Multivariate analysis of brain activity patterns as a tool to understand predictive processes in speech perception

References

• Abrams, D. A., Ryali, S., Chen, T., Balaban, E., Levitin, D. J., & Menon, V. (2013). Multivariate activation and connectivity patterns discriminate speech intelligibility in Wernicke’s, Broca’s, and Geschwind’s areas. Cerebral Cortex, 23(7), 1703–1714. https://doi.org/10.1093/cercor/bhs165
   PubMed Web of Science ®Google Scholar
• Acunzo, D. J., Low, D. M., & Fairhall, S. L. (2022). Deep neural networks reveal topic-level representations of sentences in medial prefrontal cortex, lateral anterior temporal lobe, precuneus, and angular gyrus. NeuroImage, 251, 119005. https://doi.org/10.1016/j.neuroimage.2022.119005
   PubMed Web of Science ®Google Scholar
• Aglieri, V., Cagna, B., Velly, L., Takerkart, S., & Belin, P. (2021). FMRI-based identity classification accuracy in left temporal and frontal regions predicts speaker recognition performance. Scientific Reports, 11(489). https://doi.org/10.1038/s41598-020-79922-7
   Google Scholar
• Aitchison, L., & Lengyel, M. (2017). With or without You: Predictive coding and Bayesian inference in the brain. Current Opinion in Neurobiology, 46, 219–227. https://doi.org/10.1016/j.conb.2017.08.010
   PubMed Web of Science ®Google Scholar
• Alderson-Day, B., Lima, C. F., Evans, S., Krishnan, S., Shanmugalingam, P., Fernyhough, C., & Scott, S. K. (2017). Distinct processing of ambiguous speech in people with non-clinical auditory verbal hallucinations. Brain, 140(9), 2475–2489. https://doi.org/10.1093/brain/awx206
   PubMed Web of Science ®Google Scholar
• Alink, A., & Blank, H. (2021). Can expectation suppression be explained by reduced attention to predictable stimuli? NeuroImage, 231, 117824. https://doi.org/10.1016/j.neuroimage.2021.117824
   PubMed Web of Science ®Google Scholar
• Allen, K., Pereira, F., Botvinick, M., & Goldberg, A. E. (2012). Distinguishing grammatical constructions with fMRI pattern analysis. Brain and Language, 123(3), 174–182. https://doi.org/10.1016/j.bandl.2012.08.005
   PubMed Web of Science ®Google Scholar
• Arana, S., Marquand, A., Hultén, A., Hagoort, P., & Schoffelen, J.-M. (2020). Sensory-modality independent activation of the brain network for language. Journal of Neuroscience, https://doi.org/10.1523/JNEUROSCI.2271-19.2020
   Web of Science ®Google Scholar
• Archila-Meléndez, M. E., Valente, G., Correia, J. M., Rouhl, R. P. W., Kranen-Mastenbroek, V. H., & Jansma, B. M. (2018). Sensorimotor representation of speech perception. Cross-decoding of place of articulation features during selective attention to syllables in 7T fMRI. Eneuro, 5(2). https://doi.org/10.1523/ENEURO.0252-17.2018
   Web of Science ®Google Scholar
• Arsenault, J. S., & Buchsbaum, B. R. (2015). Distributed neural representations of phonological features during speech perception. Journal of Neuroscience, 35(2), 634–642. https://doi.org/10.1523/Jneurosci.2454-14.2015
   PubMed Web of Science ®Google Scholar
• Arsenault, J. S., & Buchsbaum, B. R. (2016). No evidence of somatotopic place of articulation feature mapping in motor cortex during passive speech perception. Psychonomic Bulletin & Review, 23(4), 1231–1240. https://doi.org/10.3758/s13423-015-0988-z
   PubMed Web of Science ®Google Scholar
• Baker, B., Lansdell, B., & Kording, K. P. (2022). Three aspects of representation in neuroscience. Trends in Cognitive Sciences, 26(11), 942–958. https://doi.org/10.1016/j.tics.2022.08.014
   PubMed Web of Science ®Google Scholar
• Bejjanki, V. R., Silveira, R. A., Cohen, J. D., & Turk-Browne, N. B. (2017). Noise correlations in the human brain and their impact on pattern classification. PLoS Computational Biology, 13(8), e1005674. https://doi.org/10.1371/journal.pcbi.1005674
   PubMed Web of Science ®Google Scholar
• Blank, H., & Davis, M. H. (2016). Prediction errors but not sharpened signals simulate multivoxel fMRI patterns during speech perception. PLOS Biology, 14(11). https://doi.org/10.1371/journal.pbio.1002577
   Web of Science ®Google Scholar
• Blank, H., Spangenberg, M., & Davis, M. H. (2018). Neural prediction errors distinguish perception and misperception of speech. The Journal of Neuroscience, 38(27), 6076. https://doi.org/10.1523/JNEUROSCI.3258-17.2018
   PubMed Web of Science ®Google Scholar
• Bonte, M., Hausfeld, L., Scharke, W., Valente, G., & Formisano, E. (2014). Task-dependent decoding of speaker and vowel identity from auditory cortical response patterns. Journal of Neuroscience, 34(13), 4548–4557. https://doi.org/10.1523/Jneurosci.4339-13.2014
