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Summary
Quantifers in English, like 'few', 'some' and 'many'
give information about the numbers of objects being talked about. Yet the
actual number of objects in a scene to be described is not a perfect
predictor of the use of these terms. In this grant we showed that people's
quick estimates of the number of objects in a visual scene is a better
predictor of quantifier appropriateness than the actual number. In a series
of experiments we asked people either to estimate the number of objects in
a visual scene (when presented for a short time), or to rate the
appropropiateness of quantifiers (e.g., 'There are many fish') to describe
the same scenes. We found a correspondence between the number estimates and
quantifier ratings: variables such as the spacing between objects, how they
are grouped together, and how many other objects are present all affect
both number judgements and quantifier ratings. We then used this data to
build a computational model of quantifier use grounding in visual knowledge
of the scenes being described. The model takes as input a visual scene, and
gives a quantifier description as output. Using the model, we were able to
show that learning how to use quantifirs is faster if the model has
knowledge about number estimation first, and learning number judgements is
faster if the model has been training to use quantifiers first. These
results show that language and perception are intricately connected, and
that decoupling them adversley affects learning.
People
Kenny Coventry
(Principal investigator; now at University of Northumbria)
Angelo Cangelosi
(Co-investigator)
Steve Newstead
(Co-investigator)
Alison Bacon
Rohana Rajapakse
Davi Bugmann
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