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Statistical prediction of the future impairs episodic encoding of the present

Significance We use memory to recall the past and make predictions about the future. However, these functions can conflict: Recalling a past experience involves returning to a specific moment in time, but this exact experience will never occur again because the world and observer have changed. Rather, it is often adaptive to focus on aspects of the world that repeat across experiences and are likely to generalize. Encoding individual experiences (episodic memory) and extracting regularities across experiences (statistical learning) require fundamentally different kinds of learning, yet both have been linked to the same brain region, the hippocampus. Here we reveal the consequence of this codependence: Predicting what comes next from statistical learning prevents encoding of the current experience into episodic memory. Memory is typically thought of as enabling reminiscence about past experiences. However, memory also informs and guides processing of future experiences. These two functions of memory are often at odds: Remembering specific experiences from the past requires storing idiosyncratic properties that define particular moments in space and time, but by definition such properties will not be shared with similar situations in the future and thus may not be applicable to future situations. We discovered that, when faced with this conflict, the brain prioritizes prediction over encoding. Behavioral tests of recognition and source recall showed that items allowing for prediction of what will appear next based on learned regularities were less likely to be encoded into memory. Brain imaging revealed that the hippocampus was responsible for this interference between statistical learning and episodic memory. The more that the hippocampus predicted the category of an upcoming item, the worse the current item was encoded. This competition may serve an adaptive purpose, focusing encoding on experiences for which we do not yet have a predictive model.