Models of the Emergence of Language

MacWhinney, Brian

doi:10.1146/annurev.psych.49.1.199

Cited by 167 publications

(97 citation statements)

References 117 publications

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“…A fundamental feature of these models is that they view mental processing in terms of interaction and connection, rather than strict modularity and separation. Although connectionist models often postulate some types of modules, they tend to view these modules as emergent and permeable (MacWhinney, 1998), rather than innate and encapsulated (Fodor, 1983).…”

Section: The Learnermentioning

confidence: 99%

The competition model: the input, the context, and the brain

MacWhinney¹

2001

Cognition and Second Language Instruction

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155

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Language learning is a three-way interaction between the input, the learner, and the interactional context (Bloom, 1974). This three-way interaction provides a general framework for understanding first and second language acquisition, in both naturalistic and formal contexts. In order to elaborate this general framework, we need to model its three components:1. The input. We need to know how the linguistic input can be structured to maximize effective learning. What aspects of the phonology, syntax, semantics, and morphology of the input does the learner use to "crack the code" of the new language? 2. The learner. We need to understand exactly how the cognitive abilities of the learner shape the process and outcome of second language instruction. 3. The context. Traditionally, the classroom environment maintains a rather uniform structure in which interaction is controlled by the instructor. How does this framework affect learning and how can it be varied to improve the learning process?This paper will examine these three components within the framework of the Competition Model (MacWhinney, 1987; MacWhinney & Bates, 1989). To quantify the role of the input in second language learning, the model relies on the concepts of cue reliability and availability. To characterize the cognitive abilities of the learner, the model relies on findings from cognitive neuroscience. To understand the role of the context, the model elaborates the concepts of environmental and social support. The Competition Model views both first and second language learning as constructive, data-driven processes that rely not on universals of linguistic structure, but on universals of cognitive structure. It attributes development to learning and transfer, rather than to the principles and parameters of Universal Grammar (Chomsky, 1965). The InputThe Competition Model is designed to quantify the ways in which distributional properties of the input control language learning and language processing. The basic claim of the model in regard to input is that language comprehension is based on the detection of a series of cues and the reliability and availability of these determines the strength of cues in comprehension. The model contrasts sharply with generative grammar in this regard. Generative grammar views language through the lens of abstract trees in a universal deep structure. The Competition Model recognizes the importance of surface phrase structure, but relates all sentence processing to cue detection and interpretation. Those cues that are highest in reliability and availability are the ones that most strongly control comprehension and which are acquired first during language learning.In order to elaborate this simple relation between cues and comprehension, we need to specify how languages distribute cues across sentences. To do this, the Competition Model has turned to crosslinguistic studies of sentence processing. In particular, the forms or

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Section: The Learnermentioning

confidence: 99%

The competition model: the input, the context, and the brain

MacWhinney¹

2001

Cognition and Second Language Instruction

Self Cite

155

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“…Research in the Competition Model framework has shown that the speed at which individuals learn and process a specific form-function relationship reflects the frequency of various cues in the language. For example, cues such as word order, case marking, and animacy can correlate to express agency (MacWhinney, 1998). 1 Although there is considerable research indicating that individuals are sensitive to frequency of events, there is also evidence that use of frequency information is not the automatic occurrence that Ellis suggests.…”

Section: The Language Learner As An Intuitive Statisticianmentioning

confidence: 99%

Input-Driven Language Learning

Harrington

Dennis

2002

Stud Second Lang Acquis

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Input-driven models provide an explicit and readily testable account of language learning. Although we share Ellis's view that the statistical structure of the linguistic environment is a crucial and, until recently, relatively neglected variable in language learning, we also recognize that the approach makes three assumptions about cognition and language learning that are not universally shared. The three assumptions concern (a) the language learner as an intuitive statistician, (b) the constraints on what constitute relevant surface cues, and (c) the redescription problem faced by any system that seeks to derive abstract grammatical relations from the frequency of co-occurring surface forms and functions. These are significant assumptions that must be established if input-driven models are to gain wider acceptance. We comment on these issues and briefly describe a distributed, instance-based approach that retains the key features of the input-driven account advocated by Ellis but that also addresses shortcomings of the current approaches.

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“…The linguistic abilities of an artificial agent whose behavior is established by a computational model is strictly dependent on, and grounded in, other perceptual and motor skills (MacWhinney, 1998;Cangelosi & Riga, 2006;Cangelosi & Parisi, 2002). Such a grounded and embodied approach to language design is consistent with the theories of the grounding of language discussed above.…”

Section: Introductionmentioning

confidence: 99%