Literature DB >> 25869618

The Now-or-Never bottleneck: A fundamental constraint on language.

Morten H Christiansen1, Nick Chater2.   

Abstract

Memory is fleeting. New material rapidly obliterates previous material. How, then, can the brain deal successfully with the continual deluge of linguistic input? We argue that, to deal with this "Now-or-Never" bottleneck, the brain must compress and recode linguistic input as rapidly as possible. This observation has strong implications for the nature of language processing: (1) the language system must "eagerly" recode and compress linguistic input; (2) as the bottleneck recurs at each new representational level, the language system must build a multilevel linguistic representation; and (3) the language system must deploy all available information predictively to ensure that local linguistic ambiguities are dealt with "Right-First-Time"; once the original input is lost, there is no way for the language system to recover. This is "Chunk-and-Pass" processing. Similarly, language learning must also occur in the here and now, which implies that language acquisition is learning to process, rather than inducing, a grammar. Moreover, this perspective provides a cognitive foundation for grammaticalization and other aspects of language change. Chunk-and-Pass processing also helps explain a variety of core properties of language, including its multilevel representational structure and duality of patterning. This approach promises to create a direct relationship between psycholinguistics and linguistic theory. More generally, we outline a framework within which to integrate often disconnected inquiries into language processing, language acquisition, and language change and evolution.

Keywords:  chunking; grammaticalization; incremental interpretation; language acquisition; language evolution; language processing; online learning; prediction; processing bottleneck; psycholinguistics

Mesh:

Year:  2015        PMID: 25869618     DOI: 10.1017/S0140525X1500031X

Source DB:  PubMed          Journal:  Behav Brain Sci        ISSN: 0140-525X            Impact factor:   12.579


  62 in total

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2.  The evolution of cognitive mechanisms in response to cultural innovations.

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3.  Cross-linguistic differences in the use of durational cues for the segmentation of a novel language.

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Review 4.  What's statistical about learning? Insights from modelling statistical learning as a set of memory processes.

Authors:  Erik D Thiessen
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2017-01-05       Impact factor: 6.237

Review 5.  Using experiential optimization to build lexical representations.

Authors:  Brendan T Johns; Michael N Jones; D J K Mewhort
Journal:  Psychon Bull Rev       Date:  2019-02

6.  Zebra finches are sensitive to combinations of temporally distributed features in a model of word recognition.

Authors:  Jeffrey M Knowles; Allison J Doupe; Michael S Brainard
Journal:  J Acoust Soc Am       Date:  2018-08       Impact factor: 1.840

7.  Decoding the neural representation of story meanings across languages.

Authors:  Morteza Dehghani; Reihane Boghrati; Kingson Man; Joe Hoover; Sarah I Gimbel; Ashish Vaswani; Jason D Zevin; Mary Helen Immordino-Yang; Andrew S Gordon; Antonio Damasio; Jonas T Kaplan
Journal:  Hum Brain Mapp       Date:  2017-09-20       Impact factor: 5.038

8.  Lossy-Context Surprisal: An Information-Theoretic Model of Memory Effects in Sentence Processing.

Authors:  Richard Futrell; Edward Gibson; Roger P Levy
Journal:  Cogn Sci       Date:  2020-03

9.  Dynamic re-weighting of acoustic and contextual cues in spoken word recognition.

Authors:  Wednesday Bushong; T Florian Jaeger
Journal:  J Acoust Soc Am       Date:  2019-08       Impact factor: 1.840

10.  Language at Three Timescales: The Role of Real-Time Processes in Language Development and Evolution.

Authors:  Bob McMurray
Journal:  Top Cogn Sci       Date:  2016-03-17
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