TY - JOUR
T1 - What exactly is learned in visual statistical learning? Insights from Bayesian modeling
AU - Siegelman, Noam
AU - Bogaerts, Louisa
AU - Armstrong, Blair C.
AU - Frost, Ram
PY - 2019/11/1
Y1 - 2019/11/1
N2 - It is well documented that humans can extract patterns from continuous input through Statistical Learning (SL) mechanisms. The exact computations underlying this ability, however, remain unclear. One outstanding controversy is whether learners extract global clusters from the continuous input, or whether they are tuned to local co-occurrences of pairs of elements. Here we adopt a novel framework to address this issue, applying a generative latent-mixture Bayesian model to data tracking SL as it unfolds online using a self-paced learning paradigm. This framework not only speaks to whether SL proceeds through computations of global patterns versus local co-occurrences, but also reveals the extent to which specific individuals employ these computations. Our results provide evidence for inter-individual mixture, with different reliance on the two types of computations across individuals. We discuss the implications of these findings for understanding the nature of SL and individual-differences in this ability.
AB - It is well documented that humans can extract patterns from continuous input through Statistical Learning (SL) mechanisms. The exact computations underlying this ability, however, remain unclear. One outstanding controversy is whether learners extract global clusters from the continuous input, or whether they are tuned to local co-occurrences of pairs of elements. Here we adopt a novel framework to address this issue, applying a generative latent-mixture Bayesian model to data tracking SL as it unfolds online using a self-paced learning paradigm. This framework not only speaks to whether SL proceeds through computations of global patterns versus local co-occurrences, but also reveals the extent to which specific individuals employ these computations. Our results provide evidence for inter-individual mixture, with different reliance on the two types of computations across individuals. We discuss the implications of these findings for understanding the nature of SL and individual-differences in this ability.
KW - Bayesian modeling
KW - Individual differences
KW - Online measures
KW - Statistical learning
UR - http://www.scopus.com/inward/record.url?scp=85067391290&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067391290&partnerID=8YFLogxK
U2 - 10.1016/j.cognition.2019.06.014
DO - 10.1016/j.cognition.2019.06.014
M3 - Article
C2 - 31228679
AN - SCOPUS:85067391290
VL - 192
JO - Cognition
JF - Cognition
SN - 0010-0277
M1 - 104002
ER -