S. Shokooh

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English Language Teaching

Psychological processes in language acquisition: symbolic vs. connectionist

account

One of the most basic distinctions in the psychology of language that concerns both the representation and the acquisition of language competence is the symbolic versus connectionist contrast. Hulstijn (2002a) explains that symbolic accounts represent knowledge as a collection of symbols accompanied by rules that specify the relationship between them. By symbol we mean a unit that exists independently of the context. We can also think of a radically different type of architecture of knowledge- connectionist account- whereby knowledge is not represented as sums of tiny information-packed units but rather as

activation patterns in a neural network .

Symbolic vs. Connectionist account

The distinction between nativist and non-nativist approaches refers to whether the infant is assumed to bring a genetically coded linguistic endowment to language acquisition or not. The standard claim is to equate Nativism with symbolism and non-nativism with connectionism.

Chomsky’s prominent symbolist theory of generative grammar is explicitly nativist, whereas for connectionists the idea of innate predeterminism with no process explanation seems to be at odds with current theories of the development and the functioning of the brain .

Nativism and Universal Grammar

Innatism: Universal grammar or generative grammar!!!

We are born with a set of rules about language in our brains.

“ Children are equipped with an innate template or blueprint for language and this blueprint aids the child in the task of constructing a grammar for their language.” (Chomsky 1965)

This is known as “Innateness Hypothesis”.

Mechanism of Innate Theory

Now the question is, “What is UG”? It's a theory in linguistics credited to Chomsky, proposing

that the ability to learn grammar is hard-wired into the brain. The theory suggests that linguistic ability manifests itself without being taught and that there are properties that all natural human languages share. It is a matter of observation and experimentation to determine precisely what abilities are innate and what properties are shared by all languages. It contains the core principles underlying any human language and the parameters for any allowable variation that these languages can manifest. UG is not a theory of language acquisition because it does not spell out how the biologically driven process of developing a native-like system of grammar takes place.

UG?????

How does it work in our brain?

The poverty of stimulus paradox (also known as the "logical problem of language acquisition" or "Plato's problem"), concerns the fact that learners come to know more about language than they observe from experience. A child may acquire a language even though the data itself is too poor to determine the language: the child needs no evidence for much of the knowledge she brings to the learning situation. Since the child can fixate on any language in the face of a poverty of stimulus about each language and since all languages are equally acquirable, children all begin with the same universal linguistic knowledge. This is the

essence of the poverty of stimulus argument .

The poverty of stimulus paradox

Another frequently support for UG is the ' no negative evidence in input argument'. Much of the debate around the Poverty of the Stimulus Argument focuses on negative evidence. If there is lot of negative evidence there are more chances that the child’s learning is based on trial and error. Fortunately while making mistakes, children are neither corrected nor do they pay attention to the corrections of adults.

It also makes one wonder about the existence of a predetermined language faculty when considering how universal and uniform the L1 acquisition process is.

Crain and Thornton (2006) list several more subtle points in support of UG. Most of these are highly technical and follow the pattern that a specific linguistic structure or rule is so complex that it could not possibly have emerged spontaneously as a product of experience, without some innate regulations .

Other supports for UG;

Lack of neurobiological validity

The innatism issue

Speed of language acquisition

The selective nature of UG

Grammaticalization and other

experience-based processes.

Problems with UG (Criticism of UG);

UG admittedly does not aim to account for language acquisition processes, because as Lightbown and Spada (2006) declared, several proponents of UG openly admit that the theory is not a good explanation for the acquisition of a second language, especially by learners who have passed the critical period or who receive

formal L2 instruction. Even if one rejects the strong form of nativism that presupposes some built-in language-specific processing module such as UG, there might be a case for a weaker form of form of nativism that would state that some initial human disposition for language learning is required.

A weak form of UG!!! UG and SLA !

This view has been gathering outside the mainstream of linguistics in the fields of cognitive science, psycholinguistics, and developmental psychology and has been increasingly applied to language acquisition and recently to SLA. Within this broad movement there are five interrelated but distinct theoretical approaches: (1) Connectionism; (2) The competition model; (3) Dynamic system theory; (4) Emergentism; and (5) Usage- based theories.

Non-Symbolic psychological theories;

Connectionism refers to a broad approach in cognitive science that utilizes computational modeling as its main research tool and aims at explaining various mental processes within the framework of simple interconnected units (nodes).