   PubMed Web of Science ®Google Scholar
• Broderick, M. P., & Lalor, E. C. (2020). Co-existence of prediction and error signals in electrophysiological responses to natural speech. bioRxiv. https://doi.org/10.1101/2020.11.20.391227
   Google Scholar
• Carlson, T. A., Simmons, R. A., Kriegeskorte, N., & Slevc, L. R. (2014). The emergence of semantic meaning in the ventral temporal pathway. Journal of Cognitive Neuroscience, 26(1), 120–131. https://doi.org/10.1162/jocn_a_00458
   PubMed Web of Science ®Google Scholar
• Carota, F., Kriegeskorte, N., Nili, H., & Pulvermüller, F. (2017). Representational similarity mapping of distributional semantics in left inferior frontal, middle temporal, and motor cortex. Cerebral Cortex, 27(1), 294–309. https://doi.org/10.1093/cercor/bhw379
   PubMed Web of Science ®Google Scholar
• Carota, F., Nili, H., Pulvermüller, F., & Kriegeskorte, N. (2021). Distinct fronto-temporal substrates of distributional and taxonomic similarity among words: Evidence from RSA of BOLD signals. NeuroImage, 224, 117408. https://doi.org/10.1016/j.neuroimage.2020.117408
   PubMed Web of Science ®Google Scholar
• Chang, E. F., Rieger, J. W., Johnson, K., Berger, M. S., Barbaro, N. M., & Knight, R. T. (2010). Categorical speech representation in human superior temporal gyrus. Nature Neuroscience, 13(11). https://doi.org/10.1038/nn.2641
   Web of Science ®Google Scholar
• Cichy, R. M., Pantazis, D., & Oliva, A. (2016). Similarity-based fusion of MEG and fMRI reveals spatio-temporal dynamics in human cortex during visual object recognition. Cerebral Cortex, 26(8), 3563–3579. https://doi.org/10.1093/cercor/bhw135
   PubMed Web of Science ®Google Scholar
• Correia, J., Formisano, E., Valente, G., Hausfeld, L., Jansma, B., & Bonte, M. (2014). Brain-based translation: FMRI decoding of spoken words in bilinguals reveals language-independent semantic representations in anterior temporal lobe. Journal of Neuroscience, 34(1), 332–338. https://doi.org/10.1523/JNEUROSCI.1302-13.2014
   PubMed Web of Science ®Google Scholar
• Correia, J., Jansma, B. M. B., & Bonte, M. (2015). Decoding articulatory features from fMRI responses in dorsal speech regions. Journal of Neuroscience, 35(44), 15015–15025. https://doi.org/10.1523/Jneurosci.0977-15.2015
   PubMedGoogle Scholar
• Cui, J., & Weng, B. (2022). Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces. https://doi.org/10.48550/arXiv.2202.06948
   Google Scholar
• Daube, C., Ince, R. A. A., & Gross, J. (2019). Simple acoustic features can explain phoneme-based predictions of cortical responses to speech. Current Biology, 29(12), 1924–1937.e9. https://doi.org/10.1016/j.cub.2019.04.067
   PubMed Web of Science ®Google Scholar
• Davis, M. H., & Johnsrude, I. S. (2003). Hierarchical processing in spoken language comprehension. Journal of Neuroscience, 23(8), 3423–3431. https://doi.org/10.1523/JNEUROSCI.23-08-03423.2003
   PubMed Web of Science ®Google Scholar
• Davis, M. H., & Johnsrude, I. S. (2007). Hearing speech sounds: Top-down influences on the interface between audition and speech perception. Hearing Research, 229(1), 132–147. https://doi.org/10.1016/j.heares.2007.01.014
   PubMed Web of Science ®Google Scholar
• Davis, M. H., Johnsrude, I. S., Hervais-Adelman, A., Taylor, K., & McGettigan, C. (2005). Lexical information drives perceptual learning of distorted speech: Evidence from the comprehension of noise-vocoded sentences. Journal of Experimental Psychology. General, 134(2), 222–241. https://doi.org/10.1037/0096-3445.134.2.222
   PubMed Web of Science ®Google Scholar
• DeLong, K. A., Urbach, T. P., & Kutas, M. (2005). Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8(8). https://doi.org/10.1038/nn1504
   Web of Science ®Google Scholar
• Deniz, F., Nunez-Elizalde, A. O., Huth, A. G., & Gallant, J. L. (2019). The representation of semantic information across human cerebral cortex during listening versus Reading Is invariant to stimulus modality. Journal of Neuroscience, 39(39), 7722–7736. https://doi.org/10.1523/JNEUROSCI.0675-19.2019
   PubMed Web of Science ®Google Scholar
• Deniz, F., Tseng, C., Wehbe, L., & Gallant, J. L. (2021). Semantic representations during language comprehension are affected by context. bioRxiv. https://doi.org/10.1101/2021.12.15.472839
   Google Scholar
• de Zubicaray, G., Arciuli, J., & McMahon, K. (2013). Putting an “End” to the motor cortex representations of action words. Journal of Cognitive Neuroscience, 25(11), 1957–1974. https://doi.org/10.1162/jocn_a_00437
   PubMed Web of Science ®Google Scholar
