BEGIN:VCALENDAR
VERSION:2.0
PRODID:ILLC Website
BEGIN:VEVENT
UID:/NewsandEvents/Events/Upcoming-Events/newsitem
/7234/8-April-2016-ACLC-Seminar-Naomi-Feldman
DTSTAMP:20160317T000000
SUMMARY:ACLC Seminar, Naomi Feldman
ATTENDEE;ROLE=Speaker:Naomi Feldman
DTSTART:20160408T151500
DTEND:20160408T163000
LOCATION:Rooim 4.04, PC Hoofthuis, Spuistraat 134,
Amsterdam
DESCRIPTION:Children have impressive statistical l
earning abilities. In phonetic category acquisitio
n, for example, they are sensitive to the distribu
tional properties of sounds in their input. Howeve
r, knowing that children have statistical learning
abilities is only a small part of understanding h
ow they make use of their input during language ac
quisition. This work uses Bayesian models to exami
ne three basic assumptions that go into statistica
l learning theories: the structure of learners' hy
pothesis space, the way in which input data are sa
mpled, and the features of the input that learners
attend to. Simulations show that although a naive
view of statistical learning may not support robu
st phonetic category acquisition, there are severa
l ways in which learners can potentially benefit b
y leveraging the rich statistical structure of the
ir input. For more information, see http://aclc.u
va.nl/news-and-events/events/aclc-smart-seminar/al
l-events/ or http://ling.umd.edu/~nhf/
X-ALT-DESC;FMTTYPE=text/html:\n Children
have impressive statistical learning abilities. I
n phonetic category acquisition, for example, they
are sensitive to the distributional properties of
sounds in their input. However, knowing that chil
dren have statistical learning abilities is only a
small part of understanding how they make use of
their input during language acquisition. This work
uses Bayesian models to examine three basic assum
ptions that go into statistical learning theories:
the structure of learners' hypothesis space, the
way in which input data are sampled, and the featu
res of the input that learners attend to. Simulati
ons show that although a naive view of statistical
learning may not support robust phonetic category
acquisition, there are several ways in which lear
ners can potentially benefit by leveraging the ric
h statistical structure of their input.

\n \
n For more information, see
http://aclc.uva.nl/news-and-events/events/aclc-sma
rt-seminar/all-events/ or http://ling.umd.ed
u/~nhf/

\n
URL:/NewsandEvents/Events/Upcoming-Events/newsitem
/7234/8-April-2016-ACLC-Seminar-Naomi-Feldman
END:VEVENT
END:VCALENDAR