The DOI matches its Crossref record; 306 of the 306 references it cites were found in a registry or an index.
Precise minds in uncertain worlds: Predictive coding in autism.
Open accessAbstract
There have been numerous attempts to explain the enigma of autism, but existing neurocognitive theories often provide merely a refined description of 1 cluster of symptoms. Here we argue that deficits in executive functioning, theory of mind, and central coherence can all be understood as the consequence of a core deficit in the flexibility with which people with autism spectrum disorder can process violations to their expectations. More formally we argue that the human mind processes information by making and testing predictions and that the errors resulting from violations to these predictions are given a uniform, inflexibly high weight in autism spectrum disorder. The complex, fluctuating nature of regularities in the world and the stochastic and noisy biological system through which people experience it require that, in the real world, people not only learn from their errors but also need to (meta-)learn to sometimes ignore errors. Especially when situations (e.g., social) or stimuli (e.g., faces) become too complex or dynamic, people need to tolerate a certain degree of error in order to develop a more abstract level of representation. Starting from an inability to flexibly process prediction errors, a number of seemingly core deficits become logically secondary symptoms. Moreover, an insistence on sameness or the acting out of stereotyped and repetitive behaviors can be understood as attempts to provide a reassuring sense of predictive success in a world otherwise filled with error. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
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“However, by logical extension, beyond a single ambiguous sensory event, all Bayesian accounts imply that dynamic learning about structural regularities (i.e. the formation of priors) is likely impaired in ASD 2–7.”
From Adults with autism overestimate the volatility of the sensory environment (2017) · cites this paper as [3]
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“The influential theory by Pellicano and Burr (40) posits that autistic individuals form unusually weak priors, and this has been extended into predictive coding accounts that highlight atypical inference processes in ASD (41).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [41]
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“Bayesian theories of perception in ASD1 propose that reduced weighting of prior expectations, relative to sensory inputs, leads to the perceptual atypicalities associated with the condition2–7”
From Adults with autism overestimate the volatility of the sensory environment (2017) · cites this paper as [3]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Such an explanation is implied by conservative interpretations of non-hierarchical Bayesian accounts of ASD 2 and predictive processing accounts that emphasise “uniform” inflexibly high precision in sensory processing 3.”
From Adults with autism overestimate the volatility of the sensory environment (2017) · cites this paper as [3]
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“More recently, theories have been proposed that combine, integrate and extend these theories, e.g. the Predictive Coding account (PC; e.g., Van de Cruys et al., 2014) and the Polyvagal Theory (PT; e.g., Brown, 2020).”
From Innovations of the ICD-11 in the Field of Autism Spectrum Disorder: A Psychological Approach (2022) · cites this paper as Van de Cruys et al., 2014
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“neurodevelopmental disorders such as autism spectrum disorder [132,133] and schizophrenia [134,135] have been linked to an atypical integration of prior and incoming information”
From How Do Expectations Shape Perception? (2018) · cites this paper as [133]
In the citing paper
“A candidate theory that might fill this gap is what one might refer to as the “Bayesian brain” perspective on ASD (2–4).”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [4]
In the citing paper
“Alternatively, as suggested by Lawson et al. (3) and Van de Cruys et al. (4), precisions could be tuned abnormally.”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [4]
In the citing paper
“Finally, Van de Cruys et al. (4) pointed out that normal, or even tight, high-order beliefs could be present in ASD, provided that their effects are outweighed by overly high precision of sensory prediction errors.”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [4]
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“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 [4]
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“The recently developed Bayesian brain theories of ASD can not only explain cardinal features of autistic symptomatology but also a broad range of previous empirical findings in the domain of neuropsychology, neurophysiology, and functional neuroimaging.”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [4]
In the citing paper
“the same theme of an imbalance between bottom-up and top-down processing figures prominently in current debates about autism (33,34).”
From Shift toward prior knowledge confers a perceptual advantage in early psychosis and psychosis-prone healthy individuals (2015) · cites this paper as [33]
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Metadata
- Authors
- Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., de-Wit, L., & Wagemans, J.
- Affiliations
- KU Leuven (BE)
- Venue
- Psychological Review
- Published
- 2014
- Volume
- 121
- Issue
- 4
- Pages
- 649-675
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W2098533845
- Semantic Scholar
- 60c6614872c60b9708488f47521c1c59eda1b9ff
- Cited by
- 885
- References
- 297
- Citations by year
- 2026: 102 · 2025: 81 · 2024: 80 · 2023: 109 · 2022: 85 · 2021: 106 · 2020: 74 · 2019: 76 · 2018: 53 · 2017: 51 · 2016: 35 · 2015: 30 · 2014: 3
- FWCI
- 22.13
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- other-oa
- Locations
- Psychological Review (publishedVersion)
- PubMed (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Funders
- Flemish Government (METH/08/02)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Social Sciences › Psychology › Social Psychology › Action Observation and Synchronization
- Keywords
- autism spectrum disorder, predictive coding, prediction error, executive function, theory of mind, central coherence, meta-learning, error tolerance, sameness, stereotypy, social cognition
- MeSH terms
- Autism Spectrum Disorder, Humans, Mental Processes
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 ›
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