Does signal reduction imply predictive coding in models of spoken word recognition?
Date
2021Author
Luthra, Sahil
Li, Monica Y. C.
You, Heejo
Brodbeck, Christian
Magnuson, James S.
Metadata
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Luthra, S., Li, M.Y.C., You, H. et al. Does signal reduction imply predictive coding in models of spoken word recognition?. Psychon Bull Rev 28, 1381–1389 (2021). https://doi.org/10.3758/s13423-021-01924-x
Abstract
Pervasive behavioral and neural evidence for predictive processing has led to claims that language processing depends upon
predictive coding. Formally, predictive coding is a computational mechanism where only deviations from top-down expectations
are passed between levels of representation. In many cognitive neuroscience studies, a reduction of signal for expected inputs is
taken as being diagnostic of predictive coding. In the present work, we show that despite not explicitly implementing prediction,
the TRACE model of speech perception exhibits this putative hallmark of predictive coding, with reductions in total lexical
activation, total lexical feedback, and total phoneme activation when the input conforms to expectations. These findings may
indicate that interactive activation is functionally equivalent or approximant to predictive coding or that caution is warranted in
interpreting neural signal reduction as diagnostic of predictive coding.