Celeste Kidd

University of Rochester

Early Career Research Fellow
Assistant Professor of Brain and Cognitive Sciences and the Center for Visual Science
University of Rochester in Rochester, NY
United States of America
PhD, Brain and Cognitive Sciences, University of Rochester, 2013
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Research focus
Celeste Kidd studies learning during early cognitive development using a combination of computational and behavioral methods. The computational models act as formal theories of the relationship between various cognitive factors and learning, which she then tests using behavioral experimentation via gaze-contingent paradigms (a general term for techniques allowing a computer screen display to change in function depending on where the viewer is looking) and touchscreen applications. A major objective of her ongoing work is to understand how individual differences in cognitive mechanisms like memory and attention shape children’s curiosity and learning. By formalizing a theory of how cognitive systems relate to learning outcomes, she aims to develop and test educational interventions tailored to the specific cognitive abilities and needs of individual children.

My plans for the fellowship period
My work during the fellowship period will focus on understanding how individual differences in core cognitive capabilities impact learning, and how these differences can be leveraged in order to maximize learning within individual children. Specifically, our work will yield a more sophisticated understanding of how important cognitive factors (e.g., attentional capacity, encoding speed, executive control) relate to learning and educational practices. Our work will focus on three primary goals: (1) testing the precise linkage between these key variables across ages and individuals, (2) applying the analysis in increasingly realistic learning environments like those in schools and parent-child pedagogical interactions, and (3) using the relationships between these variables to design and test interventions to improve educational practice and tailor education to each individual’s cognitive characteristics. To achieve these goals, we will apply techniques to measure other cognitive abilities and learning outcomes across a large cross-section of ages, in both lab-based experiments and real-world educational situations (e.g., school). The results will be used to construct a quantitative description of how each cognitive component interfaces with educational achievement. This work will help us build the foundation we need to start applying these educational techniques to schools and educational programs.

How will my work change children’s and youth’s lives?
Our work will change children’s lives by creating better educational tools that leverage differences in children’s cognitive capacities in order to maximize their learning. Children approach learning with unique sets of cognitive capabilities (e.g., attention, working memory), each of which impacts learning outcomes. The dynamics between these cognitive capabilities and learning, however, are not yet well understood. Our research develops formal theories of these dynamics by building computational models that represent and simulate learning processes, which we then test and tweak to explain children’s learning patterns in the lab and real-world learning situations. These computational learning models may then be used for a wide-range of applied educational purposes, including more effective educational interventions, curricula, and apps. One of our key applied goals is building pedagogical systems that adjust the presentation of learning material to suit each learner’s individual cognitive capabilities and needs. These technologies will benefit all children, but will be especially valuable for young learners with poor educational access and learning disabilities. My lab also works to develop technology and software that are free and open-source in order to make reuse and modification easy and rapid, particularly in developing countries.

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