Processing approaches

The approaches we will review here all have in common the fact that they are interested in the way in which the brain's processing mechanisms deal with the second language. The first approach, information processing, investigates how different memory stores (short-term memory (STM);

long-term memory (LTM) - declarative and procedural) deal with new sec-ond language information, and how this information is automatized and restructured through repeated activation. The second approach, process-ability theory, looks more specifically at the processing demands made by various formal aspects of the second language, and the implications for learnability and teachability of second language structure.

4.2.1 Information-processing models of second language learning

The work we will be discussing under this heading originates from infor-mation-processing models developed by cognitive psychologists, which have then been adapted to the treatment of language processing, both first and second language. First, we examine McLaughlin's (1987, 1990) infor-mation-processing model. Second, we will turn our attention to Anderson's Active Control of Thought (ACT*) model (1983, 1985), paying particular attention to O'Malley and Chamot's (1990) application of the model in the field of learner strategies and toTowell and Hawkins' (1994) application to the development of fluency.

4.2.1.1 McLaughlin's information-processing model

In general, the fundamental notion of the information-processing approach to psychological inquiry is that complex behaviour builds on simple processes.

(McLaughlin and Heredia, 1996, p. 213) Moreover, these processes are modular and can therefore be studied inde-pendently of one another. Table 4.1 summarizes the main characteristics of such an approach.

Table 4.1 Some charaqteristics of the information-processing approach

Humans are viewed as autonomous and active

The mind is a general-purpose, symbol-processing system

Complex behaviour is composed of simpler processes; these processes are modular Component processes can be isolated and studied independently of other processes Processes take time; therefore, predictions about reaction time can be made The mind is a limited-capacity processor

{Source: McLaughlin and Heredia, 1996, p. 214)

When applied to SLL, this approach can be summarized as follows:

Within this framework, second language learning is viewed as the acquisition of a complex cognitive skill. To learn a second language is to learn a skill>

because various aspects of the task must be practised and integrated into fluent performance. This requires the automatization of component sub-skills. Learning is a cognitive process, because it is thought to involve internal representations that regulate and guide performance ... As performance improves, there is constant restructuring as learners simplify, unify, and gain increasing control over their internal representations (Karmiloff-Smith 1986). These two notions - automatization and restructuring - are central to cognitive theory.

(McLaughlin, 1987, pp. 133-4) Automatization (McLaughlin 1987, 1990; McLaughlin and Heredia 1996) is a notion based on the work of psychologists such as Shiffrin and Schneider (1977), who claim that the way in which we process information may be either controlled or automatic, and that learning involves a shift from controlled towards automatic processing. Applied to SLL, such a model works as follows.

Learners first resort to controlled processing in the second language.

This controlled processing involves the temporary activation of a selection of information nodes in the memory, in a new configuration. Such process-ing requires a lot of attentional control on the part of the subject, and is constrained by the limitations of the short-term memory. For example, a

beginner learner wanting to greet someone in the second language might activate the following words: good morning how are you? Initially, these words have to be put together in a piecemeal fashion, one at a time (assuming they have not been memorized as an unanalysed chunk).

Through repeated activation, sequences first produced by controlled processing become automatic. Automatized sequences are stored as units in the long-term memory, which means that they can be made available very rapidly whenever the situation requires it, with minimal attentional control on the part of the subject. As a result, automatic processes can work in parallel, activating clusters *of complex cognitive skills simultaneously.

So, in the above example, once a learner has activated the sequence good morning how are you? a large number of times, it becomes automatic, that is, it does not require attentional control. However, once acquired, such automatized skills are difficult to delete or modify.

Learning in this view is seen as the movement from controlled to automatic processing via practice (repeated activation). When this shift occurs, controlled processes are freed to deal with higher levels of processing (i.e. the integration of more complex skill clusters), thus explaining the incremental (step by step) nature of learning. It is necessary for simple sub-skills and routines to become automatic before more complex ones can be tackled. Once our learner has automatized good morning how are you?> he or she is free to deal with the learning of more complex language, as the short-term memory is not taken up by the production of this particular string.

