A machine learning approach to predict episodic memory formation

Abstract

Episodic memories constitute the essence of our recollections and are formed by autobiographical experiences and contextual knowledge. Memories are rich and detailed, yet at the same time they can be malleable and inaccurate. The contents that end up being remembered are the result of filtering incoming sensory inputs in the context of previous knowledge. Here we asked whether the quintessentially subjective process of memory construction could be predicted by a supervised machine learning approach based exclusively on content information. We considered audiovisual segments from a movie as a proxy for real-life memory formation and built a quantitative model to explain psychophysics data evaluating recognition memory. The inputs to the model included audiovisual information (e.g. presence of specific characters, objects, voices and sounds), scene information (e.g. location, presence or absence of action) and emotional valence information. The machine-learning model could predict memory formation in single trials both for group averages and individual subjects with an accuracy of up to 80% using solely stimulus content properties. These results provide a quantitative and predictive model that links sensory perception and emotional attributes to memory formation. Furthermore, the results demonstrate that a computational model can make sophisticated inferences about a cognitive process that involves selective filtering and subjective interpretation.