Quantification of Behavior

When organisms interact with the world, they learn relationships between events and use those relationships to guide future behavior. My work focuses on how these associations are formed, how they influence decision-making, and how behavior can be measured in a precise and interpretable way. Overall, I am particularly interested in associative learning as a foundation for more complex processes, and in identifying patterns in behavior that can be quantified and modeled across tasks and species.

Choice by Exclusion and Fast Mapping

When organisms encounter unfamiliar stimuli, they can respond appropriately without direct prior experience by avoiding known alternatives and selecting the remaining option. This process is referred to as choice by exclusion. I examine how pigeons, humans, and artificial systems form and retain new associations under these conditions. I focus on how familiarity, reinforcement history, and prior associations shape these decisions, and whether these behaviors can be explained through associative mechanisms rather than separate inferential processes.

Conjunction Fallacy 

People sometimes judge the combination of two events as more likely than a single event alone, a pattern known as the conjunction fallacy. I examine whether similar patterns can emerge in rats following structured training. This work tests how learned associations and reinforcement histories influence choices in probabilistic contexts, and whether behavior that appears to violate normative probability judgments can arise from underlying learning processes. A 2 lever choice procedure is currently under review.

Basic Learning Mechanisms

Learning does not only involve forming associations, but also updating them when conditions change. I examine how inhibitory associations are acquired and how they are extinguished over time. This work focuses on how organisms adjust behavior when previously learned relationships no longer hold, and how inhibitory learning influences future responding.