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Cited by 466

The DOI matches its Crossref record; 241 of the 241 references it cites were found in a registry or an index.

The neuropsychology of autism

Happe, F., Frith, U.·1996·Brain

Abstract

In this review, we aim to bring together major trends in autism research at three levels: biology, behaviour and cognition. We propose that cognitive theories are vital in neuropsychology, which seeks to make connections between brain abnormality and behavioural symptoms. Research at each of the three levels is incomplete, but important advances have been made. At the biological level, there is strong evidence for genetic factors, although the mechanism is, as yet, unknown. At the behavioural level, diagnosis and education are becoming more coherent and less controversial, although the possibility of autism subtypes has provoked new debate. At the cognitive level, three major theories are proving fruitful (mentalizing impairment, executive dysfunction and weak central coherence), although the relation and overlap between these is uncertain. Rapidly advancing technology and methodology (e.g. brain imaging, gene mapping), as tools of cognitive theory, may help to make autism one of the first developmental disorders to be understood at the neuropsychological level.

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Crossref · 10.1093/brain/119.4.1377

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Metadata

Authors
Happe, F., & Frith, U.
Venue
Brain
Published
1996-08-01
Volume
119
Issue
4
Pages
1377-1400
ISSN
  • 0006-8950 (ISSN-L)
  • 1460-2156
OpenAlex
W2141692971
Cited by
466
References
241
FWCI
5.03
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The neuropsychology of autism. https://app.cochat.ai/papers/10.1093/brain/119.4.1377
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Happe, F., & Frith, U. (1996). The neuropsychology of autism. Brain. https://doi.org/10.1093/brain/119.4.1377

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.