• Diedrichsen, J., & Kriegeskorte, N. (2017). Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis. PLoS Computational Biology, 13(4). https://doi.org/10.1371/journal.pcbi.1005508
   Web of Science ®Google Scholar
• Ding, N., Melloni, L., Zhang, H., Tian, X., & Poeppel, D. (2016). Cortical tracking of hierarchical linguistic structures in connected speech. Nature Neuroscience, 19(1). https://doi.org/10.1038/nn.4186
   Web of Science ®Google Scholar
• Donhauser, P. W., & Baillet, S. (2020). Two distinct neural timescales for predictive speech processing. Neuron, 105(2), 385–393.e9. https://doi.org/10.1016/j.neuron.2019.10.019
   PubMed Web of Science ®Google Scholar
• Du, Y., Buchsbaum, B. R., Grady, C. L., & Alain, C. (2014). Noise differentially impacts phoneme representations in the auditory and speech motor systems. Proceedings of the National Academy of Sciences, 111(19), 7126–7131. https://doi.org/10.1073/pnas.1318738111
   PubMed Web of Science ®Google Scholar
• Dujmović, M., Bowers, J. S., Adolfi, F., & Malhotra, G. (2022). The pitfalls of measuring representational similarity using representational similarity analysis. bioRxiv. https://doi.org/10.1101/2022.04.05.487135
   Google Scholar
• Etzel, J. A., Zacks, J. M., & Braver, T. S. (2013). Searchlight analysis: Promise, pitfalls, and potential. Neuroimage, 78, 261–269. https://doi.org/10.1016/j.neuroimage.2013.03.041
   PubMed Web of Science ®Google Scholar
• Evans, S., & Davis, M. H. (2015). Hierarchical organization of auditory and motor representations in speech perception: Evidence from searchlight similarity analysis. Cerebral Cortex, 25(12), 4772–4788. https://doi.org/10.1093/cercor/bhv136
   PubMed Web of Science ®Google Scholar
• Evans, S., Kyong, J. S., Rosen, S., Golestani, N., Warren, J. E., McGettigan, C., Mourao-Miranda, J., Wise, R. J. S., & Scott, S. K. (2014). The pathways for intelligible speech: Multivariate and univariate perspectives. Cerebral Cortex, 24(9), 2350–2361. https://doi.org/10.1093/cercor/bht083
   PubMed Web of Science ®Google Scholar
• Fedorenko, E., Nieto-Castañon, A., & Kanwisher, N. (2012). Lexical and syntactic representations in the brain: An fMRI investigation with multi-voxel pattern analyses. Neuropsychologia, 50(4), 499–513. https://doi.org/10.1016/j.neuropsychologia.2011.09.014
   PubMed Web of Science ®Google Scholar
• Feuerriegel, D., Vogels, R., & Kovács, G. (2021). Evaluating the evidence for expectation suppression in the visual system. Neuroscience & Biobehavioral Reviews, 126, 368–381. https://doi.org/10.1016/j.neubiorev.2021.04.002
   PubMed Web of Science ®Google Scholar
• Fisher, J. M., Dick, F. K., Levy, D. F., & Wilson, S. M. (2018). Neural representation of vowel formants in tonotopic auditory cortex. NeuroImage, 178, 574–582. https://doi.org/10.1016/j.neuroimage.2018.05.072
   PubMed Web of Science ®Google Scholar
• Flanagan, J. L. (2005). A difference limen for vowel formant frequency. The Journal of the Acoustical Society of America, 27(3), 613. https://doi.org/10.1121/1.1907979
   Google Scholar
• Formisano, E., De Martino, F., Bonte, M., & Goebel, R. (2008). ‘Who’ is saying ‘what’? Brain-based decoding of human voice and speech. Science, 322(5903), 970–973. https://doi.org/10.1126/science.1164318
   PubMed Web of Science ®Google Scholar
• Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J.-P., Frith, C. D., & Frackowiak, R. S. J. (1995). Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 2(4), 189–210. https://doi.org/10.1002/hbm.460020402
   Google Scholar
• Gardumi, A., Ivanov, D., Hausfeld, L., Valente, G., Formisano, E., & Uludağ, K. (2016). The effect of spatial resolution on decoding accuracy in fMRI multivariate pattern analysis. NeuroImage, 132, 32–42. https://doi.org/10.1016/j.neuroimage.2016.02.033
   PubMed Web of Science ®Google Scholar
• Giordano, B. L., McAdams, S., Zatorre, R. J., Kriegeskorte, N., & Belin, P. (2013). Abstract encoding of auditory objects in cortical activity patterns. Cerebral Cortex, 23(9), 2025–2037. https://doi.org/10.1093/cercor/bhs162
   PubMed Web of Science ®Google Scholar
• Goldstein, A., Zada, Z., Buchnik, E., Schain, M., Price, A., Aubrey, B., Nastase, S. A., Feder, A., Emanuel, D., Cohen, A., Jansen, A., Gazula, H., Choe, G., Rao, A., Kim, S. C., Casto, C., Fanda, L., Doyle, W., Friedman, D., … Hasson, U. (2021). Thinking ahead: Spontaneous prediction in context as a keystone of language in humans and machines. bioRxiv. https://doi.org/10.1101/2020.12.02.403477
   Google Scholar
