Statistical word learning: Behaviors, mechanisms and models
Recent theory and experiments offer a new solution as to how human learners may break into word learning, by using cross-situational statistics to find the underlying word-referent mappings. Computational models demonstrate the in-principle plausibility of this statistical learning solution and experimental evidence shows that both adults and infants can aggregate and make statistically appropriate decisions from word-referent co-occurrence data. In this talk, I will first review these empirical and modeling contributions to investigate cognitive processes in statistical word learning, focusing on a debate between associative learning and hypothesis testing. Next, I will present a set of studies using head-mounted cameras and eye trackers to collect and analyze toddlers’ visual input as parents label novel objects during an object-play session. The results show how toddlers and parents coordinate momentary visual attention when exploring novel objects in a free-flowing task, and how toddlers accumulate co-occurring statistics of seen objects and heard words through free play. I will conclude by suggesting that future research should focus on detailing the statistics in the learning environment and the cognitive processes that make use of those statistics.
