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The Domain-General Multiple Demand Network Is More Active in Early Balanced Bilinguals Than Monolinguals During Executive Processing
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In: MIT Press (2022)
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Grammatical cues are largely, but not completely, redundant with word meanings in natural language ...
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An ERP index of real-time error correction within a noisy-channel framework of human communication.
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Abstract:
Recent evidence suggests that language processing is well-adapted to noise in the input (e.g., spelling or speech errors, misreading or mishearing) and that comprehenders readily correct the input via rational inference over possible intended sentences given probable noise corruptions. In the current study, we probed the processing of noisy linguistic input, asking whether well-studied ERP components may serve as useful indices of this inferential process. In particular, we examined sentences where semantic violations could be attributed to noise-for example, in "The storyteller could turn any incident into an amusing antidote", where the implausible word "antidote" is orthographically and phonologically close to the intended "anecdote". We found that the processing of such sentences-where the probability that the message was corrupted by noise exceeds the probability that it was produced intentionally and perceived accurately-was associated with a reduced (less negative) N400 effect and an increased P600 effect, compared to semantic violations which are unlikely to be attributed to noise ("The storyteller could turn any incident into an amusing hearse"). Further, the magnitudes of these ERP effects were correlated with the probability that the comprehender retrieved a plausible alternative. This work thus adds to the growing body of literature that suggests that many aspects of language processing are optimized for dealing with noise in the input, and opens the door to electrophysiologic investigations of the computations that support the processing of imperfect input.
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Keyword:
Cognitive Sciences; Communication; Electroencephalography; Evoked Potentials; Experimental Psychology; Female; Humans; Language; Male; Neurosciences; Psychology; Semantics
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URL: https://escholarship.org/uc/item/8f72t5x7
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Syntactic dependencies correspond to word pairs with high mutual information
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In: Association for Computational Linguistics (2021)
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The Language Network is Recruited but Not Required for Nonverbal Event Semantics
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In: MIT Press (2021)
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Breakdowns in Informativeness of Naturalistic Speech Production in Primary Progressive Aphasia
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In: MDPI (2021)
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Lack of selectivity for syntax relative to word meanings throughout the language network
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In: PMC (2021)
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Neural encoding and production of functional morphemes in the posterior temporal lobe
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In: Nature (2021)
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Syntactic dependencies correspond to word pairs with high mutual information
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In: Association for Computational Linguistics (2021)
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Breakdowns in Informativeness of Naturalistic Speech Production in Primary Progressive Aphasia
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In: Multidisciplinary Digital Publishing Institute (2021)
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An ERP index of real-time error correction within a noisy-channel framework of human communication
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In: bioRxiv (2021)
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Do domain-general executive resources play a role in linguistic prediction? Re-evaluation of the evidence and a path forward
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In: Prof. Fedorenko (2021)
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No evidence for differences among language regions in their temporal receptive windows
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In: Elsevier (2021)
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No evidence for differences among language regions in their temporal receptive windows
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In: Elsevier (2021)
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Comprehension of computer code relies primarily on domain-general executive brain regions
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In: eLife (2021)
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Functional Network Dynamics of the Language System
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In: Oxford University Press (2021)
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The Small and Efficient Language Network of Polyglots and Hyper-polyglots
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In: bioRxiv (2021)
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The domain-general multiple demand (MD) network does not support core aspects of language comprehension: a large-scale fMRI investigation
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In: Journal of Neuroscience (2021)
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Similarity of computations across domains does not imply shared implementation: The case of language comprehension
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In: Curr Dir Psychol Sci (2021)
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