A predictive coding perspective on autism spectrum disorders
Open accessAbstract
GENERAL COMMENTARY article Front. Psychol., 28 January 2013Sec. Perception Science Volume 4 - 2013 | https://doi.org/10.3389/fpsyg.2013.00019
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Predictive coding models of autism spectrum disorder postulate that individuals with autism spectrum disorder present impairments in high-level sensory perception (hypo-priors) rather than low-level sensory perception.
“The latter finding is consistent with the predictive coding models of ASD (Pellicano and Burr, 2012; Van Boxtel and Lu, 2013), which postulate that individuals with ASD present impairments in high-level sensory perception (i.e., hypo-priors) rather than low-level sensory perception (Kern et al., 2007; Tavassoli et al., 2014).”
From Predictive coding in neuropsychiatric disorders: a systematic transdiagnostic review. (2025) · cites this paper as Van Boxtel and Lu, 2013
The paper’s discussion of exogenous attention links ASD to decreased high-level processing and less precise predictions, or “hypo-priors.” But its discussion of the predictive coding framework also says perceptual differences might arise from an attentional bias toward lower-level stimulus characteristics, rather than necessarily from malfunctioning higher-level areas. It therefore supports the hypo-prior account, not the categorical “rather than low-level” contrast. (LLM, conf=0.87)
Experimental — accuracy not yet measuredIn the elaboration of the Bayesian brain hypothesis of ASD and subsequent theoretical papers, results from numerous earlier cognitive and psychophysical studies were re-interpreted post hoc in the novel framework.
“In the elaboration of the Bayesian brain hypothesis of ASD (2) and subsequent theoretical papers (3, 4, 97–99), results from numerous earlier cognitive and psychophysical studies were re-interpreted post hoc in the novel framework.”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [98]
A rapidly gaining theory of autism spectrum disorder is that individuals with autism spectrum disorder lack a robust historical representation of the world, making it difficult to predict upcoming events.
“A theory of ASD that is rapidly gaining credibility is the notion that individuals with ASD do not have a robust historical representation of the world. This lack of a probabilistic “map” of the world makes it difficult for those with ASD to predict upcoming events, and this lack of predictive coding thus limits interactions with the external environment (Pellicano and Burr, 2012; Sinha et al., 2”
From Behavioral, Perceptual, and Neural Alterations in Sensory and Multisensory Function in Autism Spectrum Disorder (2015) · cites this paper as van Boxtel and Lu, 2013
Predictive coding, hypo-priors, Weak Central Coherence, and Enhanced Perceptual Functioning accounts characterize perception in ASD as under-utilizing top-down processes or over-functioning of bottom-up sensory processes.
“Predictive coding, hypo-priors, Weak Central Coherence, and Enhanced Perceptual Functioning accounts characterize perception in ASD as under-utilizing top-down processes (context, prior knowledge, or global coherence) or over-functioning of bottom-up sensory processes (Pellicano and Burr, 2012, Van Boxtel and Lu, 2013, Van de Cruys et al., 2012, Lawson et al., 2014, Frith, 1989, Happe and Frith, 2”
From The impact of atypical sensory processing on social impairments in autism spectrum disorder (2018) · cites this paper as Van Boxtel and Lu, 2013
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · cited 2× · see citing works
“Such scenarios go beyond a simple Bayesian account based on priors and likelihoods and could be investigated with computational models in the future (Rao and Ballard, 1999; Feldman and Friston, 2010).”
