Category: Conference (Page 2 of 2)

Just uploaded Linda Smith’s keynote to YouTube. Excellent lecture on infant word learning; many thanks to Linda for joining us in Lancaster! The keynote was delivered on June 24, 2016, as part of the Fifth Implicit Learning Seminar. Please see below for abstract.

Linda Smith (Indiana University): Infant statistical word-referent learning at scale

Infants break into word learning by mapping heard names to scenes. The unsolved problem is how infants determine the referents for unknown words from the inherently ambiguous information within a single scene. Proposed solutions to this problem have focused on how individuals learn from single events, from a series of events over short temporal scales (4 to 10 minutes) or from random selections of naming events in the wild. None of this may be relevant to word-learning at scale. Evidence on the frequency distributions of objects and words in infant environments suggests a new conceptualization of the learning problem, and new forms of statistical learning.

Just uploaded Morten Christiansen’s excellent keynote, “Implicit-Statistical Learning: A Tale of Two Literatures”, Lancaster, June 23, 2016. Thanks again to Morten for his great keynote and to our TV Unit for producing the recording! Click here to see the keynote. Please see below for the abstract.

Morten Christiansen (Cornell University, Aarhus University, and Haskins Laboratories): Implicit-statistical learning: A tale of two literatures

Although implicit learning and statistical learning both can be construed as uncovering the structure of the input from its distributional properties, the relevant research has largely been published in separate literatures and with surprisingly little cross-polination between them. This has resulted in apparently opposing perspectives on the computations involved in learning, pitting chunk-based learning against probabilistic learning. In this paper, I argue for a unification of the two approaches to learning under the heading of “implicit statistical learning” and trace its long historical pedigree going back nearly a century. Building on basic insights from the memory literature, I will sketch a framework for statistically-based chunking that may provide a unified computational basis for understanding implicit statistical learning.