• Habeck, C. G. (2010). Basics of multivariate analysis in neuroimaging data. Journal of Visualized Experiments: JoVE, 41, 1988. https://doi.org/10.3791/1988
   Google Scholar
• Hamilton, L. S., Oganian, Y., Hall, J., & Chang, E. F. (2021). Parallel and distributed encoding of speech across human auditory cortex. Cell, 184(18), 4626–4639.e13. https://doi.org/10.1016/j.cell.2021.07.019
   PubMed Web of Science ®Google Scholar
• Haxby, J. V., Connolly, A. C., & Guntupalli, J. S. (2014). Decoding neural representational spaces using multivariate pattern analysis. Annual Review of Neuroscience, 37(1), 435–456. https://doi.org/10.1146/annurev-neuro-062012-170325
   PubMed Web of Science ®Google Scholar
• Hebart, M. N., & Baker, C. I. (2018). Deconstructing multivariate decoding for the study of brain function. NeuroImage, 180, 4–18. https://doi.org/10.1016/j.neuroimage.2017.08.005
   PubMed Web of Science ®Google Scholar
• Heer, W. A., Huth, A. G., Griffiths, T. L., Gallant, J. L., & Theunissen, F. E. (2017). The hierarchical cortical organization of human speech processing. Journal of Neuroscience, 37(27), 6539–6557. https://doi.org/10.1523/JNEUROSCI.3267-16.2017
   PubMed Web of Science ®Google Scholar
• Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P., & de Lange, F. P. (2022). A hierarchy of linguistic predictions during natural language comprehension. Proceedings of the National Academy of Sciences, 119(32), e2201968119. https://doi.org/10.1073/pnas.2201968119
   PubMed Web of Science ®Google Scholar
• Hickok, G. (2014). The myth of mirror neurons: The real neuroscience of communication and cognition. W W Norton & Co. p. 292
   Google Scholar
• Hickok, G., & Poeppel, D. (2007). The cortical organization of speech processing. Nature Reviews. Neuroscience, 8(5), 393–402. https://doi.org/10.1038/nrn2113
   PubMed Web of Science ®Google Scholar
• Hoffman, P., & Tamm, A. (2020). Barking up the right tree: Univariate and multivariate fMRI analyses of homonym comprehension. NeuroImage, 219, 117050. https://doi.org/10.1016/j.neuroimage.2020.117050
   PubMed Web of Science ®Google Scholar
• Holmes, E., Folkeard, P., Johnsrude, I. S., & Scollie, S. (2018). Semantic context improves speech intelligibility and reduces listening effort for listeners with hearing impairment. International Journal of Audiology, 57(7), 483–492. https://doi.org/10.1080/14992027.2018.1432901
   PubMed Web of Science ®Google Scholar
• Hsieh, I.-H., Fillmore, P., Rong, F., Hickok, G., & Saberi, K. (2012). FM-selective Networks in human auditory cortex revealed using fMRI and multivariate pattern classification. Journal of Cognitive Neuroscience, 24(9), 1896–1907. https://doi.org/10.1162/jocn_a_00254
   PubMed Web of Science ®Google Scholar
• Huettig, F., & Mani, N. (2016). Is prediction necessary to understand language? Probably not. Language, Cognition and Neuroscience, 31(1), 19–31. https://doi.org/10.1080/23273798.2015.1072223
   Web of Science ®Google Scholar
• Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2016). Natural speech reveals the semantic maps that tile human cerebral cortex. Nature, 532(7600). https://doi.org/10.1038/nature17637
   Web of Science ®Google Scholar
• Jehee, J. F. M., Brady, D. K., & Tong, F. (2011). Attention improves encoding of task-relevant features in the human visual cortex. Journal of Neuroscience, 31(22), 8210–8219. https://doi.org/10.1523/JNEUROSCI.6153-09.2011
   PubMed Web of Science ®Google Scholar
• Joanisse, M. F., & DeSouza, D. D. (2014). Sensitivity of human auditory cortex to rapid frequency modulation revealed by multivariate representational similarity analysis. Frontiers in Neuroscience, 8, https://doi.org/10.3389/fnins.2014.00306
   Web of Science ®Google Scholar
• Jolly, E., & Chang, L. J. (2021). Multivariate spatial feature selection in fMRI. Social Cognitive and Affective Neuroscience, 16(8), 795–806. https://doi.org/10.1093/scan/nsab010
   PubMed Web of Science ®Google Scholar
• Jonas, E., & Kording, K. P. (2017). Could a neuroscientist understand a microprocessor? PLOS Computational Biology, 13(1), e1005268. https://doi.org/10.1371/journal.pcbi.1005268
   PubMed Web of Science ®Google Scholar
• Kell, A. J. E., Yamins, D. L. K., Shook, E. N., Norman-Haignere, S. V., & McDermott, J. H. (2018). A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy. Neuron, 98(3), 630–644.e16. https://doi.org/10.1016/j.neuron.2018.03.044
   PubMed Web of Science ®Google Scholar