Connectionism;

As N. Ellis (2006a) summarizes, 'Connectionist simulations are data-rich and process-light.' The essence of connectionist modeling is to devise systems that are able to find patterns in rich linguistic stimuli fed into the model with only a few relatively uncomplicated algorithms. A successful connectionist model can exhibit rule-like behavior even though no rules are explicitly represented and no symbols are passed around in the network.

One of the most powerful determiners in a connectionist architecture simulating language acquisition is the frequency of the various elements in the language input. Items that are frequent in the input increase the connection weights between the nodes in the network, and the output of such models can show that seemingly rule-like behavior can emerge simply from the processing of input, as a function of the consistency and frequency of various input properties.

Connectionist simulation of language processing

Architecture;

A connectionist system (often called a neural network) consists of a large number of processing units (i.e. nodes) that are linked together in some way. There are at least three types of nodes: input nodes, which receive information, output nodes, which represent the outcome of the model's processing work, and hidden nodes, which are within the network, between the input and output nodes, often in various layers. Depending on the nature of the nodes, neural networks can vary

greatly .

Connectionist architectures or neural networks

In the purest form of parallel distributed processing (PDP), the nodes themselves do not represent any distinct piece of information because it is the pattern of activation that carries knowledge. In this network type, any piece of information is made up of the contribution of many processing units (hidden nodes) contributing to it.

Another connectionist

principle is the localist network. A connectionist network in which single, dedicated units represent distinct pieces of information without any hidden nodes in between. Such a system can be connectionist if it allows for the spreading of activation from one unit to the next in

a parallel manner .

PDP Localist Network

The incremental adjustment of the connection weights in a network to reach a certain level of input accuracy is called error correcting or supervised learning. The most common form of this learning type is back propagation(which is an abbreviation of backward propagation of errors.) and the bulk of the work in the modelling of language processes has used this approach (MacWhinney 2001c). Backpropagation involves a stepwise process, whereby the output of the network is compared to the desired output, and the error for each output neuron is calculated.

A comprehensive neural network simulating language processing might include hybrid- that is, both symbolic and subsymbolic- elements, resulting in a combination of a parallel distributed and a localist network.

Backpropagation and Hybrid Model

Questionable neurobiological plausibility ;

The limitations of simple associative learning

mechanisms; Lack of contextual sensitivity.

Problems with connectionism

The core of the competition model is a distributed connectionist network that links the form of an input sentence to the function of that form, that is, its meaning in context. In deciphering the sentence function, the listener/ reader is unconsciously looking for various cues in the input stream.

In this model there are multiple cues that compete with each other, the winning clue determines the perceived meaning of the sentence.

The competition model

In their experiments MacWhinney and Bates (1994) specified the following four cue types: (1) the linear surface position of the argument vis-à-vis its head; (2) the affixes attached to the argument; (3) the affixes attached to the head; and (4) the inherent lexical semantic features of the argument. A key characteristic of the competition model is the assumption that linguistic knowledge is 'probabilistic all the way down'. Cue strength depends entirely on the cue's reliability and availability in the input. Thus, the competition model offers a theoretical explanation of how the distributional properties of the input control language learning and language processing.

The competition model

With this model, MacWhinney (2005,2008) made an attempt to broaden the scope of the competition model, which was not designed to give a full account of SLA, so that it can cover the acquisition and processing of both L1 and L2 in a unified model. There are two major differences between competition and unified model: (1) several new components were added to the paradigm; and (2) instead of a PDP network, MacWhinney used a different type of neural network, a self-organizing map or 'Kohonen map', as the underlying organizational framework.

The unified model of language acquisition

The construct of cue cost is added to the model, referring to limited cue evaluation when quick online decisions are called for.

A further new component is the concept of resonance, which MacWhinney (2008) considers the most important area of theoretical development. It was inspired by the neurobiological understanding of effective vocabulary learning. MacWhinney argues that resonance can be used to consolidate new forms on the phonological, lexical, and construction levels. It is also thought to be involved in code-switching: if a language is repeatedly accessed, it will be a highly resonant state, delaying or interfering with the activation of another language .

Added components

the unified model views long-term linguistic knowledge as organized into a series of self-organizing maps (Kohonen maps). In these neural networks, nodes with the highest activation are moved in the direction of the input pattern, thus creating a spatial structure that reflects the activation pattern. Thus these maps generate a representation of the input sample with additional topographical visualization .

Self-organizing maps

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