What constrains distributional learning in adults?

One of the many remarkable features of human language is it's flexibility: during acquisition, any normally-developing human infant can acquire any human language, and during adulthood, language users quickly and flexibly adapt to a wide range of talker variation. Both language acquisition in infants and adaptation in adults have been hypothesized to be forms of distributional learning, where flexibility is driven by sensitivity to statistical properties of sensory stimuli and the corresponding underlying linguistic structures. Despite the similarities between these forms of linguistic flexibility, there are obvious differences as well, chief among them being that adults have a much harder time acquiring the same unfamiliar languages that they would have picked up naturally during infancy. This suggests that there are strong constraints on distributional learning during adulthood. This paper provides further, direct evidence for these constraints, by showing that American English listeners struggle to learn voice-onset time (VOT) distributions that are atypical of American English. Moreover, computational modeling shows that the pattern of distributional learning (or lack thereof) across different VOT distributions is consistent with Bayesian belief-updating, starting from prior beliefs that are very similar to the VOT distributions produced by a typical talker of American English. Together, this suggests that distributional learning in adults is constrained by prior experience with other talkers, and that distributional learning may be a computational principle of human language that operates throughout the lifespan.