Alignment as a consequence of expectation adaptation: Syntactic priming is affected by the prime's prediction error given both prior and recent experience
T. Florian Jaeger,Neal Snider +1 more
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The guiding hypothesis is that syntactic priming is a consequence of a language processing system that is organized to achieve efficient communication in an ever-changing (subjectively non-stationary) environment.About:
This article is published in Cognition.The article was published on 2013-04-01 and is currently open access. It has received 316 citations till now. The article focuses on the topics: Structural priming & Priming (psychology).read more
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An integrated theory of language production and comprehension
Martin J. Pickering,Simon Garrod +1 more
TL;DR: It is asserted that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other.
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What do we mean by prediction in language comprehension
TL;DR: It is argued that the bulk of behavioural and neural evidence suggests that the authors predict probabilistically and at multiple levels and grains of representation, and that all these properties of language understanding can be naturally explained and productively explored within a multi-representational hierarchical actively generative architecture.
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Robust speech perception: recognize the familiar, generalize to the similar, and adapt to the novel.
TL;DR: The ideal adapter framework is formalized and can be understood as inference under uncertainty about the appropriate generative model for the current talker, thereby facilitating robust speech perception despite the lack of invariance.
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The P-chain: relating sentence production and its disorders to comprehension and acquisition
Gary S. Dell,Franklin Chang +1 more
TL;DR: The P-chain is introduced, an emerging framework for theory in psycholinguistics that unifies research on comprehension, production and acquisition that proposes that language processing involves incremental prediction, which is carried out by the production system.
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Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
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Finding Structure in Time
TL;DR: A proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory and suggests a method for representing lexical categories and the type/token distinction is developed.
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Information Theory, Inference and Learning Algorithms
TL;DR: A fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
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Information theory, inference, and learning algorithms
TL;DR: In this paper, the mathematics underpinning the most dynamic areas of modern science and engineering are discussed and discussed in a fun and exciting textbook on the mathematics underlying the most important areas of science and technology.