People

Jessica Cantlon uses behavioral and neuroimaging methods to study the origins of quantitative reasoning in human adults, children, and non-human primates. Her research shows the impact of early-developing and evolutionarily primitive nonverbal concepts on human thought. Using behavioral methods, her work observed parallel numerical processing capabilities between human children and non-human primates. Using fMRI, she found that the IPS processes quantitative information in children as young as 4 years of age. Her research implicates a primitive cognitive and neural basis for the development of human mathematical cognition derived from very old, evolutionary processes.

Marissa Laws is a Postdoctoral Researcher at Carnegie Mellon University working in the Kid Neuro Lab under Dr. Jessica Cantlon.  She is interested in using fMRI to study the developmental cognitive neuroscience of math, numbers, and the factors that contribute to these skills, including gender/sex effects and spatial visualization ability.  She earned her PhD from Georgetown University's Interdisciplinary Program in Neuroscience, where she studied brain activity in children with and without developmental dyscalculia (aka math disability) in the lab of Dr. Guinevere Eden.

Teo Ozaydin is a researcher broadly interested in learning, memory and cognition. His work uses a combination of behavioral and neuroimaging methods to study the cognitive processes involved in the mental representations of objects and mathematical concepts. His target populations include adults, children, and rhesus macaques, allowing for developmental and evolutionary comparisons.  

 Jialin Li is interested in the cognitive and the neural mechanisms that support the uniqueness of human intelligence. She studies the differences in reasoning between humans and non-human primates through behavioral experiments, neural imaging techniques, and computational modeling.

Wenjie Li is a Ph.D. student in Neural Computation at Carnegie Mellon University co-advised by Yonatan Bisk. Her research explores how symbol-like behaviors emerge and are represented in both biological and artificial neural networks. She is broadly interested in compositionality, abstraction, and the mechanisms by which humans and machines learn and represent complex concepts.