[WTI-trainee] FW: Erica Busch dissertation defense April 9 2:30-4pm

[Guerrero-Medina, Giovanna]

Sharing this info for one of our WTI and SPPC community members!

Cheers,
Giovanna

From: Psych.others <psych.others-bounces at mailman.yale.edu> on behalf of Turk-Browne, Nick <nicholas.turk-browne at yale.edu>
Date: Monday, March 30, 2026 at 10:33 PM
To: psych.all at mailman.yale.edu <psych.all at mailman.yale.edu>
Subject: Erica Busch dissertation defense April 9 2:30-4pm

Hi all,

Erica Busch will be defending her dissertation next Thursday, April 9 from 2:30-4pm in the Workshop (room 1116). The talk will begin at 2:30pm and we’ll wrap up with a reception in the adjacent lounge around 3:30pm.

Erica's dissertation (short abstract below) combines computational insights with cognitive training to advance a new paradigm for brain-computer interfaces and cognitive neuroscience more generally. Her work has implications across cognitive, developmental, and clinical psychology, and so should be of broad relevance to our community.

On behalf of Erica’s committee, I am writing to invite you and anybody you think might be interested to attend!

Thanks,
Nick Turk-Browne


--

Understanding and enhancing human cognition along neural manifolds
Erica L. Busch

A fundamental goal of cognitive neuroscience is to understand how brain activity supports our rich, flexible, and dynamic cognitive abilities. Yet brain activity is extraordinarily complex, involving thousands of interacting signals that continuously evolve over time. One promising idea is that these signals do not explore all possible configurations, but are instead constrained to simpler, low-dimensional structures known as neural manifolds. These manifolds can be thought of as hidden landscapes that guide the evolution of brain activity, limiting which patterns are possible and shaping how they change with experience. In this dissertation, I argue that neural manifolds provide a unifying framework for understanding how the human brain gives rise to the mind, and that their functional significance can be studied not only descriptively, but also causally. Across four empirical chapters, I develop new computational methods and experimental approaches to characterize neural manifolds, test their functional relevance, and examine how they vary across development and individual differences.

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