So what about Rachel's future?
Research Interests
I study learning systems with a focus on how attention is allocated and reallocated as people navigate multiple goals, changing task demands, and uncertainty. I treat multitasking as one context within learning where strategy shifts and control limits become especially visible.
Core Questions
How does attention shift across tasks as demands compete for limited resources?
How do people adapt strategies over time when task structure changes or becomes unpredictable?
Why do individuals differ in their ability to learn and adjust under load?
When does subjective experience (confidence, perceived control) diverge from objective performance, and what predicts that mismatch?
How I Think About the Problem
Learning system → attention allocation → multitasking as a stress test
Learning system: behavior changes through feedback and experience in dynamic environments
Attention allocation: a central mechanism that determines what information is sampled and prioritized
Multitasking: a high-demand setting that reveals tradeoffs, adaptation, and miscalibration
Methods I Use
Behavioral experiments to isolate task demands and track performance changes
Process-level measures (e.g., eye-tracking) to capture how attentional sampling unfolds over time
Model-driven analysis to quantify strategy shifts and individual differences
Why This Direction
I am interested in mechanistic explanations of how people learn and maintain control in complex environments. Long term, I want these insights to support real-world applications, but my immediate focus is building strong experimental foundations in attention, multitasking, and cognitive control.
Currently Learning
Interaction design language and systems thinking, focusing on how interface structures convey meaning and shape behavior.
Eye-tracking methodology, including experimental setup, calibration, and data analysis (self-taught).
Next Step
My immediate goal is to deepen my experimental training in studying learning and adaptation under competing demands, using controlled multitasking paradigms and naturalistic task environments.
I am particularly interested in leveraging immersive platforms such as driving simulation and virtual reality to examine how attention is allocated and strategies evolve as task complexity increases.
By combining behavioral performance measures with process-level indices of attention, such as eye-tracking, I aim to better characterize individual differences in learning, control, and strategy selection over time.
These next steps will allow me to build a strong mechanistic foundation for understanding how people adapt to complex, real-world task demands.