Efficient uniform sampling explains non-uniform memory of narrative stories

Humans do not remember all experiences uniformly. We remember certain moments better than others, and central gist better than detail. Current theories focus exclusively on surprise to explain why some moments are better remembered, and do not explain gist memory. We propose that humans uniformly sample incoming information in time, which explains both non-uniform memory and gist. Rather than surprise, this model predicts that the mutual information between a given moment and the rest of the experience drives memory. To test this model, participants listened to narrative stories and recalled them immediately afterward. Using large language models to quantify the information structure of narrative stories and participants' recall, we found that our parsimonious uniform sampling model explained memory better than earlier theories. These findings suggest an alternative, simpler account of human memory that does not rely on costly feedback mechanisms for prioritizing encoding of specific information.