Computational learning phenotypes are not related to individual differences in resting-state fMRI connectivity.

People learn from experience, but with considerable individual differences in the degree and type of behavioral adjustments resulting from a given experience. Error driven learning rules provide an elegant framework for explaining both learning behavior and its neural signatures; however, implementing them requires carving the world into so-called "latent states", that serve as substrates for learning, meaning that the same learning algorithm can produce different sorts of learning given different state representations. Recent theoretical and behavioral work hints that individual differences in learning may reflect differences in how individuals carve their environment into states, with some individuals combining multiple temporal contexts into a single state and others separating these contexts into individuated latent states. Here, we develop a behavioral paradigm and modeling framework to test this idea directly and show in a large cohort of human participants that individuals can be classified into groups according to whether and how they carve temporal contexts into latent states. These behavioral phenotypes impact continual learning, specifically the degree to which individuals avoid interference at context changes or are able to reuse information when encountering a familiar context. We tested whether these behavioral phenotypes related to individual differences in underlying brain connectivity, as measured by resting state-fMRI, but found no evidence for such a relationship. Taken together, this work suggests that learning differences across individuals are attributable to differences in underlying state representations that are not predicted by underlying resting state brain connectivity.