practice are skill-specific.
● Prolonged and repeated meaningful practice changes the knowledge representation itself; with repeated access, the stored knowledge becomes more elaborated, well specified and analysed.
● Memory is composed of two types, long-term memory and working
memory, which interact.
● Long-term memory is about representations and it is unlimited; it can be explicit-declarative (the facts and events we know and can talk about) or implicit-procedural (knowledge we do not know we hold, but which affects our behaviour and supports our skills, habits and performance). Another distinction that has received less attention in SLA is between semantic memory (decontextualized knowledge) and episodic memory (knowledge encoded with information about the lived experiences in which we acquired it).
● How L2 vocabulary is encoded in long-term memory has resulted in research about the strength, size and depth of word knowledge, as well as various theories about the content of the entries in the mental bilingual lexicon and how the L1 and L2 lexicons interact during lexical access. ● Working memory is about access and it is limited; it controls what
information can be stored momentarily (the issue of storage) and how well and how long it can be activated and integrated with already known information in long-term memory (the issue of processing); it is the site for controlled processing and consciousness.
● It is known that working memory capacity is smaller in the L2 than the L1, and it is assumed that this lag narrows with increasing proficiency; there is also debate as to whether passive or active measures of working memory are more suitable to investigate the relationship between working memory and L2 learning. SLA researchers have not investigated these issues systematically yet. More is known about working memory capacity and
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individual differences in L2 learning, which we will examine in Chapter 7 (sections 7.7 and 7.8).
● Attention is thought to be central to understanding L2 learning. Attention is limited and selective, and it can be voluntary and accessible to consciousness. Several questions have been investigated in SLA with regard to attention.
● Is L2 learning possible without intention? Yes, incidental learning, or learning without intention, is possible in L2 learning. The best-known example is the incidental learning of L2 vocabulary while reading for pleasure. However, people learn faster, more and better when they learn deliberately or with intention.
● Is L2 learning possible without attention? Examination of this question has taken the form of a debate over whether detection (registration outside focal attention) is sufficient for L2 learning or whether noticing (detection plus activation into the focus of attention) is also required. The jury is still out, but rather than investigating the qualities of attention that are necessary for learning, most studies have examined the issue of awareness.
● Is L2 learning possible without awareness? This question cannot be directly answered, because zero awareness may be methodologically impossible to establish. However, awareness (as measured through reports of noticing and understanding during think-aloud protocols) has been shown to be related to higher post-test scores, suggesting that noticing with awareness is facilitative of L2 learning.
● Is L2 learning possible without rules? In the absence of rules, low-level associative learning is certainly possible. This kind of learning draws on data-driven processes supported by memory. With rules, learning proceeds by drawing on controlled operations and conceptually driven processes supported by conscious attention. Both types of processing of new L2 material can lead to learning, and both can interact. A pending question for future research is whether all aspects of an L2 are equally learnable by implicit means or whether some particularly complex aspects of the L2 may require conceptually driven processing in order for associations and representations to be formed.
● An imminent emergentist turn has made inroads into several research programmes about cognition in SLA. Emergentism refers to a contemporary family of theories in cognitive science that reconceptualizes information processing as an associative, probabilistic, rational, usage- based, grounded, dynamic and, in sum, emergent adaptation of the agent to the environment.
● It is difficult to predict how long it will take for emergentist SLA to really come to fruition and so far the publications offer discussions and
expositions more than they offer L2-specific empirical evidence. Emergentism will likely flourish in cognitive SLA in future years, however, judging from the pervasiveness of emergentist thought in many other areas of the cognitive sciences.
5.18 ANNOTATED SUGGESTIONS FOR FURTHER READING
If you are looking to read some more about information processing and emergentism in SLA, the best accessible choices are DeKeyser (2007b) and Ellis (2007), both in the same collection and following a similar and helpful approach to their topics. The best overviews of memory and attention in SLA remain Robinson (1995) and Schmidt (2001).
If you are interested in deeper readings, here are some recommendations. To delve deep into information processing, I recommend beginning by reading the chapters in DeKeyser (2007a) that examine skill acquisition theory and notions surrounding L2 practice, including DeKeyser’s own. Following that, you can tackle Segalowitz (2003) and McLaughlin and Heredia (1996) in order to develop a better sense for what proceduralization, automaticity and restructuring are or are not. The emergentist range of positions can nicely be reviewed in a special issue of the
Modern Language Journal guest-edited by de Bot (2008) and the seminal collection
by Robinson and Ellis (2008a).
If you are interested in concrete empirical applications, you can read DeKeyser (1997) and Robinson (1997), already discussed in depth in this chapter, and three more related influential studies by Ellis (1993) and John Williams (1999, 2005). If you want to delve into this empirical area even more deeply, you can read the studies gathered in two special issues of the journal Studies in Second Language
Acquisition in 1997 (issue 2) and 2005 (issue 2). They will make good sense now!
An alternative route to developing expertise in cognition and SLA is to read foundational L1 articles, and only then embark on the SLA-specific readings that I outlined above. You would probably get a lot more mileage out of your readings in this way. If you want to take this mini-journey, I strongly recommend you begin with an interview with K. Anders Ericsson (Schraw and Ericsson, 2005), in which he describes in personable terms his development, with Herbert Simon, of the think-aloud method, his research on expertise through deliberate practice (a concept that is very relevant to L2 learning but has not made it into SLA yet!), and the ideas about memory and knowledge that he developed with Walter Kintsch. It is also particularly useful to consult a few journals whose mission is to disseminate advances to a generalist audience in psychology and cognitive sciences. In Annual
Review of Psychology you will find the accessible and fascinating recount of episodic
memory by Endel Tulving (2002) and the informative and readable introduction to grounding in the study of cognition by Barsalou (2008). Both articles make for a good case not to shy away from investigating meaning, consciousness and experience in cognition (as well as in cognitively oriented SLA). In Current
Annotated suggestions for further reading 109
discussion about working memory and attention. In TRENDS in Cognitive Sciences, you will find an introduction to dynamical systems theory by Smith and Thelen (2003) that is lucid and engaging and can be a good way to lead into de Bot et al. (2007) for an SLA-specific treatment. Also in TRENDS is the technical but brief and worthwhile introduction to probabilistic learning by Chater and Manning (2006), which makes for good reading to pave the way into Ellis (2006a, 2006b).
Finally, if anything mentioned in this chapter about vocabulary piqued your interest, accessible and enjoyable are any readings by Paul Nation (e.g. his recent 2006 article about vocabulary size), Norbert Schmitt (e.g. the longitudinal study he conducted in 1998; or the case study of extensive reading reported in Pigada and Schmitt, 2006) and Batia Laufer (e.g. Hulstijn and Laufer, 2001).