• Kemmerer, D. (2015). Does the motor system contribute to the perception and understanding of actions? Reflections on gregory hickok’s The myth of mirror neurons: The real neuroscience of communication and cognition. Language and Cognition, 7(3), 450–475. https://doi.org/10.1017/langcog.2014.36
   Web of Science ®Google Scholar
• Kilian-Hutten, N., Valente, G., Vroomen, J., & Formisano, E. (2011). Auditory cortex encodes the perceptual interpretation of ambiguous sound. Journal of Neuroscience, 31(5), 1715–1720. https://doi.org/10.1523/Jneurosci.4572-10.2011
   PubMed Web of Science ®Google Scholar
• Kivisaari, S. L., van Vliet, M., Hultén, A., Lindh-Knuutila, T., Faisal, A., & Salmelin, R. (2019). Reconstructing meaning from bits of information. Nature Communications, 10(1). https://doi.org/10.1038/s41467-019-08848-0
   Google Scholar
• Kok, P., Bains, L. J., Mourik, T., Norris, D. G., & Lange, F. P. (2016). Selective activation of the deep layers of the human primary visual cortex by Top-down feedback. Current Biology, 26(3), 371–376. https://doi.org/10.1016/j.cub.2015.12.038
   PubMed Web of Science ®Google Scholar
• Kok, P., Jehee, J. F. M., & Lange, F. P. (2012). Less is more: Expectation sharpens representations in the primary visual cortex. Neuron, 75(2), 265–270. https://doi.org/10.1016/j.neuron.2012.04.034
   PubMed Web of Science ®Google Scholar
• Kriegeskorte, N. (2008). Representational similarity analysis – connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2. https://doi.org/10.3389/neuro.06.004.2008
   Google Scholar
• Kriegeskorte, N., & Kievit, R. (2013). Representational geometry: Integrating cognition, computation, and the brain. Trends in Cognitive Sciences, 17(8). https://doi.org/10.1016/j.tics.2013.06.007
   Web of Science ®Google Scholar
• Krumbiegel, J., Ufer, C., & Blank, H. (2022). Influence of voice properties on vowel perception depends on speaker context. The Journal of the Acoustical Society of America, 152(2), 820–834. https://doi.org/10.1121/10.0013363
   PubMed Web of Science ®Google Scholar
• Kuperberg, G. R., & Jaeger, T. F. (2016). What do we mean by prediction in language comprehension? Language, Cognition and Neuroscience, 31(1), 32–59. https://doi.org/10.1080/23273798.2015.1102299
   PubMed Web of Science ®Google Scholar
• Lee, Y. S., Peelle, J. E., Kraemer, D., Lloyd, S., & Granger, R. (2015). Multivariate sensitivity to voice during auditory categorization. Journal of Neurophysiology, 114(3), 1819–1826. https://doi.org/10.1152/jn.00407.2014
   PubMed Web of Science ®Google Scholar
• Lee, Y. S., Turkeltaub, P., Granger, R., & Raizada, R. D. S. (2012). Categorical speech processing in broca’s area: An fMRI study using multivariate pattern-based analysis. Journal of Neuroscience, 32(11), 3942–3948. https://doi.org/10.1523/Jneurosci.3814-11.2012
   PubMed Web of Science ®Google Scholar
• Levy, D. F., & Wilson, S. M. (2020). Categorical encoding of vowels in primary auditory cortex. Cerebral Cortex, 30(2), 618–627. https://doi.org/10.1093/cercor/bhz112
   PubMed Web of Science ®Google Scholar
• Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106(3), 1126–1177. https://doi.org/10.1016/j.cognition.2007.05.006
   PubMed Web of Science ®Google Scholar
• Ley, A., Vroomen, J., Hausfeld, L., Valente, G., Weerd, P. D., & Formisano, E. (2012). Learning of new sound categories shapes neural response patterns in human auditory cortex. Journal of Neuroscience, 32(38), 13273–13280. https://doi.org/10.1523/JNEUROSCI.0584-12.2012
   PubMed Web of Science ®Google Scholar
• Liberman, A. M., Cooper, F. S., Shankweiler, D. P., & Studdert-Kennedy, M. (1967). Perception of the speech code. Psychological Review, 74(6), 431–461. https://doi.org/10.1037/h0020279
   PubMed Web of Science ®Google Scholar
• Liberman, A. M., & Mattingly, I. G. (1985). The motor theory of speech perception revised. Cognition, 21(1), 1–36. https://doi.org/10.1016/0010-0277(85)90021-6
   PubMed Web of Science ®Google Scholar
• Lowe, M. X., Mohsenzadeh, Y., Lahner, B., Charest, I., Oliva, A., & Teng, S. (2020). Spatiotemporal dynamics of sound representations reveal a hierarchical progression of category selectivity. bioRxiv. https://doi.org/10.1101/2020.06.12.149120
   Google Scholar
• Ma, W. J., & Peters, B. (2020). A neural network walks into a lab: Towards using deep nets as models for human behavior. ArXiv. https://doi.org/10.48550/arXiv.2005.02181
   Google Scholar
• Mahon, B. Z., & Caramazza, A. (2010). Judging semantic similarity: An event-related fMRI study with auditory word stimuli. Neuroscience, 169(1), 279–286. https://doi.org/10.1016/j.neuroscience.2010.04.029
   PubMed Web of Science ®Google Scholar
