Richard Prather is an Associate Professor of Human Development at the University of Maryland. His work addresses children’s cognitive development, with a focus on mathematical cognition. Richard holds an SB in Brain & Cognitive Sciences from MIT, and a PhD in Psychology from the University of Wisconsin.
My research program uses a mix of behavioral experimentation, neuroimaging, and
computational modeling to develop explanations of behavior and novel insights into the
relationship between behavior and neural activity. This approach allows me to
investigate questions in a manner that integrates neuroscience with cognitive and developmental
theory. My broad goal has been to use computational models to contribute to the understanding of
cognitive development and the connections between children’s context and behavior.
The overarching goal of my research is to improve children’s mathematics performance,
especially for children with poor math performance in classroom settings. The theoretical approach
that underlies my research is a complex-systems view of human cognition and the context in which
learning, and development occur. While cognitive processes can be examined in relative isolation,
they also occur within the broader school and home contexts influenced by internal factors, such as
math anxiety, motivation, socio-emotional skills, and environmental factors, such as air quality.
The long-term goal of my research program is the development of comprehensive
computational models of individual differences in mathematical learning. The models account for
both internal cognitive processes and external environmental factors (i.e., context).
My modeling approach combines elements critical for understanding success in mathematics
achievement ranging from socio-emotional factors like motivation to ecological, such as air
pollution. My approach focuses on individual differences and the range of factors that may
contribute to these differences.
The current phase of my research program includes the development and implementation of
a framework for characterizing human cognition. My prior work focused on the cognitive
mechanisms underlying individual differences in numerical cognition. I tended to focus on internal
neural and cognitive mechanisms. My current and future work expands that to multiple levels of
analysis of context, inspired by sociology, public health, mathematics education and other
disciplines. Context includes developmental, cultural, and societal experiences that may affect
human behavior. My contention is that cognition can not be fully understood by treating learners
as if they operate in a vacuum. Research studies must integrate internal cognitive mechanisms with
contextual factors, as all humans are embedded in developmental, cultural, and societal contexts
(see selected papers below). The difficult question is how to do this and still produce experimental
studies that can build on each other over time to facilitate scientific progress.