Neural correlates of hierarchical predictive processes in autistic adults
Open accessAbstract
Bayesian theories of autism spectrum disorders (ASD) suggest that atypical predictive mechanisms could underlie the autistic symptomatology, but little is known about their neural correlates. Twenty-six neurotypical (NT) and 26 autistic adults participated in an fMRI study where they performed an associative learning task in a volatile environment. By inverting a model of perceptual inference, we characterized the neural correlates of hierarchically structured predictions and prediction errors in ASD. Behaviorally, the predictive abilities of autistic adults were intact. Neurally, predictions were encoded hierarchically in both NT and ASD participants and biased their percepts. High-level predictions were following activity levels in a set of regions more closely in ASD than NT. Prediction errors yielded activation in shared regions in NT and ASD, but group differences were found in the anterior cingulate cortex and putamen. This study sheds light on the neural specificities of ASD that might underlie atypical predictive processing.
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These findings differ from those of a previous study showing decreased neural correlates of pwPE in autism (Sapey-Triomphe et al., 2023).
“These findings differ from those of a previous study showing decreased neural correlates of pwPE in autism (Sapey-Triomphe et al., 2023).”
From Are prediction error modifications domain specific in autism but domain general in ADHD? (2025) · cites this paper as Sapey-Triomphe et al., 2023
The Results (Neural correlates of prediction errors; page unavailable) report a stronger association between mid-level precision-weighted prediction errors and putamen activity in autistic adults, not a decreased correlate. The Discussion likewise interprets these findings as potentially reflecting stronger prediction errors in autism; it reports no significant group difference for high-level precision-weighted prediction errors. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredIn autism spectrum disorder, mid-level predictions were associated with higher activation of the retrosplenial cortex, left putamen, and anterior cingulate cortex compared to healthy controls.
“mid-level predictions (i.e., predictions based on sensory information within a context) were associated with higher activation of the retrosplenial cortex, left putamen, and anterior cingulate cortex in ASD compared to HC”
From Predictive coding in neuropsychiatric disorders: a systematic transdiagnostic review. (2025) · cites this paper as Sapey-Triomphe et al., 2023
The Results (Neural correlates of predictions) report a stronger correlation between mid-level predictions and right retrosplenial activity in ASD than in neurotypical controls, not higher activation overall. The Discussion (Fig. 6 summary) attributes the left putamen and anterior cingulate findings to mid-level prediction errors instead; it says anterior cingulate activity correlated with those errors in controls but not in ASD. (LLM, conf=0.88)
Experimental — accuracy not yet measuredIn adults with autism spectrum disorder, high-level predictions led to greater activations of the left orbitofrontal cortex, bilateral posterior cingulate cortex, and right posterior temporal sulcus compared to healthy controls.
“High-level predictions (i.e., more abstract predictions) led to greater activations of the left orbitofrontal cortex, bilateral posterior cingulate cortex, and right posterior temporal sulcus in ASD compared to HC”
From Predictive coding in neuropsychiatric disorders: a systematic transdiagnostic review. (2025) · cites this paper as Sapey-Triomphe et al., 2023
The Results (Neural correlates of predictions) and Discussion (Fig. 6 summary) identify the same three regions and report that their activity followed high-level predictions more closely in autistic than neurotypical adults. They describe stronger correlations with predictions, not generally greater activation in those regions. (LLM, conf=0.87)
Experimental — accuracy not yet measuredIn Sapey-Triomphe et al. (2023), neural activation associated with prediction errors was decreased in autistic adults in the anterior cingulate cortex and the putamen.
“the authors also found associations between neural activation and prediction errors to be decreased in autistic adults in the anterior cingulate cortex and the putamen.”
From Are prediction error modifications domain specific in autism but domain general in ADHD? (2025) · cites this paper as Sapey-Triomphe et al., 2023
The Results and Discussion report that mid-level prediction errors correlated with anterior cingulate cortex activity in neurotypical adults but not in autistic adults. The putamen finding runs in the opposite direction from the claim: its association with prediction errors was stronger in autistic adults. (LLM, conf=0.88)
Experimental — accuracy not yet measuredIn autism spectrum disorder, low-level predictions correlated only with activation of the occipital cortex and postcentral gyrus.
“low-level predictions (i.e., sensory-motor predictions) correlated only with the activation of oc cipital cortex and postcentral gyrus (Sapey-Triomphe et al., 2023).”
From Predictive coding in neuropsychiatric disorders: a systematic transdiagnostic review. (2025) · cites this paper as Sapey-Triomphe et al., 2023
Sapey-Triomphe and colleagues found no differences in tonic volatility parameters between autistic and non-autistic adults, at odds with Lawson et al.
