Date of Award
Master of Science (MS)
At the heart of statistical learning lies the concept of uncertainty.
Similarly, embodied agents such as robots
and animals must likewise address uncertainty, as sensation
is always only a partial reflection of reality. This
thesis addresses the role that uncertainty can play in
a central building block of intelligence: categorization.
Cognitive agents are able to perform tasks like categorical perception
through physical interaction (active categorical perception; ACP),
or passively at a distance (distal categorical perception; DCP).
It is possible that the former scaffolds the learning of
the latter. However, it is unclear whether DCP indeed scaffolds
ACP in humans and animals, nor how a robot could be trained
to likewise learn DCP from ACP. Here we demonstrate a method
for doing so which involves uncertainty: robots perform
ACP when uncertain and DCP when certain.
Furthermore, we demonstrate that robots trained
in such a manner are more competent at categorizing novel
objects than robots trained to categorize in other ways.
This suggests that such a mechanism would also be
useful for humans and animals, suggesting that they
may be employing some version of this mechanism.
Number of Pages
Powell, Nathaniel V., "The role of Uncertainty in Categorical Perception Utilizing Statistical Learning in Robots" (2016). Graduate College Dissertations and Theses. 581.