Mark Johnson is professor of experimental psychology and head of the psychology department at the University of Cambridge in the United Kingdom.
Mark Johnson
Professor
Birkbeck University of London
From this contributor
Autism may arise from brain’s response to early disturbances
Autism is not a developmental disorder, but rather the brain’s adaptive response to early genetic or environmental disturbances, says Mark Johnson.

Autism may arise from brain’s response to early disturbances
Executive confusion
Among siblings of children with autism, those with better prefrontal cortex functioning — observable as relatively strong executive functions for their age — are better able to compensate for atypicalities in other brain systems early in life, and are therefore less likely to receive a diagnosis of autism later in their development, argues Mark H. Johnson.
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Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.

Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.
Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.

Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.

The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.