A key feature of human learning is use of the past to predict what is likely in the future. For example, people rely on past experience to predict that thunder is often followed by a downpour. But there are different ways to learn how events are related. For example, some events co-occur (thunder and rain), while others are causally related (cloud cover and rain). People can also learn long-range relationships—for example, that rain will lead to more flowers, which leads to more insects, even though rain may not directly affect insects population. This project will investigate how different brain areas support these different kinds of learning abilities, and will fill important gaps in understanding exactly how the brain changes in response to experience. As such, the proposed research will significantly advance our understanding of how the brain supports learning and memory. Because predictive and causal relationships are at the heart of many high-level cognitive activities including reasoning, language, decision making, the proposed research will have a broad scientific impact. More broadly, the findings may have important implications for education by elucidating how we learn, and for artificial intelligence, in which causal reasoning is a major frontier. Additional impacts on education and society will also be enabled through our outreach activities.
A set of neural areas, important for memory, show learning-related changes that represent predictive relations. However, it is unknown how predictive memories form, or what they reflect about observed experience. This project will investigate which “core predictive memory†areas support learning of causal or non-causal predictive relations. Experimental aims will focus on three major principles of causal learning: sensitivity to confounds, temporal specificity, and representation of structure. Computational models will make specific predictions about how these areas should respond to evidence if they learn according to classic theories from theoretical neuroscience. A well-established fMRI measure of relational strength will then test these model predictions in core memory areas. The first aim will test whether core memory areas are sensitive to confounds, or reflect simple co-occurrence. The second aim will test whether core memory areas are temporally specific. This will resolve whether and which areas learn temporally precise, causal relations as opposed to cumulative predictive relations better suited for planning. The third aim will test whether core memory areas represent explicit structure. Overall, these findings will sharpen and deepen understanding of how predictive memory areas work together to support higher order cognition.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
