Do They Know It's Christmash? Lexical Knowledge Directly Impacts Speech Perception.
Computational model
Feedback
Language
Speech perception
Top‐down effects
Journal
Cognitive science
ISSN: 1551-6709
Titre abrégé: Cogn Sci
Pays: United States
ID NLM: 7708195
Informations de publication
Date de publication:
May 2024
May 2024
Historique:
medline:
22
5
2024
pubmed:
22
5
2024
entrez:
22
5
2024
Statut:
ppublish
Résumé
We recently reported strong, replicable (i.e., replicated) evidence for lexically mediated compensation for coarticulation (LCfC; Luthra et al., 2021), whereby lexical knowledge influences a prelexical process. Critically, evidence for LCfC provides robust support for interactive models of cognition that include top-down feedback and is inconsistent with autonomous models that allow only feedforward processing. McQueen, Jesse, and Mitterer (2023) offer five counter-arguments against our interpretation; we respond to each of those arguments here and conclude that top-down feedback provides the most parsimonious explanation of extant data.
Types de publication
Journal Article
Letter
Langues
eng
Sous-ensembles de citation
IM
Pagination
e13449Subventions
Organisme : National Science Foundation
ID : BCS-PAC2043903
Organisme : Basque Government
ID : BERC 2022-2025
Organisme : Spanish State Research Agency
ID : CEX2020-001010-S
Organisme : Spanish State Research Agency
ID : PID2020-119131GB-I00
Informations de copyright
© 2024 Cognitive Science Society LLC.
Références
Bushong, W., & Jaeger, T. F. (2019). Dynamic re‐weighting of acoustic and contextual cues in spoken word recognition. Journal of the Acoustical Society of America, 146(2), EL135–EL140.
Cummings, S. N., & Theodore, R. M. (2023). Hearing is believing: Lexically guided perceptual learning is graded to reflect the quantity of evidence in speech input. Cognition, 235, 105404.
Dienes, Z. (2014). Using Bayes to get the most out of non‐significant results. Frontiers in Psychology, 5, 781.
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7, 195–225.
Elman, J. L., & McClelland, J. L. (1988). Cognitive penetration of the mechanisms of perception: Compensation for coarticulation of lexically restored phonemes. Journal of Memory and Language, 27(2), 143–165.
Firestone, C., & Scholl, B. J. (2016). Cognition does not affect perception: Evaluating the evidence for “top‐down” effects. Behavioral and Brain Sciences, 39, 1–77.
Ganong, W. F. (1980). Phonetic categorization in auditory word perception. Journal of Experimental Psychology: Human Perception and Performance, 6(1), 110–125.
Hope, R. M. (2022). Rmisc: Ryan Miscellaneous. R package version 1.5.1. Retrieved from https://CRAN.R‐project.org/package=Rmisc.
Jesse, A., & Kaplan, E. (2019). Attentional resources contribute to the perceptual learning of talker idiosyncrasies in audiovisual speech. Attention, Perception, & Psychophysics, 81(4), 1006–1019.
Jurafsky, D., & Martin, J. H. (forthcoming). Speech and language processing (3rd edition draft). Retrieved March 4, 2024, from https://web.stanford.edu/~jurafsky/slp3/
Kraljic, T., & Samuel, A. G. (2007). Perceptual adjustments to multiple speakers. Journal of Memory and Language, 56(1), 1–15.
Lupyan, G. (2015). Cognitive penetrability of perception in the age of prediction: Predictive systems are penetrable systems. Review of Philosophy and Psychology, 6(4), 547–569.
Luthra, S., Peraza‐Santiago, G., Beeson, K. N., Saltzman, D., Crinnion, A. M., & Magnuson, J. S. (2021). Robust lexically mediated compensation for coarticulation: Christmash time is here again. Cognitive Science, 45(4), e12962.
Magnuson, J. S., & Luthra, S. Simple Recurrent Networks are interactive. Submitted.
Magnuson, J. S., McMurray, B., Tanenhaus, M. K., & Aslin, R. N. (2003a). Lexical effects on compensation for coarticulation: The ghost of Christmash past. Cognitive Science, 27(2), 285–298.
Magnuson, J. S., McMurray, B., Tanenhaus, M. K., & Aslin, R. N. (2003b). Lexical effects on compensation for coarticulation: A tale of two systems? Cognitive Science, 27(5), 801–805.
Magnuson, J. S., Mirman, D., Luthra, S., Strauss, T., & Harris, H. D. (2018). Interaction in spoken word recognition models: Feedback helps. Frontiers in Psychology, 9, 1–18.
Mann, V. A., & Repp, B. H. (1981). Influence of preceding fricative on stop consonant perception. Journal of the Acoustical Society of America, 69(2), 548–558.
McClelland, J. L., & Elman, J. L. (1986). The TRACE model of speech perception. Cognitive Psychology, 18(1), 1–86.
McQueen, J. M., Jesse, A., & Mitterer, H. (2023). Lexically‐mediated compensation for coarticulation still as elusive as a white Christmash. Cognitive Science, 47, e13342.
McQueen, J. M., Jesse, A., & Norris, D. (2009). No lexical–prelexical feedback during speech perception or: Is it time to stop playing those Christmas tapes? Journal of Memory and Language, 61(1), 1–18.
Norris, D. (1993). Must connectionist models be interactive? In Cognitive Models of Speech Processing: The Second Sperlonga Meeting (pp. 211–234). Psychology Press.
Norris, D., McQueen, J. M., & Cutler, A. (2000). Merging information in speech recognition: Feedback is never necessary. Behavioral and Brain Sciences, 23(3), 299–325.
Norris, D., McQueen, J. M., & Cutler, A. (2016). Prediction, Bayesian inference and feedback in speech recognition. Language, Cognition and Neuroscience, 31(1), 4–18.
Pitt, M. A., & McQueen, J. M. (1998). Is compensation for coarticulation mediated by the lexicon? Journal of Memory and Language, 39(3), 347–370.
Prince, S. J. D. (2023). Understanding deep learning. The MIT Press.
R Core Team. (2022). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R‐project.org/
Rouder, J. N., & Morey, R. D. (2011). A Bayes factor meta‐analysis of Bem's ESP claim. Psychonomic Bulletin & Review, 18, 682–689.
Samuel, A. G. (2016). Lexical representations are malleable for about one second: Evidence for the non‐automaticity of perceptual recalibration. Cognitive Psychology, 88, 88–114.
Samuel, A. G., & Pitt, M. A. (2003). Lexical activation (and other factors) can mediate compensation for coarticulation. Journal of Memory and Language, 48(2), 416–434.