Savannah Cookson

Position: Ph.D. Candidate, Fourth Year


MS (2014) Psychology, Georgia Institute of Technology
Thesis: Neural Mechanisms for Stimulus-Response Preparation
BS (2011) Biomedical Engineering, Georgia Institute of Technology

Fun Fact about Me:
I was born with a hole in my heart that should have been operated on but closed naturally at 3 years old.

About Me

I am a fourth-year Ph.D. candidate at CoNTRoL. As an undergraduate, I was involved in the LaPlaca Neural Injury Biomechanics and Repair Laboratory (NeuroLab), where I became interested in the mechanics of the brain as related to behavior. My ultimate goal is a career as a research professor investigating the mind and brain at all levels of cognition in order to discover the links between those levels and, one day, elucidate the full story behind cognition.

Outside of lab and class, I dabble in theater, sewing, other arts and crafts, tumbling, and eating lots of good food. Some of my other academic interests include computer science (I designed this website – thanks WordPress!), math, statistics, engineering, and design. I have a kitty named Blink and a boyfriend named Craig, and I love both of them to death!

Research Interests

My current projects investigate how control mechanisms use context to affect task processing by biasing activity in the brain based on information given before the task is performed and how altering the implicit sub-grouping of stimulus-response mappings into “task sets” along different dimensions affects this implementation of control.

In general, I am interested in understanding the structure-function relationship in the prefrontal cortex and its connections. I am intrigued by research that bridges traditional fields in order to develop new methods and reach new conclusions about the structure and implementation of cognitive mechanisms.

Areas of interest:
– Cognitive neuroscience
– fMRI techniques and analyses
– Cognitive control
– Functional networks
– Task set representation
– Neural mechanisms of cognition
– Machine learning (classifiers in multi-voxel pattern analysis)
– Object and scene perception
– Computer modeling