Predictive Processing during a Naturalistic Statistical Learning Task in ASD
Open accessAbstract
Children’s sensitivity to regularities within the linguistic stream, such as the likelihood that syllables co-occur, is foundational to speech segmentation and language acquisition. Yet, little is known about the neurocognitive mechanisms underlying speech segmentation in typical development and in neurodevelopmental disorders that impact language acquisition such as autism spectrum disorder (ASD). Here, we investigate the neural signals of statistical learning in 15 human participants (children ages 8–12) with a clinical diagnosis of ASD and 14 age-matched and gender-matched typically developing peers. We tracked the evoked neural responses to syllable sequences in a naturalistic statistical learning corpus using magnetoencephalography (MEG) in the left primary auditory cortex, posterior superior temporal gyrus (pSTG), and inferior frontal gyrus (IFG), across three repetitions of the passage. In typically developing children, we observed a neural index of learning in all three regions of interest (ROIs), measured by the change in evoked response amplitude as a function of syllable surprisal across passage repetitions. As surprisal increased, the amplitude of the neural response increased; this sensitivity emerged after repeated exposure to the corpus. Children with ASD did not show this pattern of learning in all three regions. We discuss two possible hypotheses related to children’s sensitivity to bottom-up sensory deficits and difficulty with top-down incremental processing.
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ASD children with poorer language composite scores present a general auditory processing deficit, with atypical left hemisphere responses in the high order time-window of 200-400 ms, and different neural and behavioral effects of syllable-to-syllable processing in speech segmentation.
“When speech and non-speech were compared, ASD children with poorer language composite scores presented a general auditory processing deficit, with atypical left hemisphere responses in the high order time-window of 200–400 ms (Yau et al. 2016), and different neural and behavioral effects of syllable-to-syllable processing in speech segmentation (Wagley et al. 2020).”
From Autism Spectrum Disorder and auditory sensory alterations: a systematic review on the integrity of cognitive and neuronal functions related to auditory processing (2023) · cites this paper as Wagley et al. 2020
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Experimental — accuracy not yet measuredASD children with lower IQ levels did not display the surprisal-related learning pattern.
“However, ASD children with lower IQ levels did not display such learning pattern (Wagley et al. 2020).”
From Autism Spectrum Disorder and auditory sensory alterations: a systematic review on the integrity of cognitive and neuronal functions related to auditory processing (2023) · cites this paper as Wagley et al. 2020
ASD children do not seem to have differences in phonological competence but significantly differ in other measures, such as attention, syntax, and pragmatics.
“Interestingly, ASD children do not seem to have differences in phonological competence but significantly differ in other measures, such as attention, syntax, and pragmatics (Brennan et al. 2016; Wagley et al. 2020), suggesting impairments at language structure knowledge.”
From Autism Spectrum Disorder and auditory sensory alterations: a systematic review on the integrity of cognitive and neuronal functions related to auditory processing (2023) · cites this paper as Wagley et al. 2020
As surprisal values increase, the amplitude of the neural response also increases in the left primary auditory cortex, posterior superior temporal gyrus (pSTG), and inferior frontal gyrus (IFG) in neurotypical children.
“A different study showed that as surprisal values increase, the amplitude of the neural response also increases in the left primary auditory cortex, posterior superior temporal gyrus (pSTG), and inferior frontal gyrus (IFG) in neurotypical children (Wagley et al. 2020).”
