Pervasive behavioral and neural evidence for predictive processing has led to claims that language processing depends upon predictive coding. In some cases, this may reflect a conflation of terms, but predictive coding formally is a computational mechanism where only deviations from top- down expectations are passed between levels of representation. We evaluate three models’ ability to simulate predictive processing and ask whether they exhibit the putative hallmark of formal predictive coding (reduced signal when input matches expectations). Of crucial interest, TRACE, an interactive activation model that does not explicitly implement prediction, exhibits both predictive processing and model- internal signal reduction. This 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.
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Does signal reduction imply predictive coding in models of spoken word recognition?
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.
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- NSF-PAR ID:
- 10281208
- Date Published:
- Journal Name:
- Psychonomic Bulletin & Review
- ISSN:
- 1069-9384
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3758/s13423-021-01924-x
