My developmental cognitive neuroscience research studies how attention and learning interact from infancy to aging adulthood when finding and learning relevant information. My guiding hypothesis is that infants are more input-driven and adults are more knowledge-driven in the way they take in information. Input-driven learning is based on salience and frequency-of-occurrence with a weak distinction between relevant and irrelevant. By contrast, the knowledge-driven approach relies on processing only relevant or necessary events as defined by previous experience. Using neural (EEG) and behavioral (eye-tracking, accuracy/reaction time) responses in visual search and statistical learning paradigms, my studies show that infants and adults differ in their approaches to finding and learning about target objects. My research program has two components: 1) measuring adults’ use of previously acquired knowledge and tracking the development of this ability from infancy, and 2) applying infant and child learning strategies to mitigate cognitive decline during aging. Using infant learning to inform adult learning and vice versa has the greatest promise to lead to discoveries about optimal learning strategies that can be applied throughout the lifespan.
Wu, R., Rebok, G. W., & Lin, F. V. (under review). A novel theoretical life course framework for triggering cognitive development across the lifespan.
Wu, R., Nako, R., Band, J., Pizzuto, J., Shadravan, Y., Scerif, G., & Aslin, R. N. (2015). Rapid selection of non-native stimuli despite perceptual narrowing. Journal of Cognitive Neuroscience, 27(11), 2299-2307.
Wu, R. (2013). Learning from learners. The Psychologist, 26(2), 550-551.
Wu, R., Gopnik, A., Richardson, D. C., & Kirkham, N. Z. (2011). Infants learn about objects from statistics and people. Developmental Psychology, 47(5), 1220-1229.