Our experience in the world is very much like a continuous recognition experiment: We
have a continuous stream of experiences, some new and some old. There is one big
difference with a typical recognition experiment: there is not an experimenter controlling
the order of these experiences. Lael Schooler and colleagues have shown that the
statistical structure of these real world experiences predict performance in controlled
memory experiments. Mining two large data bases of human experience (Twitter
messages and Reddit comments) and using a prescription for optimal retrieval (the
Reciprocal Square-Root Law), we developed an algorithm that can make a-priori
predictions for speed of recognition in any continuous recognition experiment. We
discuss its performance in experiments that either have controlled experimental
statistics or real-world statistics or a mixture of the two.
This is an in-person presentation on July 27, 2025
(13:40 ~
14:40 EDT).
