Flexible statistical learning: Online target detection, but not offline recognition reveals adaptation to changing regularities

The ability to discern the statistical regularities in our environments has been shown to support key cognitive functions, including attention, prediction and language learning. While most research has focused on stable regularities, real-world patterns often change over time, requiring flexible updating of internal representations. In the context of embedded pattern learning, where continuous input consists of hidden pairs or triplets, prior work showed that learning of an initial structure can hinder learning of an updated one. Unlike most previous studies, which relied on post-exposure (offline) learning measures, the current study also incorporated an online target detection during exposure, to gauge real-time learning and adaptation to novel patterns more directly. In three separate blocks, participants were exposed to a stream of embedded pairs that were reshuffled into new pairs halfway through the stream. We administered the same task in both the visual and auditory modality, allowing us to explore modality-specific differences. The online target detection measure revealed that participants learned both the initial and updated regularities, but with an advantage for learning the former. In contrast, our offline measure only evidenced recognition of the initial patterns, echoing previously reported primacy effects. These learning effects were only present in the auditory modality, with no evidence of visual statistical learning. Our results corroborate earlier findings and underscore the importance of online measures in capturing flexible learning that is not captured by offline measures.