Image credit: Public domain (CC0)
The human brain can record snapshots of details from specific events – such as where and when the event took place – and retrieve this information later. Recalling these ‘episodic memories’ can help us gain a better understanding of our current surroundings and predict what will happen next.
Studies of episodic memory have typically involved observing volunteers while they perform simple, well-defined tasks, such as learning and recalling lists of random pairs of words. However, it is less clear how episodic memory works ‘in the wild’ when no one is quizzing us, and we are going about everyday activities.
Recently, researchers have started to study memory in more naturalistic situations, for example, while volunteers watch a movie. Here, Lu et al. have built a computational model that can predict when our brains store and retrieve episodic memories during these experiments.
The team gave the model a sequence of inputs corresponding to different stages of an event, and asked it to predict what was coming next. Intuitively, one might think that the best use of episodic memory would be to store and retrieve snapshots as frequently as possible. However, Lu et al. found that the model performed best when it was more selective – that is, preferentially storing episodic memories at the end of events and waiting to recover them until there was a gap in the model’s understanding of the current situation. This strategy may help the brain to avoid retrieving irrelevant memories that might (in turn) result in the brain making incorrect predictions with negative outcomes.
This model makes it possible for researchers to predict when the brain may store and retrieve episodic memories in a particular experiment. Lu et al. have openly shared the code for the model so that other researchers will be able to use it in their studies to understand how the brain uses episodic memory in everyday situations.