BackgroundThe weak coherence account: detail-focused cognitive style in autism spectrum disorders
Happé, F., & Frith, U. (2006) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-005-0039-0 · Cited by 2,884 · see citing works
Enhanced Perceptual Functioning in Autism: An Update, and Eight Principles of Autistic Perception
Mottron, L., Dawson, M., Soulières, I., Hubert, B., & Burack, J. (2006) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-005-0040-7 · Cited by 1,957 · see citing works
Just, M. A. (2004) · Brain · doi:10.1093/brain/awh199 · Cited by 1,589 · see citing works
Attention, uncertainty, and free-energy
Feldman, H., & Friston, K. J. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00215 · Cited by 1,365 · see citing works
When the world becomes ‘too real’: a Bayesian explanation of autistic perception
Pellicano, E., & Burr, D. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.08.009 · Cited by 1,156 · see citing works
Predictive coding: an account of the mirror neuron system
Kilner, J. M., Friston, K. J., & Frith, C. D. (2007) · Cognitive Processing · doi:10.1007/s10339-007-0170-2 · Cited by 1,134 · see citing works
Schultz, R. T. (2005) · International Journal of Developmental Neuroscience · doi:10.1016/j.ijdevneu.2004.12.012 · Cited by 1,071 · see citing works
Belmonte, M. K., Cook, E. H., Anderson, G. M., Rubenstein, J. L. R., Greenough, W. T., Beckel-Mitchener, A., Courchesne, E., Boulanger, L. M., Powell, S. B., Levitt, P. R., et al. (2004) · Molecular Psychiatry · doi:10.1038/sj.mp.4001499 · Cited by 442 · see citing works
Feature-based attention modulates feedforward visual processing
Zhang, W., & Luck, S. J. (2008) · Nature Neuroscience · doi:10.1038/nn.2223 · Cited by 317 · see citing works
Reduced Generalization in Autism: An Alternative to Weak Central Coherence
Plaisted, K. C. (2001) · Apollo (University of Cambridge) · Cited by 205 · see citing works
Intra-individual variability in ADHD, autism spectrum disorders and Tourette's syndrome
Geurts, H. M., Grasman, R. P. P. P., Verté, S., Oosterlaan, J., Roeyers, H., van Kammen, S. M., & Sergeant, J. A. (2008) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2008.06.013 · Cited by 192 · see citing works
Brosnan, M., Gullon-Scott, F., Fox, S., & Pye, J. (2004) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2004.00237.x · Cited by 181 · see citing works
Alternative Bayesian accounts of autistic perception: Comment on Pellicano and Burr
Brock, J. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.10.005 · Cited by 173 · see citing works
Bayes in the Brain—On Bayesian Modelling in Neuroscience
Colombo, M., & Seriès, P. (2012) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/axr043 · Cited by 164 · see citing works
Ambiguity and invariance: two fundamental challenges for visual processing
Rust, N. C., & Stocker, A. A. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.04.013 · Cited by 52 · see citing works
Liu, Y., Cherkassky, V. L., Minshew, N. J., & Just, M. A. (2011) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2011.04.005 · Cited by 47 · see citing works
Reduced Generalization in Autism: An Alternative to Weak Central Coherence
Burack, J. A., Charman, T., Yirmiya, N., & Zelazo, P. R. (2001) · Routledge eBooks · doi:10.4324/9781410600196-15 · Cited by 26 · see citing works
Response to Brock: noise and autism
Pellicano, E., & Burr, D. C. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.10.004 · Cited by 11 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Precise minds in uncertain worlds: Predictive coding in autism.
Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., de-Wit, L., & Wagemans, J. (2014) · Psychological Review · doi:10.1037/a0037665 · Cited by 885 · see citing works
An aberrant precision account of autism
Lawson, R. P., Rees, G., & Friston, K. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00302 · Cited by 641 · see citing works
The impact of atypical sensory processing on social impairments in autism spectrum disorder
Thye, M. D., Bednarz, H. M., Herringshaw, A. J., Sartin, E. B., & Kana, R. K. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2017.04.010 · Cited by 513 · see citing works
“Predictive coding, hypo-priors, Weak Central Coherence, and Enhanced Perceptual Functioning accounts characterize perception in ASD as under-utilizing top-down processes (context, prior knowledge, or global coherence) or over-functioning of bottom-up sensory processes (Pellicano and Burr, 2012; Van Boxtel and Lu, 2013…”
InfluentialBaum, S. H., Stevenson, R. A., & Wallace, M. T. (2015) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2015.09.007 · Cited by 370 · see citing works
Bayesian approaches to autism: Towards volatility, action, and behavior.
Palmer, C. J., Lawson, R. P., & Hohwy, J. (2017) · Psychological Bulletin · doi:10.1037/bul0000097 · Cited by 307 · see citing works
Prediction in autism spectrum disorder: A systematic review of empirical evidence
Cannon, J., O'Brien, A. M., Bungert, L., & Sinha, P. (2021) · Autism Research · doi:10.1002/aur.2482 · Cited by 168 · see citing works
Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?
