  Lifelong experiences and learned knowledge lead to shared expectations about
how common situations tend to unfold. Such knowledge of narrative event flow
enables people to weave together a story. However, comparable computational
tools to evaluate the flow of events in narratives are limited. We quantify the
differences between autobiographical and imagined stories by introducing
sequentiality, a measure of narrative flow of events, drawing probabilistic
inferences from a cutting-edge large language model (GPT-3). Sequentiality
captures the flow of a narrative by comparing the probability of a sentence
with and without its preceding story context. We applied our measure to study
thousands of diary-like stories, collected from crowdworkers about either a
recent remembered experience or an imagined story on the same topic. The
results show that imagined stories have higher sequentiality than
autobiographical stories and that the sequentiality of autobiographical stories
increases when the memories are retold several months later. In pursuit of
deeper understandings of how sequentiality measures the flow of narratives, we
explore proportions of major and minor events in story sentences, as annotated
by crowdworkers. We find that lower sequentiality is associated with higher
proportions of major events. The methods and results highlight opportunities to
use cutting-edge computational analyses, such as sequentiality, on large
corpora of matched imagined and autobiographical stories to investigate the
influences of memory and reasoning on language generation processes.
