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The Magic of words reconsidered : investigating the automaticity of reading color-neutral words in the Stroop task
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What causes the greater perceived similarity of consonant-transposed nonwords?
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Prediction, Bayesian inference and feedback in speech recognition
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Abstract:
Speech perception involves prediction, but how is that prediction implemented? In cognitive models prediction has often been taken to imply that there is feedback of activation from lexical to pre-lexical processes as implemented in interactive-activation models (IAMs). We show that simple activation feedback does not actually improve speech recognition. However, other forms of feedback can be beneficial. In particular, feedback can enable the listener to adapt to changing input, and can potentially help the listener to recognise unusual input, or recognise speech in the presence of competing sounds. The common feature of these helpful forms of feedback is that they are all ways of optimising the performance of speech recognition using Bayesian inference. That is, listeners make predictions about speech because speech recognition is optimal in the sense captured in Bayesian models.
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Original Articles
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4685608/ https://doi.org/10.1080/23273798.2015.1081703
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Bottoms up! How top-down pitfalls ensnare speech perception researchers, too
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30 |
Prediction, Bayesian inference and feedback in speech recognition
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31 |
Transposed-letter priming effects in reading aloud words and nonwords
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The Representation of Order Information in Auditory-Verbal Short-Term Memory
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Pseudohomophone priming in lexical decision is not fragile in a sparse lexical neighborhood
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