The transformation of sensory inputs into complex memory representations is fundamental to human experience; yet, little is known about how this crucial process is achieved. When you meet your friend at the new cafe in town, what part of the brain encodes this novel scene into long term memory? What part encoded your friend’s favorite t-shirt, so that the sight of it gives you a feeling of familiarity rather than surprise? It is well-established that the medial temporal lobe (MTL) is crucial to both processes, but the MTL is not a single homogeneous region. In fact, it is composed of several anatomically distinct subregions including hippocampus, perirhinal cortex (PRC) and parahippocampal cortex (PHC). However, the computations performed by each subregion to encode individual events is still unclear. The present research tests the central hypothesis that different forms of event content are transformed into memory by distinct subregions within the MTL. A critical barrier in the study of content representation thus far has been its focus on comparing univariate peak activations in a region to different stimulus materials. To go beyond this limited approach, we employed multivariate statistical analyses that takes into account how event content is represented by distributed activity in MTL subregions. First, we examine the content-specific contributions of MTL subregions to episodic encoding and retrieval. Then, we demonstrate how these distributed representations support memory-based prediction to resolve ambiguities in our environment.Neuroscienc