From Autism Spectrum Disorder and auditory sensory alterations: a systematic review on the integrity of cognitive and neuronal functions related to auditory processing (2023) · cites this paper as Wagley et al. 2020
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Cites
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323 · see citing works
Linear Mixed-Effects Models using 'Eigen' and S4
Bates, D. M., Maechler, M., Bolker, B. M., & Walker, S. C. (2015) · Cited by 5,924 · see citing works
The cortical organization of speech processing
Hickok, G., & Poeppel, D. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2113 · Cited by 5,495 · see citing works
Näätänen, R., & Picton, T. (1987) · Psychophysiology · doi:10.1111/j.1469-8986.1987.tb00311.x · Cited by 3,321 · see citing works
Advanced Bayesian Multilevel Modeling with the R Package brms
Bürkner, P.-C. (2018) · The R Journal · doi:10.32614/rj-2018-017 · Cited by 3,015 · see citing works
(1989) · UCL Discovery (University College London) · Cited by 2,724 · see citing works
Van Veen, B. D., Van Drongelen, W., Yuchtman, M., & Suzuki, A. (1997) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.623056 · Cited by 2,561 · see citing works
lme4: Linear Mixed-Effects Models using 'Eigen' and S4
Bates, D. M., Maechler, M., Bolker, B. M., Walker, S. C., Ly, A., & Jagan, M. (2003) · doi:10.32614/cran.package.lme4 · Cited by 2,389 · see citing works
Early language acquisition: cracking the speech code
Kuhl, P. K. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1533 · Cited by 2,291 · see citing works
Unbiased average age-appropriate atlases for pediatric studies
Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., & Collins, D. L. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.033 · Cited by 2,290 · see citing works
Expectation-based syntactic comprehension
Lévy, R. (2007) · Cognition · doi:10.1016/j.cognition.2007.05.006 · Cited by 2,220 · see citing works
Cortical oscillations and speech processing: emerging computational principles and operations
Giraud, A., & Poeppel, D. (2012) · Nature Neuroscience · doi:10.1038/nn.3063 · Cited by 1,924 · see citing works
Poeppel, D. (2003) · Speech Communication · doi:10.1016/s0167-6393(02)00107-3 · Cited by 1,315 · see citing works
The Autism Diagnostic Observation Schedule: Revised Algorithms for Improved Diagnostic Validity
Gotham, K., Risi, S., Pickles, A., & Lord, C. (2007) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-006-0280-1 · Cited by 1,161 · see citing works
Wechsler Abbreviated Scale of Intelligence--Second Edition
Wechsler, D. (2018) · PsycTESTS Dataset · doi:10.1037/t15171-000 · Cited by 1,153 · see citing works
A probabilistic earley parser as a psycholinguistic model
Hale, J. (2001) · Second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies 2001 - NAACL '01 · doi:10.3115/1073336.1073357 · Cited by 1,150 · see citing works
Language and Communication in Autism
Tager-Flusberg, H. B., Paul, R., & Lord, C. (2005) · doi:10.1002/9780470939345.ch12 · Cited by 1,008 · see citing works
Courchesne, E., & Pierce, K. (2005) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2005.03.001 · Cited by 968 · see citing works
Word Segmentation by 8-Month-Olds: When Speech Cues Count More Than Statistics
Johnson, E. K., & Jusczyk, P. (2001) · Journal of Memory and Language · doi:10.1006/jmla.2000.2755 · Cited by 691 · see citing works
Comprehensive Test of Phonological Processing (CTOPP)
Bruno, R. M., & Walker, S. C. (1999) · Diagnostique · doi:10.1177/153450849902401-408 · Cited by 576 · see citing works
Autism as a disorder of prediction
Sinha, P., Kjelgaard, M. M., Gandhi, T. K., Tsourides, K., Cardinaux, A. L., Pantazis, D., Diamond, S. P., & Held, R. M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1416797111 · Cited by 563 · cited 1× · see citing works
“Prediction has been implicated as an important component of early learning (Romberg and Saffran, 2013), and some suggest prediction plays a major role in the underlying impairments observed in ASD (Sinha et al., 2014).”