• Markiewicz, C. J., & Bohland, J. W. (2016). Mapping the cortical representation of speech sounds in a syllable repetition task. NeuroImage, 141, 174–190. https://doi.org/10.1016/j.neuroimage.2016.07.023
   PubMed Web of Science ®Google Scholar
• McClelland, J. L., & Elman, J. L. (1986). The TRACE model of speech perception. Cognitive Psychology, 18(1), 1–86. https://doi.org/10.1016/0010-0285(86)90015-0
   PubMed Web of Science ®Google Scholar
• McGettigan, C., Evans, S., Rosen, S., Agnew, Z. K., Shah, P., & Scott, S. K. (2012). An application of univariate and multivariate approaches in fMRI to quantifying the hemispheric lateralization of acoustic and linguistic processes. Journal of Cognitive Neuroscience, 24(3), 636–652. https://doi.org/10.1162/jocn_a_00161
   PubMed Web of Science ®Google Scholar
• Miller, G. A., Newman, E. B., & Friedman, E. A. (1958). Length-frequency statistics for written English. Information and Control, 1(4), 370–389. https://doi.org/10.1016/S0019-9958(58)90229-8
   Google Scholar
• Mitchell, D. J., & Cusack, R. (2016). Semantic and emotional content of imagined representations in human occipitotemporal cortex. Scientific Reports, 6(1), 20232. https://doi.org/10.1038/srep20232
   PubMedGoogle Scholar
• Mitchell, T. M., Hutchinson, R., Niculescu, R. S., Pereira, F., Wang, X., Just, M., & Newman, S. (2004). Learning to decode cognitive states from brain images. Machine Learning, 57(1), 145–175. https://doi.org/10.1023/B:MACH.0000035475.85309.1b
   Web of Science ®Google Scholar
• Muckli, L., De Martino, F., Vizioli, L., Petro, L. S., Smith, F. W., Ugurbil, K., Goebel, R., & Yacoub, E. (2015). Contextual feedback to superficial layers of V1. Current Biology, 25(20), 2690–2695. https://doi.org/10.1016/j.cub.2015.08.057
   PubMed Web of Science ®Google Scholar
• Murray, S. O., Schrater, P., & Kersten, D. (2004). Perceptual grouping and the interactions between visual cortical areas. Neural Networks, 17(5–6), 695–705. https://doi.org/10.1016/j.neunet.2004.03.010
   PubMed Web of Science ®Google Scholar
• Nearey, T. M. (1998). Locus equations and pattern recognition. Behavioral and Brain Sciences, 21(2), 277–277. https://doi.org/10.1017/S0140525X98421174
   Web of Science ®Google Scholar
• Norman-Haignere, S. V., Long, L. K., Devinsky, O., Doyle, W., Irobunda, I., Merricks, E. M., Feldstein, N. A., McKhann, G. M., Schevon, C. A., Flinker, A., & Mesgarani, N. (2022). Multiscale temporal integration organizes hierarchical computation in human auditory cortex. Nature Human Behaviour, 6(3). https://doi.org/10.1038/s41562-021-01261-y
   Web of Science ®Google Scholar
• Norris, D., McQueen, J. M., & Cutler, A. (2000). Merging information in speech recognition: Feedback is never necessary. Behavioral and Brain Sciences, 23(3), 299. https://doi.org/10.1017/S0140525×00003241
   PubMed Web of Science ®Google Scholar
• Obleser, J., Leaver, A., VanMeter, J., & Rauschecker, J. (2010). Segregation of vowels and consonants in human auditory cortex: Evidence for distributed hierarchical organization. Frontiers in Psychology, 1. https://doi.org/10.3389/fpsyg.2010.00232
   Web of Science ®Google Scholar
• Ogg, M., Moraczewski, D., Kuchinsky, S. E., & Slevc, L. R. (2019). Separable neural representations of sound sources: Speaker identity and musical timbre. NeuroImage, 191, 116–126. https://doi.org/10.1016/j.neuroimage.2019.01.075
   PubMed Web of Science ®Google Scholar
• Okada, K., Rong, F., Venezia, J., Matchin, W., Hsieh, I.-H., Saberi, K., Serences, J. T., & Hickok, G. (2010). Hierarchical organization of human auditory cortex: Evidence from acoustic invariance in the response to intelligible speech. Cerebral Cortex, 20(10), 2486–2495. https://doi.org/10.1093/cercor/bhp318
   PubMed Web of Science ®Google Scholar
• Pakravan, M., Abbaszadeh, M., & Ghazizadeh, A. (2022). Coordinated multivoxel coding beyond univariate effects is not likely to be observable in fMRI data. NeuroImage, 247, 118825. https://doi.org/10.1016/j.neuroimage.2021.118825
   PubMed Web of Science ®Google Scholar
• Partyka, M., Demarchi, G., Roesch, S., Suess, N., Sedley, W., Schlee, W., & Weisz, N. (2019). Phantom auditory perception (tinnitus) is characterised by stronger anticipatory auditory predictions. bioRxiv. https://doi.org/10.1101/869842
   Google Scholar
• Partyka, M., Demarchi, G., Sedley, W., Schlee, W., & Weisz, N. (2019). Enhanced anticipatory predictions in the auditory system of individuals with tinnitus. Poster session presented at 49th Annual Meeting of the Society for Neuroscience (SfN), Chicago, Illinois, United States.
