We use computational models to explore how the brain gives rise to learning and memory phenomena, and then we test the predictions of these models using neuroimaging studies where we decode people’s thoughts as they learn and remember. With other Princeton researchers, we are also developing new machine learning methods for analyzing distributed patterns of neural activity. We use these new analysis tools to track how thoughts and memories change over time.
Learn more about our research
Lingering representations of stimuli influence recall organization
Shared experience, shared memory: a common structure for brain activity during naturalistic recall
Discovering event structure in continuous narrative perception and memory
Neural pattern change during encoding of a narrative predicts retrospective duration estimates
A probability distribution over latent causes in the orbitofrontal cortex
{"dots":"false","arrows":"true","autoplay":"false","autoplay_interval":"5000","speed":"1000","design":"prodesign-20","rtl":"false","loop":"true","slidestoshow":"3","slidestoscroll":"1","centermode":"false"}
