Ashley de Marchena is assistant professor of psychology and neuroscience at the University of the Sciences in Philadelphia, Pennsylvania. Her research focuses on communication skills and related behaviors in autism.
Ashley de Marchena
Assistant professor
University of the Sciences
From this contributor
Motor skills in autism: A missed opportunity
Motor differences are more relevant than has historically been appreciated for understanding, assessing and supporting people on the spectrum.
Motor skills in autism: A missed opportunity
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Autism-linked genes alter sleep behavior, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 13 April.
Autism-linked genes alter sleep behavior, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 13 April.
This paper changed my life: Erin Calipari ponders the nuances of rewarding and aversive stimuli
A 1960s study by Kelleher and Morse found that lever pressing in squirrel monkeys depended not on whether they received a reward or shock, but on the rules of the task. This taught Calipari to think deeply about factors that influence how behavior is generated and maintained.
This paper changed my life: Erin Calipari ponders the nuances of rewarding and aversive stimuli
A 1960s study by Kelleher and Morse found that lever pressing in squirrel monkeys depended not on whether they received a reward or shock, but on the rules of the task. This taught Calipari to think deeply about factors that influence how behavior is generated and maintained.
Why neural foundation models work, and what they might—and might not—teach us about the brain
These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?
Why neural foundation models work, and what they might—and might not—teach us about the brain
These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?