Computational neuroscientist. Assistant professor @USC psychology. Previously @Princeton and @Donders
www.piraylab.com
Payam Piray
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I argue that we need to account for the size of the model space when determining sample size, as larger model spaces reduce power. I also show that the commonly used “fixed effects” model selection approach is statistically unreliable. An analysis of the literature suggests shortcomings in both
Payam Piray
We review studies showing that when brain areas face similar computational demands in social and non-social context, they perform the same computations. We argue that exaptation (repurposing of traits for new functions) played a key role in brain evolution.
Our experiences have countless details, and it can be hard to know which matter.
How can we behave effectively in the future when, right now, we don't know what we'll need?
Out today in @nathumbehav.nature.com , @marcelomattar.bsky.social and I find that people solve this by using episodic memory.
Thrilled that my paper is out in the @nature.com. We explored how the brain builds complex tasks by compositionally combining simpler sub-task representations. The brain flexibly performs multiple tasks by dynamically reusing neural subspaces for sensory inputs and motor actions
rdcu.be/eRVUk
🎇 Excited to finally share JL Romero Sosa’s publication! Results are from single-cell imaging in different subregions of rat frontal cortex during ✨de novo learning. Spoiler: everything is not everywhere all at once www.nature.com/articles/s41...
Good news of the front of restoring UCLA's grants! calmatters.org/education/20...
Nicholas and Mattar found that people use episodic memory to make decisions when it is unclear what will be needed in the future. These findings reveal how the rich representational capacity of episod...
Happy to share my new paper published in @nathumbehav.nature.com: A critical look at statistical power in computational modeling studies, particularly those based on model selection.
www.nature.com/articles/s41...
