Interdisciplinary Advances in Statistical Learning 2017
We are pleased to announce the International Conference on Interdisciplinary Advances in Statistical Learning (#StatLearnBCBL), which will take place in Bilbao, Spain June 28-30, 2017.
The conference will discuss statistical learning and its underlying mechanisms from behaviour to neuroscience, in various domains such as language, music, vision, and audition, with data from adult participants, development, individual differences, computational modeling, and non-human species.
The conference will include invited speakers, regular talks, panel discussions, and poster sessions.
- Jenny Saffran, University of Wisconsin-Madison.
- Sharon Thompson-Schill, University of Pennsylvania
- Simon Kirby, University of Edinburgh
- Michael C. Frank, Stanford University
- Juan Toro, Pompeu Fabra University---Cross-species Comparisons & Evolution
- Linda Smith, Indiana University Bloomington---Development
- Chris Conway, Georgia State University--- Statistical Learning in Special Populations
- Dare Baldwin, University of Oregon---Vision, Action and Event Processing
BCBL - Basque Center on Cognition, Brain and Language.
IMPORTANT DATES TO REMEMBER
Abstract deadline: March 3rd, 2017.
Notification of abstract acceptance: March 20th, 2017.
Early registration deadline: April 10th, 2017.
Online registration deadline: May 14th, 2017.
Conference dates: June 28-30, 2017.
Special Issue Resulting from Previous SL conference (#StatLearnBCBL):
We are pleased to announce that early online access is now available for a special issue on New Frontiers for Statistical Learning in the Cognitive Sciences [B. C. Armstrong, R. Frost, & M. H. Christiansen, Eds.], which will appear in print in Philosophical Transactions of the Royal Society of London: Biological Sciences, in January.
Overview of the Issue:
Two decades ago, statistical learning (SL) was proposed as a powerful domain-general mechanism for processing a wide range of regularities. However, because of its rather narrow focus, SL research has largely failed to deliver on the wide-reaching promise of SL as a theoretical construct. This is mainly due to SL being investigated largely a separate ability, isolated from other aspects of cognition. This theme issue fosters a transition to studying statistical learning as an integral part of different cognitive systems, taking into consideration complementary perspectives from neurobiology, computation, development and evolutionary studies. This collection of work by international leaders from a range of disciplines shows that statistical learning is not simply learning to accurately represent the regularities of the environment. Rather it is a product of the complex interaction between environmental statistics, the neurocomputational principles of the cognitive systems in which learning takes place, and pre-existing biases due to previous experience and/or architectural constraints of the brain. This new perspective will enable statistical learning to impact a broad range of theories related to language, vision, audition, memory and social behaviour.
The full introduction to the special issue and a synopsis of the 13 articles in the theme issues is available at: http://rstb.royalsocietypublishing.org/content/372/1711/20160047
Table of Contents:
Anna C. Schapiro, Nicholas B. Turk-Browne, Matthew M. Botvinick, Kenneth A. Norman
Rebecca L. Gómez
Noam Siegelman, Louisa Bogaerts, Morten H. Christiansen, Ram Frost
Elizabeth M. Clerkin, Elizabeth Hart, James M. Rehg, Chen Yu, Linda B. Smith
Olga Fehér, Iva Ljubičić, Kenta Suzuki, Kazuo Okanoya, Ofer Tchernichovski
Renee E. Shimizu, Allan D. Wu, Jasmine K. Samra, Barbara J. Knowlton
Erik D. Thiessen
Denis Mareschal, Robert M. French
Gerry T. M. Altmann
Kenny Smith, Amy Perfors, Olga Fehér, Anna Samara, Kate Swoboda, Elizabeth Wonnacott