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Caramazza and Costa, 2000 (Cognition 75, B51 B64) report three picture-word interference experiments testing the response set mechanism of the WEAVER++ model of spoken word production.
Others speculate that in the case of speech generating devices, the model of spoken output leads to an increase in speech production.
Our results support assumptions of the FUL model of spoken word recognition [ 2- 4].
Regarding the neural basis of language comprehension, a linguistically based model of spoken language comprehension discriminates between segmental and suprasegmental information of speech (Friederici and Alter 2004).
We propose a predictive coding model of spoken word recognition in which STG neurons represent the difference between predicted and heard speech sounds.
The FUL model of spoken word recognition [ 2- 4] provides excellent theoretical underpinning for the discovery of neurophysiological mechanisms underlying human speech comprehension.
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Even a representative of a minority organisation that has benefited from funding thought the model of speaking to people via community leaders has long had its day.
In the framework of pre-lexical phonological representations and lexical word form representations sketched by classical models of spoken word recognition, the facilitation effect exerted by syllable prosody might have at least two origins.
The dominant proposal in current computational models of spoken word recognition is that multiple lexical candidates are activated in parallel and compete through inhibitory connections [ 7] or other, functionally equivalent lexical mechanisms [ 8, 9].
Third, asymmetric pseudoword effects for coronal and non-coronal pseudowords cannot be captured by models of spoken word recognition that assume that all segments, usually phonemes, equally contribute to lexical activation (e.g., [ 42- 44]).
Any attempt to assess speaking requires a clear understanding of the multi-faceted nature of speaking, as described in theories and models of speaking in a second or foreign language, which are operationalised in the constructs and sub-constructs that underlie that skill.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com