BackgroundBetween a ROC and a hard place: decision making and making decisions about using the SCQ
Corsello, C. M., Bal, V. H., Pickles, A. R., Risi, S., Cook, E. H., LEVENTHAL, B. L., & Lord, C. (2007) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2007.01762.x · Cited by 355 · see citing works
Statistical language learning in neonates revealed by event-related brain potentials
Teinonen, T., Fellman, V., Näätänen, R., Alku, P., & Huotilainen, M. (2009) · BMC Neuroscience · doi:10.1186/1471-2202-10-21 · Cited by 319 · see citing works
Roberts, T. P. L., Khan, S. Y., Rey, M., Monroe, J. F., Cannon, K. M., Blaskey, L. G., Woldoff, S., Qasmieh, S., Gandal, M., Schmidt, G. L., et al. (2010) · Autism Research · doi:10.1002/aur.111 · Cited by 298 · see citing works
Prediction during natural language comprehension
Willems, R. M., Frank, S. L., Nijhof, A. D., Hagoort, P., & van den Bosch, A. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv075 · Cited by 288 · see citing works
Redcay, E., & Courchesne, E. (2008) · Biological Psychiatry · doi:10.1016/j.biopsych.2008.05.020 · Cited by 245 · see citing works
Linking sounds to meanings: Infant statistical learning in a natural language
Hay, J. F., Pelucchi, B., Estes, K. G., & Saffran, J. R. (2011) · Cognitive Psychology · doi:10.1016/j.cogpsych.2011.06.002 · Cited by 225 · see citing works
Self-citationSegmenting nonsense: an event-related potential index of perceived onsets in continuous speech
Sanders, L. D., Newport, E., & Neville, H. (2002) · Nature Neuroscience · doi:10.1038/nn873 · Cited by 216 · see citing works
The neural correlates of statistical learning in a word segmentation task: An fMRI study
Karuza, E. A., Newport, E., Aslin, R., Starling, S., Tivarus, M., & Bavelier, D. (2013) · Brain and Language · doi:10.1016/j.bandl.2012.11.007 · Cited by 213 · see citing works
Different Functional Neural Substrates for Good and Poor Language Outcome in Autism
Lombardo, M. V., Pierce, K., Eyler, L. T., Barnes, C. C., Ahrens‐Barbeau, C., Solso, S., Campbell, K. M., & Courchesne, E. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.023 · Cited by 209 · see citing works
Rogers, A., Boyd-Graber, J., & Okazaki, N. (2023) · doi:10.18653/v1/2023.acl-short · Cited by 208 · see citing works
Abstract linguistic structure correlates with temporal activity during naturalistic comprehension
Brennan, J., Stabler, E. P., Wagenen, S. E. V., Luh, W., & Hale, J. (2016) · Brain and Language · doi:10.1016/j.bandl.2016.04.008 · Cited by 201 · see citing works
Self-citationCracking the Language Code: Neural Mechanisms Underlying Speech Parsing
Mcnealy, K., Mazziotta, J., & Dapretto, M. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.5501-05.2006 · Cited by 198 · see citing works
Perceptual beginnings to language acquisition
Werker, J. F. (2018) · Applied Psycholinguistics · doi:10.1017/s0142716418000152 · Cited by 190 · see citing works
Information‐theoretical Complexity Metrics
Hale, J. (2016) · Language and Linguistics Compass · doi:10.1111/lnc3.12196 · Cited by 186 · see citing works
Event Related Potentials in the Understanding of Autism Spectrum Disorders: An Analytical Review
Jeste, S. S., & Nelson, C. A. (2009) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-008-0652-9 · Cited by 179 · see citing works
Auditory Magnetic Mismatch Field Latency: A Biomarker for Language Impairment in Autism
Roberts, T. P. L., Cannon, K. M., Tavabi, K., Blaskey, L. G., Khan, S. Y., Monroe, J. F., Qasmieh, S., Levy, S. E., & Edgar, J. C. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.01.015 · Cited by 171 · see citing works
In Spoken Word Recognition, the Future Predicts the Past
Gwilliams, L., Linzen, T., Poeppel, D., & Marantz, A. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0065-18.2018 · Cited by 164 · see citing works
Whitehouse, A. J. O., & Bishop, D. V. M. (2008) · Developmental Science · doi:10.1111/j.1467-7687.2008.00697.x · Cited by 163 · see citing works
Edgar, J. C., Khan, S. Y., Blaskey, L., Chow, V. Y., Rey, M., Gaetz, W., Cannon, K. M., Monroe, J. F., Cornew, L., Qasmieh, S., et al. (2015) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-013-1904-x · Cited by 153 · see citing works
Statistical Learning in Specific Language Impairment and Autism Spectrum Disorder: A Meta-Analysis