   Google Scholar
• Pasley, B. N., David, S. V., Mesgarani, N., Flinker, A., Shamma, S. A., Crone, N. E., Knight, R. T., & Chang, E. F. (2012). Reconstructing speech from human auditory cortex. PLOS Biology, 10(1), e1001251. https://doi.org/10.1371/journal.pbio.1001251
   PubMed Web of Science ®Google Scholar
• Pereira, F., Just, M., & Mitchell, T. (2001). Distinguishing natural language processes on the basis of fMRI-measured brain activation. In L. De Raedt, & A. Siebes (Eds.), Principles of data mining and knowledge discovery (pp. 374–385). Springer. https://doi.org/10.1007/3-540-44794-6_31.
   Google Scholar
• Pereira, F., Lou, B., Pritchett, B., Ritter, S., Gershman, S. J., Kanwisher, N., Botvinick, M., & Fedorenko, E. (2018). Toward a universal decoder of linguistic meaning from brain activation. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-03068-4
   Google Scholar
• Perrodin, C., Kayser, C., Logothetis, N. K., & Petkov, C. I. (2011). Voice cells in the primate temporal lobe. Current Biology, 21(16), 1408–1415. https://doi.org/10.1016/j.cub.2011.07.028
   PubMed Web of Science ®Google Scholar
• Poldrack, R. A. (2007). Region of interest analysis for fMRI. Social Cognitive and Affective Neuroscience, 2(1), 67–70. https://doi.org/10.1093/Scan/Nsm006
   PubMed Web of Science ®Google Scholar
• Preisig, B., Riecke, L., & Hervais-Adelman, A. (2022). Speech sound categorization: The contribution of non-auditory and auditory cortical regions. NeuroImage, 258, 119375. https://doi.org/10.1016/j.neuroimage.2022.119375
   PubMed Web of Science ®Google Scholar
• Raizada, R. D. S., & Poldrack, R. A. (2007). Selective amplification of stimulus differences during categorical processing of speech. Neuron, 56(4), 726–740. https://doi.org/10.1016/j.neuron.2007.11.001
   PubMed Web of Science ®Google Scholar
• Rampinini, A. C., Handjaras, G., Leo, A., Cecchetti, L., Ricciardi, E., Marotta, G., & Pietrini, P. (2017). Functional and spatial segregation within the inferior frontal and superior temporal cortices during listening, articulation imagery, and production of vowels. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-17314-0
   Google Scholar
• Remez, R. E., Rubin, P. E., Pisoni, D. B., & Carrell, T. D. (1981). Speech perception without traditional speech cues. Science, 212(4497), 947–950. https://doi.org/10.1126/science.7233191
   PubMed Web of Science ®Google Scholar
• Ritchie, J. B., Kaplan, D. M., & Klein, C. (2019). Decoding the brain: Neural representation and the limits of multivariate pattern analysis in cognitive neuroscience. The British Journal for the Philosophy of Science, 70(2), 581–607. https://doi.org/10.1093/bjps/axx023
   PubMed Web of Science ®Google Scholar
• Saddler, M. R., Gonzalez, R., & McDermott, J. H. (2021). Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-27366-6
   Web of Science ®Google Scholar
• Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Müller, K.-R. (2021). Explaining deep neural networks and beyond: A review of methods and applications. Proceedings of the IEEE, 109(3), 247–278. https://doi.org/10.1109/JPROC.2021.3060483
   Web of Science ®Google Scholar
• Saxe, A., Nelli, S., & Summerfield, C. (2021). If deep learning is the answer, what is the question? Nature Reviews Neuroscience, 22(1). https://doi.org/10.1038/s41583-020-00395-8
   Web of Science ®Google Scholar
• Schmitt, L.-M., Erb, J., Tune, S., Rysop, A. U., Hartwigsen, G., & Obleser, J. (2021). Predicting speech from a cortical hierarchy of event-based time scales. Science Advances, 7(49), eabi6070. https://doi.org/10.1126/sciadv.abi6070
   PubMed Web of Science ®Google Scholar
• Schomers, M. R., & Pulvermüller, F. (2016). Is the sensorimotor cortex relevant for speech perception and understanding? An integrative review. Frontiers in Human Neuroscience, 10. https://doi.org/10.3389/fnhum.2016.00435
   Web of Science ®Google Scholar
• Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2021). The neural architecture of language: Integrative modeling converges on predictive processing. Proceedings of the National Academy of Sciences, 118(45), e2105646118. https://doi.org/10.1073/pnas.2105646118
   PubMed Web of Science ®Google Scholar
• Schwiedrzik, C. M., & Freiwald, W. A. (2017). High-level prediction signals in a Low-level area of the macaque face-processing hierarchy. Neuron, 96(1), 89–97.e4. https://doi.org/10.1016/j.neuron.2017.09.007
   PubMed Web of Science ®Google Scholar
• Schyns, P. G., Snoek, L., & Daube, C. (2022). Degrees of algorithmic equivalence between the brain and its DNN models. Trends in Cognitive Sciences, 26(12). https://doi.org/10.1016/j.tics.2022.09.003
