Christiansen, M. H.
1 Cornell University
2 University of Southern Denmark
3 Haskins Laboratories
Our world is full of patterns. Being able to encode and use such patterns to inform behavior is important for many aspects of perception and action. A growing amount of research has pointed to statistical learning--the discovery of distributional properties in the input--as a key mechanism for detecting patterns in the environment. In this talk, I discuss the nature of the mechanisms underlying statistical learning. I will argue that statistical learning is not supported by a unitary mechanism but instead involves separate neural networks working in different modalities, each relying on a set of domain-general computational principles. This provides a possible explanation for why some studies have observed similar sensitivity to statistical patterns across different domains, while others have revealed substantial modality effects on statistical learning. To further elucidate the intricate relationships between statistical learning and other aspects of cognition, cognitive scientists need to adopt an individual differences approach that compare sensitivity to statistical patterns across different domains. I conclude by highlighting the importance of statistically-based chunking as a key component of language and cognition, linking statistical learning more closely to basic work on learning and memory.