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Information processing (IP) is a theoretical framework that focuses on cognitive process involved in second language acquisition (SLA). IP came into existences when behaviorist view of learning language failed to address the cognitive process involved in SLA. Although IP emphasis on learning (SLA) to be an internal process just like nativist theory, it sees learning language to be no different from learning any other forms of skills. Nativist, on other hand, posits certain aspects of learning language to be innate; that is every child has access to language acquisition device (LAD)-Universal Grammar (UG). Based on the aforementioned assumption IP views language as hierarchical sets of skills, ranging from lower-level skills to higher-level skills. Acquisitions of higher skills are impossible without first acquiring lower sets of skills (Saville-Troike & Barto, 2017).
According to IP such skills are learned in 3 stages: input (perception), central processing and, output (production). IP beings with sensory perception of information (input). Input as Troika and Barto (2005) states is any representative sample of target language that the learners come across with. Krashen (1985) stated that language is acquired from the comprehensive input that is slightly higher than the current competency (i +1). However, it is of paramount importance to qualify that not all input becomes intake. For input to become intake, it will depend on the attentional demand of input.
Cherry (1953) stated in her well known Cocktail party effect that despite seeing and hearing so much of information on daily basis, people will only select necessary information and discard the remaining. True to her words, attention acts like a filter only letting in information (input) that has the highest attentional demand. Such selective function also helps to prevent information overload from happening.Once input becomes intake, the information goes through central processing. It is comprised of controlled processing (short-term memory/STM) and automatic processing (long-term memory/LTM). Humans cannot process everything simultaneously as we, by nature, are limited processor; therefore, controlled processing is limited in number as it exerts higher demand on cognitive skills (Saville-Troike & Barto, 2017).
If a learner hears any idiosyncratic sentence in TG, it will be analyzed semantically- trying to make sense out of it. Processed data then move to automatic processing where they are stored for future retrieval. Stored data are said to be automatized, in another words they become schema or schemata. Schema in layman’s term can be understood as background knowledge. Whenever, new information is processed in STM, they are compared with existing schema to aid the learning process. Automatized data are stored for a lifetime and they are easy to access on demand there by exerting less demand on cognitive skills. Saville-Troike & Barto (2017) asserts that learning happens when learners move from control processing to automatic processing.
John Anderson in his ACT* and ACT-R theory explains similar concept through declarative knowledge (DK) and procedural knowledge (PK). DK is an explicit knowledge, which is having the “knowledge about”, for instance having the knowledge that sun, rises from the east. PK on other hand is an implicit knowledge that is having the knowledge of “how to do” like knowing how to drive a car. Hence, it can be concluded that learning occurs when a learner develops from declarative to procedural stage.Automatized data undergo constant reshaping and restructuring as seen in the phenomenon known as U-shaped development (adopted from Saville-Troike & Barto, 2017). When a learner goes through U-shaped development, they initially produce target like production but then later produces incorrect production- known in U-shaped development as backsliding. Later through some negative evidence the learner will again produce the correct production.
Final stage of IP is the output. In SLA output is the final production of the TG. Before output was merely viewed as the final production without being given due recognition to its role in SLA. Even Krashen’s comprehensible input hypothesis gives little to no emphasis on the role of output. Merrill Swain in her Output Hypothesis first discussed prominent role of output in SLA. Swain (1988) claimed that a successful learning happens when learners are obliged to produce comprehensible output, other wise input alone is not enough. Output brings about noticing function through which learners can identify a gap in their knowledge and work accordingly to improve. Output also helps learner to get real time feedback of their production and at a same time reap the benefit of its metalinguistic functions.Psychological perspective of language learning answers the question of how a learner learns language; IP being paradigm of psychological perspective has lots of implications in teaching and learning process.
Understanding IP can provide insight into role of attention, memory functions, and distributed practice that are necessary for learning. IP tells us that attention is the precursor of learning. It is mandatory that students are aware of what they are learning. As a language teacher one should model strategies to channelize learner’s attention. Voice modulation can avoid monotonousness from setting in; thus, keeping students attentive. Walking around while lecturing rather than just sitting often works wonders as it keeps learners on their toes and attentive. Using memory aids such as visual imagery can help learners to remember the concept easily. Distributed practice can help to automatize the information faster. Resorting to rote drill, frequent test or even simple revision compels learners to keep in constant touch with lessons; thus, facilitating the information to be recalled easily at will.
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