tence is often contrasted with performance, which is the overtly observable and concrete manifestation or realization of competence. It is the actual do-ing of somethdo-ing: walkdo-ing, sdo-ingdo-ing, dancdo-ing, speakdo-ing. In reference to lan-guage, performance refers to the individual’s actual language use. It is actual production (speaking, writing) or the comprehension (listening, reading) of linguistic events. For example, a mature speaker of a language (e.g., English) might make an error and produce an utterance such as ‘I will be home yes-terday’. The adult speaker of English who makes this error knows that this constitutes an ill-formed utterance, and can easily recognize it as such. How-ever, performance errors like these are fairly common in adult speech and can result from such performance variables as memory limitations, distrac-tions, shifts of attention and interest, errors, and hesitation phenomena, such as repeats, false starts, pauses, omissions, and additions, for example. Chom-sky likened competence to an ‘idealized’ speaker-hearer who does not dis-play such performance variables. Chomsky’s point was that a theory of lan-guage had to be a theory of competence lest the linguist try in vain to catego-rize an infinite number of performance variables that are not reflective of the underlying linguistic ability of the speaker-hearer.
While many researchers both within L1 and L2 research (see INTERLAN-GUAGE) are interested in understanding the nature of a learner’s underlying competence, all language behavior is ultimately performance, and therefore a tension exists between measures of linguistic knowledge (i.e., competence) which necessarily have to rely on human behavior (i.e., performance). Re-searchers interested in understanding linguistic competence need to be dili-gent therefore to use measures which minimize performance errors.
Brown 2007; Macaro et al. 2010; VanPatten & Benati 2010 Competition Model
also CM
a functional model of language use and language acquisition proposed ini-tially by Bates and MacWhinney, for understanding both L1 and L2 learn-ing. It views the task of language learning as that of discovering the particu-lar form-function mappings that characterize the target language. It is argued that the forms of natural languages are created, governed, constrained, ac-quired and used in the service of communicative functions. Any one form may realize through a number of functions and, conversely, any one function can be realized through a number of forms. The learner’s task is to discover the particular form-function mappings that characterize the target language.
Form-function mappings are characterized as being of varying strengths in different languages. This is usually illustrated with reference to the function of ‘agency’, which has a number of possible formal exponents:
1) Word order: in the case of transitive constructions, the first noun men-tioned in a clause is likely to function as the agent. For example, in the
Competition Model 73 English sentence Mary kissed John, ‘Mary’ is the agent.
2) Agreement: the noun phrase which functions as agent may agree in num-ber with the verb. Thus, in English, a singular noun phrase functioning as agent takes a singular verb form (e.g., She likes ice-cream), while a plural noun phrase takes a plural verb form (e.g., They like ice-cream). The ob-ject of the sentence has no effect on the verb form.
3) Case: the noun phrase functioning as agent may be morphologically marked in some way. For example, the agent is signaled in German by nominative case marking on the article, while the object is signaled by means of accusative case marking (e.g., Der Mann isst den Apfel = The man is eating the apple).
4) Animacy: agents are normally animate, patients are normally inanimate.
Any one language is likely to utilize several devices for signaling the ‘agent’
of a sentence. English, for example, uses all four, as illustrated in these sen-tences:
Mary kissed John. (word order) Money they like. (agreement) She kissed him. (case)
This book Mary likes a lot. (animacy)
However, a language is likely to assign different weights to these devices in terms of the probability of their use in signaling a given function. English, as the above examples show, relies primarily on word order to encode agency, while Russian uses case marking, and Japanese, animacy. Like VARIABILITY
models, the Competition Model (CM) is probabilistic in nature.
The model takes its name from the ‘competition’ that arises from the differ-ent devices or cues that signal a particular function. For example, in a sen-tence like that lecturer we like a lot there is competition between ‘lecturer’,
‘we’, and ‘lot’ for the role agent of the verb. ‘Lot’ rapidly loses out because, unlike ‘lecturer’ and ‘we’, it is inanimate, and because it follows rather than precedes the verb. The candidacy of ‘lecturer’ is promoted by its position in the sentence—it is the first noun—but, ultimately, this cue is not strong enough to overcome two other cues. ‘We’ is the strongest candidate for agent because it is nominative in case and because it agrees in number with the verb.
The task facing the L2 learner is to discover (1) which forms are used to re-alize which functions in the L2, and (2) what weights to attach to the use of individual forms in the performance of specific functions. This is what is meant by ‘form-function mapping’. The INPUT supplies the learner with cues of four broad types: word order, vocabulary, morphology, and intonation.