Obeid, R., Brooks, P. J., Powers, K. L., Gillespie‐Lynch, K., & Lum, J. A. G. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01245 · Cited by 148 · see citing works
Cortical auditory evoked potentials in autism: a review
Bomba, M. D., & Pang, E. W. (2004) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2004.04.001 · Cited by 126 · see citing works
Statistical word learning in children with autism spectrum disorder and specific language impairment
Haebig, E., Saffran, J. R., & Weismer, S. E. (2017) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12734 · Cited by 126 · see citing works
Cunillera, T., Toro, J. M., Sebastián-Gallés, N., & Rodríguez-Fornells, A. (2006) · Brain Research · doi:10.1016/j.brainres.2006.09.046 · Cited by 110 · see citing works
Functional neuroimaging of speech perception during a pivotal period in language acquisition
Redcay, E., Haist, F., & Courchesne, E. (2008) · Developmental Science · doi:10.1111/j.1467-7687.2008.00674.x · Cited by 102 · see citing works
Zeeland, A. A. S., McNealy, K., Wang, A. T., Sigman, M., Bookheimer, S. Y., & Dapretto, M. (2010) · Biological Psychiatry · doi:10.1016/j.biopsych.2010.01.011 · Cited by 92 · see citing works
Lexical surprisal as a general predictor of reading time
Monsalve, I. F., Frank, S. L., & Vigliocco, G. (2012) · UCL Discovery (University College London) · Cited by 87 · see citing works
Lopopolo, A., Frank, S. L., van den Bosch, A. P. J., & Willems, R. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177794 · Cited by 78 · see citing works
The Neural Basis of Speech Parsing in Children and Adults
Mcnealy, K., Mazziotta, J., & Dapretto, M. (2010) · Developmental Science · doi:10.1111/j.1467-7687.2009.00895.x · Cited by 68 · see citing works
Berman, J. I., Edgar, J. C., Blaskey, L., Kuschner, E. S., Levy, S. E., Ku, M., Dell, J., & Roberts, T. P. L. (2016) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2016.00030 · Cited by 68 · see citing works
Mayo, J., & Eigsti, I. (2012) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-012-1493-0 · Cited by 66 · see citing works
Missing and delayed auditory responses in young and older children with autism spectrum disorders
Edgar, J. C., Lanza, M. R., Daina, A. B., Monroe, J. F., Khan, S. Y., Blaskey, L., Cannon, K. M., Jenkins, J., Qasmieh, S., Levy, S. E., et al. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00417 · Cited by 61 · see citing works
Stroganova, T. A., Kozunov, V. V., Posikera, I. N., Galuta, I. A., Gratchev, V. V., & Orekhova, E. V. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069100 · Cited by 60 · see citing works
Gwilliams, L., & Marantz, A. (2015) · Brain and Language · doi:10.1016/j.bandl.2015.04.006 · Cited by 56 · see citing works
Word surprisal predicts N400 amplitude during reading
Frank, S. L., Otten, L. J., Galli, G., & Vigliocco, G. (2013) · Research Repository (Kingston University London) · Cited by 48 · see citing works
Expectancy Learning from Probabilistic Input by Infants
Romberg, A. R., & Saffran, J. R. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00610 · Cited by 31 · see citing works
Lajiness-O'Neill, R. R., Brennan, J., Moran, J. E., Richard, A. E., Flores, A., Swick, C., Goodcase, R., Andersen, T., McFarlane, K., Rusiniak, K. W., et al. (2017) · Autism Research · doi:10.1002/aur.1908 · Cited by 29 · see citing works
Self-citationPredictive sentence comprehension during story-listening in autism spectrum disorder
Brennan, J. R., Lajiness-O'Neill, R., Bowyer, S., Kovelman, I., & Hale, J. T. (2019) · Language, Cognition and Neuroscience · doi:10.1080/23273798.2018.1560483 · Cited by 19 · cited 1× · see citing works
“November/December 2020, 7(6) ENEURO.0069-19.2020 eNeuro.org ASD using MEG (Brennan et al., 2016b; Lajiness-O’Neill et al., 2018; Brennan et al., 2019).”
InfluentialSelf-citationGalilee, A., Stefanidou, C., & McCleery, J. P. (2017) · PLOS ONE · doi:10.1371/journal.pone.0181354 · Cited by 16 · see citing works
Brennan, J. R., Wagley, N., Kovelman, I., Bowyer, S. M., Richard, A. E., & Lajiness-O’Neill, R. (2016) · NeuroReport · doi:10.1097/wnr.0000000000000643 · Cited by 9 · see citing works
Self-citation
Flagged first, then by citations
1 of the 62 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Reynolds CR, Kamphaus RW (2002) The clinician's guide to the behavior assessment system for children (BASC). New York: Guilford Press.
