TY - JOUR
T1 - General cognitive principles for learning structure in time and space
AU - Goldstein, Michael H.
AU - Waterfall, Heidi R.
AU - Lotem, Arnon
AU - Halpern, Joseph Y.
AU - Schwade, Jennifer A.
AU - Onnis, Luca
AU - Edelman, Shimon
N1 - Funding Information:
MHG and JAS were supported by NSF BCS 0844015 and NICHD R03 HD061524-01. JYH was supported in part by NSF grants ITR-0325453, IIS-0534064, IIS-0812045 and IIS-0911036, by AFOSR grants FA9550-08-1-0438 and FA9550-09-1-0266, and ARO grant W911NF-09-1-0281. SE was supported in part by World Class University program at Korea University, funded by the National Research Foundation of Korea through the Ministry of Education, Science and Technology grant R31-2008-000-10008-0.
PY - 2010/6
Y1 - 2010/6
N2 - How are hierarchically structured sequences of objects, events or actions learned from experience and represented in the brain? When several streams of regularities present themselves, which will be learned and which ignored? Can statistical regularities take effect on their own, or are additional factors such as behavioral outcomes expected to influence statistical learning? Answers to these questions are starting to emerge through a convergence of findings from naturalistic observations, behavioral experiments, neurobiological studies, and computational analyses and simulations. We propose that a small set of principles are at work in every situation that involves learning of structure from patterns of experience and outline a general framework that accounts for such learning.
AB - How are hierarchically structured sequences of objects, events or actions learned from experience and represented in the brain? When several streams of regularities present themselves, which will be learned and which ignored? Can statistical regularities take effect on their own, or are additional factors such as behavioral outcomes expected to influence statistical learning? Answers to these questions are starting to emerge through a convergence of findings from naturalistic observations, behavioral experiments, neurobiological studies, and computational analyses and simulations. We propose that a small set of principles are at work in every situation that involves learning of structure from patterns of experience and outline a general framework that accounts for such learning.
UR - http://www.scopus.com/inward/record.url?scp=77952958685&partnerID=8YFLogxK
U2 - 10.1016/j.tics.2010.02.004
DO - 10.1016/j.tics.2010.02.004
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AN - SCOPUS:77952958685
SN - 1364-6613
VL - 14
SP - 249
EP - 258
JO - Trends in Cognitive Sciences
JF - Trends in Cognitive Sciences
IS - 6
ER -