   Web of Science ®Google Scholar
• Scott, S. K., Blank, C. C., Rosen, S., & Wise, R. J. (2000). Identification of a pathway for intelligible speech in the left temporal lobe. Brain: A Journal of Neurology, 123(12), 2400–2406. https://doi.org/10.1093/brain/123.12.2400
   PubMed Web of Science ®Google Scholar
• Scott, S. K., & Johnsrude, I. S. (2003). The neuroanatomical and functional organization of speech perception. Trends in Neurosciences, 26(2), 100–107. https://doi.org/10.1016/S0166-2236(02)00037-1
   PubMed Web of Science ®Google Scholar
• Shahbazi, M., Shirali, A., Aghajan, H., & Nili, H. (2021). Using distance on the riemannian manifold to compare representations in brain and in models. NeuroImage, 239, 118271. https://doi.org/10.1016/j.neuroimage.2021.118271
   PubMed Web of Science ®Google Scholar
• Sohoglu, E., & Davis, M. H. (2020). Rapid computations of spectrotemporal prediction error support perception of degraded speech. ELife, 9, e58077. https://doi.org/10.7554/eLife.58077
   PubMed Web of Science ®Google Scholar
• Sohoglu, E., Peelle, J. E., Carlyon, R. P., & Davis, M. H. (2012). Predictive Top-down integration of prior knowledge during speech perception. Journal of Neuroscience, 32(25), 8443–8453. https://doi.org/10.1523/JNEUROSCI.5069-11.2012
   PubMed Web of Science ®Google Scholar
• Stilp, C. (2020). Acoustic context effects in speech perception. WIRES Cognitive Science, 11(1), e1517. https://doi.org/10.1002/wcs.1517
   Web of Science ®Google Scholar
• Su, Y., Olasagasti, I., & Giraud, A.-L. (2022). A deep hierarchy of predictions enables assignment of semantic roles in real-time speech comprehension. bioRxiv. https://doi.org/10.1101/2022.04.01.486694
   Google Scholar
• Thomas, A. W., Ré, C., & Poldrack, R. A. (2022). Interpreting mental state decoding with deep learning models. Trends in Cognitive Sciences, 26(11), 972–986. https://doi.org/10.1016/j.tics.2022.07.003
   PubMed Web of Science ®Google Scholar
• Todorovic, A., & Lange, F. P. d. (2012). Repetition suppression and expectation suppression Are dissociable in time in early auditory evoked fields. Journal of Neuroscience, 32(39), 13389–13395. https://doi.org/10.1523/JNEUROSCI.2227-12.2012
   PubMed Web of Science ®Google Scholar
• Wang, L., Hu, X., Liu, H., Huang, H., Guo, L., & Liu, T. (2019). Explore the hierarchical auditory information processing Via deep convolutional autoencoder. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 1788–1791. https://doi.org/10.1109/ISBI.2019.8759330
   Google Scholar
• Wang, L., Liu, H., Zhang, X., Zhao, S., Guo, L., Han, J., & Hu, X. (2022). Exploring hierarchical auditory representation via a neural encoding model. Frontiers in Neuroscience, 16. https://doi.org/10.3389/fnins.2022.843988
   Web of Science ®Google Scholar
• Williams, M. A., Baker, C. I., Op de Beeck, H. P., Mok Shim, W., Dang, S., Triantafyllou, C., & Kanwisher, N. (2008). Feedback of visual object information to foveal retinotopic cortex. Nature Neuroscience, 11(12). https://doi.org/10.1038/nn.2218
   Web of Science ®Google Scholar
• Yan, S., Kuperberg, G. R., & Jaeger, T. F. (2017). Prediction (or not) during language processing. A commentary on Nieuwland et al. (2017) and DeLong et al. (2005). bioRxiv. https://doi.org/10.1101/143750
   Google Scholar
• Yang, Z., Fang, F., & Weng, X. (2012). Recent developments in multivariate pattern analysis for functional MRI. Neuroscience Bulletin, 28, 399–408. https://doi.org/10.1007/s12264-012-1253-3
   PubMed Web of Science ®Google Scholar
• Yeo, B. T. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/jn.00338.2011
   PubMed Web of Science ®Google Scholar
• Yoshimura, N., Nishimoto, A., Belkacem, A. N., Shin, D., Kambara, H., Hanakawa, T., & Koike, Y. (2016). Decoding of covert vowel articulation using electroencephalography cortical currents. Frontiers in Neuroscience, 10. https://doi.org/10.3389/fnins.2016.00175
   Web of Science ®Google Scholar
• Yu, Y., Huber, L., Yang, J., Jangraw, D. C., Handwerker, D. A., Molfese, P. J., Chen, G., Ejima, Y., Wu, J., & Bandettini, P. A. (2019). Layer-specific activation of sensory input and predictive feedback in the human primary somatosensory cortex. Science Advances, 5(5). https://doi.org/10.1126/sciadv.aav9053
   Web of Science ®Google Scholar
• Zhang, Q., Hu, X., Luo, H., Li, J., Zhang, X., & Zhang, B. (2016). Deciphering phonemes from syllables in blood oxygenation level-dependent signals in human superior temporal gyrus. European Journal of Neuroscience, 43(6), 773–781. https://doi.org/10.1111/ejn.13164
   PubMed Web of Science ®Google Scholar