common ground seminar

Today I was surprised with an invitation to present at the UPenn Common Ground Seminar in Language and Communication Sciences.

Looking forward!


CUNY 2017

Here are my abstract and poster for CUNY 2017.

Our experiment was a first attempt to test a direct, causal relation between prediction and learning. We find that prediction itself doesn’t explain how children learn novel words, but 3-year-olds and 4-year-olds who predict and redirect attention toward the novel referent were more successful in learning. That being said, learning 12 novel words in just a few minutes was clearly difficult for children, as indicated by their low accuracy at test. In sum, further experiments are needed to evaluate the role of prediction in learning, and to clarify what other factors (e.g., cognitive control) are involved during learning.

It was a great time, as always, despite the snow!

Looking forward to CUNY 2018!

BUCLD 2016

This year, I gave a talk on nonverbal prediction at BUCLD.

Below are an abstract and slides for reference. I’m working on this paper with Lauren Emberson, Alexa Romberg, and Casey Lew-Williams, so stay tuned for the publication.

Prediction may be a language learning mechanism. This idea is supported by research showing that children with larger vocabularies (MCDI, PPVT) generate verbal predictions while processing language, and flexibly update predictions in light of new information. But do predictions support language learning, or vice-versa? In the present study, we assessed nonverbal prediction and vocabulary (MCDI) in 12-24-month-old infants (n=50). Infants with larger vocabularies efficiently updated nonverbal predictions in light of new information. This work reveals that links between prediction and language learning extend beyond the verbal domain, and are apparent even in infancy. Given the vast potential for making inaccurate predictions, the ability to continuously update predictions may be crucial for learning. In ongoing experiments, I am exploring the complex relations between language input, nonverbal prediction, verbal prediction, and language learning.

Here are slides for reference.