This research shows that pauses in information streams alter decision-making. After a break, the brain increases effort, giving greater weight to subsequent information—a “peak-after-break” effect. A computational model explains this as a performance-effort tradeoff. Findings challenge traditional theories and suggest strategic pauses can shape attention, memory, and judgment.

In our complex world, how do humans learn and make decisions when their cognitive resources are limited? My thesis introduces a new theory called "policy compression" to answer this question! The basic idea is that people simplify their decision-making processes to reduce the mental effort required, without significantly compromising the benefits or rewards of those decisions. I use computational modeling, human experiments, and brain studies in rats to explain why people exhibit certain decision-making patterns, like the tendency to stick with familiar choices, and why they use strategies like "chunking" to reduce mental load. I also propose that different brain regions work together to balance mentally taxing decisions with more automatic, habitual decisions. This allows the brain to optimize behavior in complex environments. In conclusion, my thesis offers a new way to understand how humans and animals make decisions with limited mental resources, and shows how the brain organizes itself to handle decision-making efficiently.

This PhD uses brain-inspired AI to decode vision from neural data. Using human fMRI (24 hours of Doctor Who) and monkey electrophysiology, signals are transformed into 2D brain maps to improve reconstruction. The model learns receptive-field structure, compares contributions of V1/V4/IT, and aims for efficient, interpretable decoding with applications to neuroscience and BCIs.

This research explores how immune-related cells and molecules, beneficial in wound healing, may become harmful in Parkinson’s disease. Using the fruit fly as a model organism, the study investigates which inflammatory processes contribute to brain damage. Early results suggest that excessive activation worsens degeneration, offering potential targets for future therapies.

This research develops a virtual human model and predictive algorithm to detect blast-induced traumatic brain injuries in real time. Using simulations and body-mounted sensors, the system estimates injury risk on the battlefield, helping medics and commanders make rapid decisions to protect soldiers and improve mission safety.

This research examines how sugar consumption and impaired reward sensitivity predict later alcohol-related behaviors in rats. Findings show that addiction-like responses to sugar can forecast alcohol responses, suggesting shared neural mechanisms. Understanding these early behavioral markers may help identify addiction risk factors and inform prevention strategies.