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A verb-frame frequency account of constraints on long-distance dependencies in English
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In: Prof. Gibson (2022)
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| 2 |
Dependency locality as an explanatory principle for word order
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In: Prof. Levy (2022)
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| 3 |
Extraction from subjects: Differences in acceptability depend on the discourse function of the construction
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In: Prof. Gibson (2022)
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| 4 |
Syntactic dependencies correspond to word pairs with high mutual information
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In: Association for Computational Linguistics (2021)
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| 5 |
Word Order Predicts Cross‐Linguistic Differences in the Production of Redundant Color and Number Modifiers
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In: MIT web domain (2021)
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| 6 |
Communication efficiency of color naming across languages provides a new framework for the evolution of color terms
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In: PMC (2021)
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| 7 |
The Natural Stories corpus: a reading-time corpus of English texts containing rare syntactic constructions
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In: Springer Netherlands (2020)
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| 8 |
How Efficiency Shapes Human Language ; How Efficiency Shapes Human Language, TICS 2019
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In: Prof. Levy via Courtney Crummett (2019)
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| 9 |
Comprehenders model the nature of noise in the environment
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In: PMC (2019)
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Abstract:
In everyday communication, speakers make errors and produce language in a noisy environment. Recent work suggests that comprehenders possess cognitive mechanisms for dealing with noise in the linguistic signal: a noisy-channel model. A key parameter of these models is the noise model: the comprehender's implicit model of how noise affects utterances before they are perceived. Here we examine this noise model in detail, asking whether comprehension behavior reflects a noise model that is adapted to context. We asked readers to correct sentences if they noticed errors, and manipulated context by including exposure sentences containing obvious deletions (A bystander was rescued by the fireman in the nick time.), insertions, exchanges, mixed errors, or no errors. On test sentences (The bat swung the player.), participants’ corrections differed depending on the exposure condition. The results demonstrate that participants model specific types of errors and make inferences about the intentions of the speaker accordingly. Keywords: Sentence comprehension; Noisy-channel; Rational inference; Adaptation; Error correction ; National Institutes of Health (U.S.) (Grant F32DC015163) ; National Science Foundation (U.S.). Linguistics Program (Grant 1534318)
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Keyword:
Cognitive Neuroscience; Developmental and Educational Psychology; Experimental and Cognitive Psychology; Language and Linguistics; Linguistics and Language
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URL: https://hdl.handle.net/1721.1/125134
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| 10 |
Partial Truths: Adults Choose to Mention Agents and Patients in Proportion to Informativity, Even If It Doesn’t Fully Disambiguate the Message
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In: MIT Press (2019)
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| 11 |
Word Forms Are Structured for Efficient Use
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In: Prof. Gibson via Courtney Crummett (2018)
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| 12 |
Color naming across languages reflects color use
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In: National Academy of Sciences (2018)
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| 13 |
Tracking Colisteners’ Knowledge States During Language Comprehension
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In: Prof. Gibson via Courtney Crummett (2018)
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| 14 |
SNAP judgments: A small N acceptability paradigm (SNAP) for linguistic acceptability judgments: Online Appendices
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In: Language (2018)
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| 15 |
Words cluster phonetically beyond phonotactic regularities
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In: Prof. Gibson via Courtney Crummett (2017)
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| 16 |
A meta-analysis of syntactic priming in language production
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In: Prof. Gibson via Courtney Crummett (2016)
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| 17 |
Wordform Similarity Increases With Semantic Similarity: An Analysis of 100 Languages
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In: Prof. Gibson via Courtney Crummett (2016)
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| 18 |
Processing temporal presuppositions: an event-related potential study
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In: Prof. Gibson via Courtney Crummett (2016)
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| 19 |
L2 processing as noisy channel language comprehension
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In: Prof. Gibson via Courtney Crummett (2016)
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| 20 |
Don’t Underestimate the Benefits of Being Misunderstood
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In: Prof. Gibson via Courtney Crummett (2016)
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