C. Shawn Green
Learning and Transfer Lab
My research interests are in human learning – in particular in the perceptual, motor, and cognitive domains.
Specific topics of interest can be broken down into three broad themes:
– Transfer of Learning: Given appropriate training conditions (e.g. sufficient time on task, proper spacing of training sessions, feedback, etc) humans will tend to improve on virtually any perceptual and/or cognitive task. One critical question though is under what training conditions do you only get better at the trained task (for example, if you do a lot of Sudoku, you may really only get better at Sudoku – not other types of reasoning tasks) and under what conditions do you see generalization from the trained task to a new task (for example, if you learn to drive in a car, you’ll likely be better at driving a pickup truck than you would have been without any driving experience at all)?
– Rate of Learning: There are large inter-individual differences in the rate with which new perceptual, motor, and cognitive tasks are learned as well as large differences in the rates of learning produced by various training paradigms. The key questions are thus: 1) what traits/characteristics – either of the individual or the task – will allow us to best predict the rate with which an individual will learn a new task and 2) how can we manipulate either the individual or the task to alter the rate of learning (typically to increase the rate of learning)?
– Depth of Learning: In laboratory studies of learning, participants are usually given a specific task to practice for a particular period of time. They have no choice in what task to practice or for how long they will practice. In the real-world though, these are both decisions that have to be made by a learner. Is it worth investing my time and resources in an attempt to improve performance on this task in the first place? If I have started practicing, is it worth continuing? Making such decisions rationally requires that the participant estimate not only their current level of skill and the value of performing the task given that level of skill, but also to estimate the rate with which their skills are likely to improve with practice time and the value of performing the task given their potentially improved performance.
Green, C.S., Strobach, T., Schubert, T. (2014). On methodological standards in training and transfer experiments. Psychological Research. Epub ahead of print.
Green, C.S. & Bavelier, D. (2012). Learning, attentional control, and action video games. Current Biology, 22, R197-R026.
Bavelier, D., Green, C.S., Pouget, A., & Schrater, P. (2012). Brain Plasticity Through the Life Span: Learning to Learn and Action Video Games. Annual Review of Neuroscience, 35, 391-412.
Green, C.S., Benson, C., Kersten, D. & Schrater, P.(2010). Alterations in choice behavior by manipulations of world-model. PNAS. 107, 16401-16406.
Green, C.S., Pouget, A., & Bavelier, D. (2010). Improved probabilistic inference as a general mechanism for learning with action video games. Current Biology, 23, 1573-1579.
Dye, M.W.G., Green, C.S., & Bavelier, D. (2009). Increasing speed of processing with action video games. Current Directions in Psychological Science, 18, 321-326.
Green, C.S. & Bavelier, D. (2007). Action video game experience alters the spatial resolution of attention. Psychological Science, 18(1), 88-94.
Green, C.S. & Bavelier, D. (2003). Action video game modifies visual selective attention. Nature, 423, 534 –538.