Haker, H., Schneebeli, M., & Stephan, K. E. (2016) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2016.00107 · Cited by 130 · see citing works
Central tendency effects in time interval reproduction in autism
Karaminis, T., Cicchini, G. M., Neil, L., Cappagli, G., Aagten-Murphy, D., Burr, D., & Pellicano, E. (2016) · Scientific Reports · doi:10.1038/srep28570 · Cited by 112 · see citing works
Predictive Coding: a Theoretical and Experimental Review
Millidge, B., Seth, A., & Buckley, C. L. (2021) · arXiv · doi:10.48550/arxiv.2107.12979 · Cited by 106 · see citing works
Palmer, C. J., Seth, A. K., & Hohwy, J. (2015) · Consciousness and Cognition · doi:10.1016/j.concog.2015.04.007 · Cited by 101 · see citing works
Palmer, C. J., Paton, B., Kirkovski, M., Enticott, P. G., & Hohwy, J. (2015) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2014.1557 · Cited by 82 · see citing works
Sensory Prediction Errors Are Less Modulated by Global Context in Autism Spectrum Disorder
Goris, J., Braem, S., Nijhof, A. D., Rigoni, D., Deschrijver, E., Van de Cruys, S., Wiersema, J. R., & Brass, M. (2018) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.02.003 · Cited by 79 · see citing works
Sensory processing in autism across exteroceptive and interoceptive domains.
Proff, I., Williams, G. L., Quadt, L., & Garfinkel, S. N. (2022) · Psychology & Neuroscience · doi:10.1037/pne0000262 · Cited by 69 · see citing works
Haarsma, J., Knolle, F., Griffin, J. D., Taverne, H., Mada, M., Goodyer, I. M., The NSPN Consortium, Fletcher, P. C., & Murray, G. K. (2020) · Journal of Abnormal Psychology · doi:10.1037/abn0000494 · Cited by 60 · see citing works
Palmer, C. J., Paton, B., Hohwy, J., & Enticott, P. G. (2013) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2013.06.020 · Cited by 55 · see citing works
Repetition suppression to objects is modulated by stimulus-specific expectations
Utzerath, C., St. John-Saaltink, E., Buitelaar, J., & de Lange, F. P. (2017) · Scientific Reports · doi:10.1038/s41598-017-09374-z · Cited by 28 · see citing works
Skewes, J. C., & Gebauer, L. (2016) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-016-2774-9 · Cited by 27 · see citing works
A predictive coding account of psychotic symptoms in autism spectrum disorder
van Schalkwyk, G. I., Volkmar, F. R., & Corlett, P. R. (2017) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-017-3065-9 · Cited by 25 · see citing works
Toward a Multidimensional Understanding of Misophonia Using Cluster-Based Phenotyping
Norris, J. E., Kimball, S. H., Nemri, D. C., & Ethridge, L. E. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.832516 · Cited by 25 · see citing works
Predictive coding in neuropsychiatric disorders: a systematic transdiagnostic review.
Qela, B., Damiani, S., De Santis, S., Groppi, F., Pichiecchio, A., Asteggiano, C., Brondino, N., Monteleone, A. M., Grassi, L., Politi, P., et al. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106020 · Cited by 26 · see citing works
“The latter finding is consistent with the predictive coding models of ASD (Pellicano and Burr, 2012; Van Boxtel and Lu, 2013), which postulate that individuals with ASD present impairments in high-level sensory perception (i.e., hypo-priors) rather than low-level sensory perception (Kern et al.,…”
Interpersonal Motor Interactions Shape Multisensory Representations of the Peripersonal Space
Fanghella, M., Era, V., & Candidi, M. (2021) · Brain Sciences · doi:10.3390/brainsci11020255 · Cited by 22 · see citing works
Electrophysiological alterations in motor-auditory predictive coding in autism spectrum disorder
van Laarhoven, T., Stekelenburg, J. J., Eussen, M. L. J. M., & Vroomen, J. (2019) · Autism Research · doi:10.1002/aur.2087 · Cited by 22 · see citing works
Lacroix, A., Dutheil, F., Logemann, A., Cserjesi, R., Peyrin, C., Biro, B., Gomot, M., & Mermillod, M. (2022) · Autism · doi:10.1177/13623613211062776 · Cited by 21 · see citing works
- Authors
- van Boxtel, J. J. A., & Lu, H.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2013
- Volume
- 4
- Pages
- 19-19
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC3556598
- OpenAlex
- W2030520068
- Cited by
- 210
- References
- 19
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 13 · 2023: 16 · 2022: 13 · 2021: 29 · 2020: 15 · 2019: 15 · 2018: 10 · 2017: 29 · 2016: 22 · 2015: 12 · 2014: 13 · 2013: 5
- FWCI
- 9.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Social Sciences › Psychology › Social Psychology › Action Observation and Synchronization
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- predictive coding, autism spectrum disorder, Bayesian brain, perceptual prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review