- Semel E, Wiig E, Secord W (2006) CELF 4. Clinical evaluation of language fundamentals, Ed 4 London: Pearson Assessment.
- Wechsler D, Hsiao-pin C (2011) WASI-II: Wechsler abbreviated scale of intelligence. London: Pearson.
Cited by
Sorted by citations
Gonçalves, A. M., & Monteiro, P. (2023) · Journal of Neural Transmission · doi:10.1007/s00702-023-02595-9 · Cited by 88 · see citing works
Cerebellar Contributions to Social Cognition in ASD: A Predictive Processing Framework
Frosch, I. R., Mittal, V. A., & D’Mello, A. M. (2022) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2022.810425 · Cited by 27 · see citing works
Wan, L., Li, Y., Zhu, G., Yang, D., Li, F., Wang, W., Chen, J., Yang, G., & Li, R. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120895 · Cited by 19 · see citing works
Probabilistic Learning of Cue-Outcome Associations is not Influenced by Autistic Traits
Ong, J. H., & Liu, F. (2022) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-022-05690-0 · Cited by 6 · see citing works
A systematic review of statistical learning in autism spectrum disorder
Bell, R. R., Thomas, H., Saffran, J. R., & Eigsti, I. (2025) · Molecular Autism · doi:10.1186/s13229-025-00697-7 · Cited by 6 · see citing works
Ong, J. H., Zhang, L., & Liu, F. (2025) · Molecular Autism · doi:10.1186/s13229-025-00651-7 · Cited by 3 · see citing works
Non-social Language Exposure Facilitates Autistic Children’s Language Learning
Hwang, H., Louie, K., & To, C. K. S. (2026) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-026-07286-4 · Cited by 2 · see citing works
Qi, J., Peng, J., & Kang, X. (2025) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-024-06704-9 · Cited by 3 · see citing works
Batterink, L. J., Liu, Y., Westerkamp, G. C., Citarella, J., Siekierski, P., Voorhees, L., Ethridge, L. E., Smith, E. G., Elmaghraby, R., Erickson, C. A., et al. (2026) · Autism Research · doi:10.1002/aur.70312 · Cited by 2 · see citing works
Godel, M., Fló, A., Benjamin, L., Dehaene‐Lambertz, G., & Schaer, M. (2025) · eLife · doi:10.7554/elife.109901
Godel, M., Fló, A., Benjamin, L., Dehaene‐Lambertz, G., & Schaer, M. (2025) · eLife · doi:10.7554/elife.109901.1
Neurobiology of language in autism: a systematic review of pediatric studies
Witt, K. M., & Carnett, A. (2026) · Brain Imaging and Behavior · doi:10.1007/s11682-026-01140-y
Effects of Cannabidiol on Auditory Mismatch Negativity in Autistic ChildrenPreprint
Budhiraja, K., Parrella, N.-F., Hill, A. T., Downey, L. A., Catchlove, S. J., Enticott, P. G., & Ford, T. C. (2026) · medRxiv · doi:10.64898/2026.10.14.26365371
- Authors
- Wagley, N., Lajiness-O’Neill, R., Hay, J. S. F., Ugolini, M., Bowyer, S. M., Kovelman, I., & Brennan, J. R.
- Affiliations
- Vanderbilt University (US)
- University of Michigan (US)
- Henry Ford Hospital (US)
- Corresponding author
- Wagley, Neelima
- Brennan, Jonathan R.
- ORCID
- Venue
- eneuro
- Publisher
- Society for Neuroscience
- Published
- 2020-11
- Volume
- 7
- Issue
- 6
- Pages
- ENEURO.0069-19.2020
- ISSN
- 2373-2822 (ISSN-L)
- PMC
- PMC7729300
- OpenAlex
- W3104714766
- Cited by
- 13
- References
- 62
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 1 · 2023: 1 · 2022: 2
- FWCI
- 1.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- eNeuro (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Funders
- University of Michigan
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Keywords
- autism spectrum disorder, statistical learning, speech segmentation, predictive processing, primary auditory cortex, posterior superior temporal gyrus, inferior frontal gyrus, children, language acquisition, top-down processing
- MeSH terms
- Autism Spectrum Disorder, Auditory Cortex, Child, Humans, Learning, Magnetoencephalography
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