This continuing movement from controlled to automatic processing results in a constant restructuring of the linguistic system of the second language learner. This phenomenon may account for some of the variabil-ity characteristic of learner language. Restructuring destabilizes some structures in the interlanguage, which seemed to have been previously acquired, and hence leads to the temporary reappearance of second language errors. Restructuring is also the result of exemplar-based repre-sentations becoming rule-based (McLaughlin and Heredia, 1996). As we suggested earlier, second language learners often start by memorizing unanalysed chunks of language, which will later be analysed and give rise to productive rules (Wong-Fillmore, 1976; Weinert, 1995; N.C. Ellis 1996a,

1996b; Myles et al.> 1998, 1999;Wray and Perkins, 2000; Wray, 2002). For example, a learner might first memorize a question as an unanalysed chunk, for example have you got a pet?, without having a productive rule for interrogatives, involving inversion. When this learner starts generating inter-rogatives that are not rote-learned chunks, he or she might produce an alternative, uninverted form, such has you have pet?

This account is especially convincing in its explanation of the vexed issue of fossilization, which is so well documented in second language acquisition studies. As we saw in Chapter 1, fossilization refers to the fact that second language learners, unlike first language learners, sometimes seem unable to get rid of non-native-like structures in their second language despite abundant linguistic input over many years. Fossilization in this model would arise as a result of a controlled process becoming automatic prematurely, before it is native-like. As we have seen, automatic processes are difficult to modify as they are outside the attentional control of the subject. Thus they are likely to remain in the learner's interlanguage, giving rise to a stable but erroneous construction.

However, this general idea does not explain why some structures seem much more likely to fossilize than others.

4.2.1.2 Anderson's ACT* model

Another processing model from cognitive psychology, which has also been applied to aspects of SLL, is Anderson's (1983, 1985) ACT* model. This model is not dissimilar from McLaughlin's. It is more wide-ranging, and the terminology is different, but practice leading to automatization also plays a central role. It enables declarative knowledge (i.e. knowledge that something is the case) to become procedural knowledge (i.e. knowledge how to do something). One of the major differences is that Anderson posits three kinds of memory: a working memory, similar to McLaughlin's short-term memory and therefore tightly capacity-limited, and two kinds of long-term memory - a declarative long-long-term memory and a procedural long-term memory. Anderson believes that declarative and procedural knowledge are different kinds of knowledge that are stored differently.

But, before outlining the way in which the different kinds of memories work and interact, let us illustrate with a simple example what is meant by declarative and procedural knowledge. If you are learning to drive, for example, you will be told that if the engine is revving too much, you need to change to a higher gear; you will also be told how to change gear. In the early stages of learning to drive, however, knowing that (declarative know-ledge) you have to do this does not necessarily mean that you know how (procedural knowledge) to do it quickly and successfully. In other words, you go through a declarative stage before acquiring the procedural know-ledge linked with this situation. With practice, however, the mere noise of the engine getting louder will trigger your gear changing, without you even having to think about it. This is how learning takes place in this view: by declarative knowledge becoming procedural and automatized.

Anderson's (1983) application of his model to first language acquisition has been criticized for insisting that all knowledge starts out in declarative form (DeKeyser, 1997). This is clearly problematic in the case of first language learners, as Anderson has accepted in answering some of these criticisms. With respect to language learning, Anderson does not claim that all knowledge needs to start as declarative knowledge any longer (Anderson and Fincham, 1994; MacWhinney and Anderson, 1986). However, other applications, such as to the learning of algebra, geometry or computer programming, have been very successful. Indeed, it is the comparability of the teaching or learning of sec'6nd languages in instructional environments with the teaching or learning of complex skills such as algebra that has attracted the attention of second language acquisition researchers. Because Anderson's model is a general cognitive model of skill acquisition, it can be applied to those aspects of SLL that require proceduralization and autom-atization (Raupach, 1987; O'Malley and Chamot, 1990; Schmidt, 1992;

Towell and Hawkins, 1994; Johnson, 1996).

Let us illustrate with an example how the notions of declarative and pro-cedural knowledge could apply to SLL. If we take the example of the third person singular -s marker on present tense verbs in English, the classroom learner might initially know, in the sense that she has consciously learnt the rule, that s/he + Verb requires the addition of an -s to the stem of the verb.

However, that same learner might not necessarily be able to consistently produce the -s in a conversation in real time. This is because this particular learner has declarative knowledge of that rule, but it has not yet been proceduralized. After much practice, this knowledge will hopefully become fully proceduralized, and the third person -s will be supplied when the context requires it. This dichotomy between, on the one hand, knowing a rule, and on the other, being able to apply it when needed, is all too famil-iar to second language learners and teachers.

According to Anderson, the move from declarative to procedural know-ledge takes place in three stages (Anderson, 1985, p. 232, cited in Towell and Hawkins 1994, p. 203):