The natural world is rich with patterns, and organisms learn these patterns through passive exposure. This presents a powerful and flexible means of learning about the world that does not involve explicit instruction that appears to play an important role in spoken language learning. However, not all patterns can be learned by passive exposure alone. This research project investigates how learning across patterns of experience proceeds when passive exposure is insufficient to drive learning and yet there is no explicit instruction. The prior work that this project builds on suggests that real-world statistical learning may capitalize on input regularities’ global temporal alignment with behaviorally-relevant actions and events to hasten learning. Learning across statistical regularities can be incidental, and not overtly driven by an intention to learn, while still taking place in the context of an active task that generates valuable predictions and rewarding outcomes. This perspective may be transformative in how we think about human learning of statistically-structured input in complex, naturalistic environments. Findings from this research will inform the design of learning interventions that capitalize on these learning principles to be useful for diverse communities of learners.
The proposed research will advance a new research approach, empirical tests of mechanistic predictions, and complementary information from behavior, electrophysiology and functional magnetic resonance imaging to understand statistical learning under more natural circumstances involving interplay among active behavior, multimodal input, selective attention, and statistical input regularities. It pursues the twin hypotheses that (1) active engagement in a rich, environment can support statistical learning by virtue of loose temporal alignment of statistically-structured input with behaviorally-relevant actions, objects, and events and (2) that this incidental statistical learning drives the emergence of selective attention to behaviorally relevant regularities, creating a virtuous cycle that promotes later learning. The team will also conduct several outreach activities including collecting data from non-university samples via a “data-truck†and providing science of learning outreach to high school students.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
