Areas of Research: Cognitive neuroscience of learning and memory
In the Norman lab, we use biologically realistic neural network models to explore how the brain gives rise to learning and memory phenomena, and we test these models’ predictions using several different methods, ranging from studies of memory performance in college students, to studies of brain-damaged patients with memory disorders, to neuroimaging studies that record brain activity as people learn and remember.
Currently, students in the lab are using computational models to address questions like: What are the “learning rules” that govern strengthening and weakening of memories in the brain? How do brain oscillations contribute to learning? How does sleep contribute to learning? How can we intentionally forget memories? What are the optimal “search strategies” to use when trying to retrieve memories? We are presently running experiments to test the predictions of our models regarding how memories can be weakened and strengthened, and regarding how subjects strategically cue memory and make decisions on different kinds of memory tests.
With regard to neuroimaging, we are developing (along with other Princeton researchers) new techniques for analyzing distributed patterns of neural activity. Specifically, we are using sophisticated classification (“data mining”) algorithms, applied to fMRI and EEG data, to isolate the neural signatures of specific thoughts and memories. Our goal is to use these new analysis tools to track how mental representations come and go and change over the course of an experiment. For example, these new tools make it possible to determine how subjects are cuing memory (i.e., what information are they using to try to elicit recall) at a particular moment based on brain activity.
- Kim, G., Lewis-Peacock, J. A., Norman, K. A., & Turk-Browne, N. B. (2014). Pruning of memories due to context-based prediction error. PNAS. PDF
- Poppenk, J, & Norman, K. A. (2014). Briefly cueing memories leads to suppression of their neural representations. Journal of Neuroscience. PDF
- Manning, J. R., Ranganath, R., Norman, K. A., & Blei, D. M. (2014). Topographic factor analysis: a Bayesian model for inferring brain networks from neural data. PLoS ONE. PDF
- Detre, G. J., Natarajan, A., Gershman, S. J., & Norman, K. A. (2013). Moderate levels of activation lead to forgetting in the think/no-think paradigm. Neuropsychologia. PDF
- Sederberg, P. B., Gershman, S. J., Polyn, S. M., & Norman, K. A. (2011). Human memory reconsolidation can be explained using the Temporal Context Model. Psychonomic Bulletin & Review, 18, 455-468. PDF
- Gershman, S. J., Jones, C. E., Norman, K. A., Monfils, M.-H., & Niv, Y. (2013). Gradual extinction prevents the return of fear: implications for the discovery of state. Frontiers in Behavioral Neuroscience. PDF
- Gershman, S. J., Schapiro, A. C., Hupbach A., Norman, K. A. (2013). Neural context reinstatement predicts memory misattribution. Journal of Neuroscience. PDF
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