“Sapey-Triomphe and colleagues [14, 15] also used a probabilistic associative learning task but found no differences in the levels of tonic volatility parameters extracted with the HGF between autistic and non-autistic adults, at odds with Lawson et al. [1].”
From Predicting emotional valence in autism: a preregistered study in the Bayesian Brain framework (2026) · cites this paper as [15]
Multiple studies using the HGF have documented comparable tonic volatility estimates in autistic and non-autistic adults in probabilistic associative learning tasks, the Wisconsin Card-Sorting task, and a size-illusion task.
“Multiple studies using the HGF have now documented this similarity: probabilistic associative learning tasks [13, 14], the Wisconsin Card-Sorting task measuring cognitive flexibility and set-shifting [15] and a size-illusion task [16], experiment [9].”
From Predicting emotional valence in autism: a preregistered study in the Bayesian Brain framework (2026) · cites this paper as [15]
Sapey-Triomphe et al. (2023) demonstrated differences in neural correlates of prediction errors in autistic compared with non-autistic adults.
“A recent study demonstrated differences in neural correlates of prediction errors in autistic compared with non-autistic adults (Sapey-Triomphe et al., 2023).”
From Are prediction error modifications domain specific in autism but domain general in ADHD? (2025) · cites this paper as Sapey-Triomphe et al., 2023
The results of Sapey-Triomphe et al. suggest that predictive impairments postulated for ASD are associated with differences in neural correlates of prediction errors.
“These results suggest that predictive impairments postulated for ASD are associated with differences in neural correlates of prediction errors.”
From Are prediction error modifications domain specific in autism but domain general in ADHD? (2025) · cites this paper as Sapey-Triomphe et al., 2023
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Fournier, D. I., Eddy, M. C., DeAngeli, N. E., Huszár, R., & Bucci, D. J. (2019) · Behavioural Brain Research · doi:10.1016/j.bbr.2019.111920 · Cited by 13 · see citing works
Ventral stream hierarchy underlying perceptual organization in adolescents with autism
Sapey‐Triomphe, L., Boets, B., Eylen, L. V., Noens, I. L. J., Sunaert, S. G., Steyaert, J. G., & Wagemans, J. P. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102197 · Cited by 13 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
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Cited by
Sorted by citations
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
Sapey-Triomphe, L.-A., Sanchez, G., Hénaff, M.-A., Sonié, S., Schmitz, C., & Mattout, J. (2023) · npj Science of Learning · doi:10.1038/s41539-023-00207-5 · Cited by 11 · see citing works
Self-citationBayesian Workflow for Generative Modeling in Computational Psychiatry
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Are prediction error modifications domain specific in autism but domain general in ADHD?
Plank, I. S., Yurova, A., Pior, A., Nowak, J., Bierlich, A. M., Papazov, B., Shi, Z., & Falter-Wagner, C. M. (2025) · Imaging neuroscience · doi:10.1162/imag.a.942 · Cited by 3 · see citing works
“A recent study demonstrated differences in neural correlates of prediction errors in autistic compared with non-autistic adults ( Sapey-Triomphe et al., 2023).”
Predicting emotional valence in autism: a preregistered study in the Bayesian Brain framework
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“Nonetheless, some evidence for group differences does exist [9], [16], experiment [1], and these studies also found differences in learning rates or their updates, while others found comparable learning rates in autistic and non-autistic adults [15], [16], experiment [9].”
Influential
- Authors
- Sapey-Triomphe, L.-A., Pattyn, L., Weilnhammer, V., Sterzer, P., & Wagemans, J.
- Affiliations
- KU Leuven (BE)
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Venue
- Nature Communications
- Published
- 2023-06-19
- Volume
- 14
- Issue
- 1
- Pages
- 3640-3640
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10279690
- OpenAlex
- W4381249236
- Cited by
- 35
- References
- 82
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 8 · 2023: 3
- FWCI
- 5.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- Lirias (KU Leuven) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Vlaamse regering (METH/14/02)
- Horizon 2020 Framework Programme
- H2020 Marie Skłodowska-Curie Actions (H2020)
- 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
- autism spectrum disorder, predictive processing, hierarchical prediction, prediction error, perceptual inference, associative learning, environmental volatility, anterior cingulate cortex, putamen
- MeSH terms
- Autism Spectrum Disorder, Adult, Autistic Disorder, Bayes Theorem, Humans, Magnetic Resonance Imaging
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.
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