The usefulness of a cue is determined by several factors: cue availability, cue reliability, and conflict validity. Availability refers to how present or
74 Competition Model
frequent the cue is in the input. In English, word order is a readily available cue for what a subject is; however, subject-verb agreement is not (i.e., only in the present tense and with third-person singular do we see person-number marking on verbs). Reliability refers to how often the cue leads to the correct sentence interpretation. In English, word order almost always leads to cor-rect interpretation of what the subject is as English is rigidly subject-verb-object. Conflict validity refers to how valid a cue is for correct sentence in-terpretation when it is in conflict with another. That is, whether a cue ‘wins’
or ‘loses’ when it appears in competitive environments. In a language like Hebrew, case marking has high conflict validity because Hebrew sentences can be either subject-verb-object or object-verb-subject (or possibly other combinations). If people normally expect subjects to appear before verbs, then object-verb order is in conflict with case marking, but case marking would win out because it is more reliable (i.e., subjects are always marked one way and objects are always marked another). This is not the case in German. Although German consistently marks articles for case, the markings themselves are not always unique. Masculine definite articles are either der for subject or den for object. But for feminine, the article is always die.
Thus, when case marking is in conflict with word order in German (German allows object-verb-subject and requires certain orders in embedded clauses and other constructions), case marking has high conflict validity only for masculine nouns indicating subject and object. In language acquisition, cues that are available, highly reliable, and have high conflict validity will be ac-quired before those that do not possess the same characteristics. The CM also claims that L2 learners will transfer the cue strengths of their L1 to the L2 in the initial stages of acquisition. Language learning within the view of the CM is, therefore, driven by the input and not by a set of prespecified in-ternal linguistic constraints as in UNIVERSAL GRAMMAR. Learners have to de-tect linguistic cues which are distributed within the linguistic input and by detecting these cues will be able to learn language. Thus, the model provides a minimalist, empiricist prediction for the ways cues are acquired.
The CM has informed a number of studies of L2 acquisition. These studies take the form of sentence-interpretation experiments using bilingual subjects in a within-subject, cross-language design. That is, speakers of different lan-guages are asked to identify the function of different cues in L1 and L2 sen-tences that have been designed to reflect both the coordination and competi-tion of cues. For example, they may be asked to say which noun is the agent of an action in acceptable sentences like The boy is chopping the log and in semantically unlikely sentences such as The logs are shopping the boy, where the animacy cue is in competition with the word order cue, but the agreement cue is in coordination. The studies then compare the responses of learners with different language backgrounds.
Competition Model 75 MacWhinney, later, outlined a development of the CM, which he called the
Unified Model because it sought to provide an account of both L1 and L2 learning. According to this model, forms are stored in associative maps for syllables, lexical items, constructions, and mental models. For example, in lexical maps, words are viewed as associations between forms and functions.
Construction maps consist of patterns that show how a predicate (verb, ad-jective, preposition) can combine with its arguments. The idea of self-organizing associative maps is derived from computer modeling of language learning. These show learning involving three phases. In the first phase, all units in the model are activated by the input with each unit computing its current activation. In the second phase, units compete with the best matching unit emerging as the winner. In the third phase, the weights of the respond-ing unit are adjusted to increase the precision of future activation. Within these associative maps, learning is self-organized, modulated by a number of processes—‘buffering’, ‘chunking’, and ‘resonance’. Buffering serves as a mechanism for the final form/interpretation. Chunking (the process of stor-ing formulaic sequences) provides a data base from which grammar can emerge through analogic processing. Resonance is the process by which ro-bust connections within neural structure of the brain are formed. It is achieved through careful timing of practice to stimulate resonant activation of the relevant neurons.
The strength of the CM is that it provides a convincing account of a number of aspects of L2 acquisition which any theory must consider: the role of the L1, the effect of input, and the gradual way in which native-like ability is acquired. There are, of course, other aspects which it does not address, at least not at the moment. It is not clear, for instance, what kind of knowledge (implicit or explicit) learners use in sentence interpretation. The early ver-sion of the model did not have much to say about the cognitive mechanisms responsible for the obtaining INTAKE from input or for using L2 knowledge in production. However, the later Unified Model with its account of buffer-ing, chunkbuffer-ing, and resonance has largely filled this gap.
Probably the main weakness of the model is over-reliance on rather artificial interpretation tasks, a problem that is aggravated by the unnatural sentences that figure in such tasks. The justification for such a methodology is the Eco-logical Validity Hypothesis, according to which the processing of both grammatical and ungrammatical sentences proceed by reference to the same set of cues and processing patterns. It might be further argued that L2 acqui-sition take place as a result of ‘utterance processing’ rather than ‘sentence processing’, the distinguishing feature being that utterances are contextual-ized whereas sentences are not. Utterance processing involves pragmatic procedures, which are ignored in the kind of sentence-processing tasks on which the CM has relied. Nevertheless, the CM is a powerful theory, like
PROCESSABILITY THEORY, it affords very precise prediction about L2