From cognitive abstraction to adaptive behavior: neural bases of concept learning in autistic adolescents

BACKGROUND: Learned knowledge does not consistently generalize to new contexts in autistic individuals, limiting potential for adapting to real-world demands. This challenge is hypothesized to stem from difficulties with forming abstract representations, potentially arising from perceptual processing that favors local details over the gestalt. We tested the prediction that generalization would be primarily based on exemplar-specific representations in autistic youth using computational modelling coupled with neuroimaging. METHODS: Sixty-four autistic adolescents without intellectual disability (69% males; ages 14-18 years) completed a category generalization task during functional magnetic resonance imaging at two time points. Computational models estimated abstract (prototype-based) and specific (exemplar-based) representations and underlying neural correlates. We further examined associations with adaptive functioning and moderation by autistic traits. RESULTS: Contrary to predictions, we observed a consistent prototype-dominant majority, a subgroup who generalized without consistent representational reliance, and a small minority who failed to acquire category structure. Neural prototype correlates were observed in bilateral ventromedial prefrontal cortex (VMPFC), inferior parietal lobule (IPL), right frontal pole, and right lateral occipital cortex, while neural exemplar correlates were observed in bilateral cuneus. Better generalization predicted better real-world adaptive functioning. Moreover, greater prototype-related activation in left IPL predicted better adaptive functioning in participants with higher autistic traits. LIMITATIONS: Generalizability is limited to autistic adolescents without intellectual disability as the design did not include typically developing youth. CONCLUSIONS: These findings challenge the prevailing view that concept learning in autism relies primarily on hyper-specific perceptual processing, identify meaningful variability in representational strategies, and reveal neural pathways through which abstract representation may support real-world adaptive behavior. These findings set the foundation for exploring abstract representation as an intervention target in autism.

Authors

Institutions

Publication Details

Journal
Molecular Autism
Published
2026-05-22
DOI
https://doi.org/10.1186/s13229-026-00721-4
Primary Topic
Autism Spectrum Disorder Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

From cognitive abstraction to adaptive behavior: neural bases of concept learning in autistic adolescents

Brylee Hawkins, Dagmar Zeithamová, Chandan J. Vaidya, Hua Xie et al.
Molecular Autism
Autism Spectrum Disorder Research
article

From cognitive abstraction to adaptive behavior: neural bases of concept learning in autistic adolescents

Brylee Hawkins, Dagmar Zeithamová, Chandan J. Vaidya, Hua Xie, Andrea Lopez, Kat Sharp, Ashley S. VanMeter, Lauren Kenworthy, Hannah Puckett, William D. Gaillard, Yijun Chen, Alyssa Verbalis
article en

Abstract

BACKGROUND: Learned knowledge does not consistently generalize to new contexts in autistic individuals, limiting potential for adapting to real-world demands. This challenge is hypothesized to stem from difficulties with forming abstract representations, potentially arising from perceptual processing that favors local details over the gestalt. We tested the prediction that generalization would be primarily based on exemplar-specific representations in autistic youth using computational modelling coupled with neuroimaging. METHODS: Sixty-four autistic adolescents without intellectual disability (69% males; ages 14-18 years) completed a category generalization task during functional magnetic resonance imaging at two time points. Computational models estimated abstract (prototype-based) and specific (exemplar-based) representations and underlying neural correlates. We further examined associations with adaptive functioning and moderation by autistic traits. RESULTS: Contrary to predictions, we observed a consistent prototype-dominant majority, a subgroup who generalized without consistent representational reliance, and a small minority who failed to acquire category structure. Neural prototype correlates were observed in bilateral ventromedial prefrontal cortex (VMPFC), inferior parietal lobule (IPL), right frontal pole, and right lateral occipital cortex, while neural exemplar correlates were observed in bilateral cuneus. Better generalization predicted better real-world adaptive functioning. Moreover, greater prototype-related activation in left IPL predicted better adaptive functioning in participants with higher autistic traits. LIMITATIONS: Generalizability is limited to autistic adolescents without intellectual disability as the design did not include typically developing youth. CONCLUSIONS: These findings challenge the prevailing view that concept learning in autism relies primarily on hyper-specific perceptual processing, identify meaningful variability in representational strategies, and reveal neural pathways through which abstract representation may support real-world adaptive behavior. These findings set the foundation for exploring abstract representation as an intervention target in autism.

Molecular Autism
Children's National (US), University of Oregon (US), Georgetown University (US), George Washington University (US), Georgetown University Medical Center (US)
Quality Education
Openalex Percentile: Top 5%
Autism Spectrum Disorder Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.