Team of developers
-Ari Biswas and
An experimental framework to allow scientist to directly query subjects' 2D
mental models of a collection of objects.
In our quest to understand how people behave and think and to somehow quantify
this understanding, we come upon the problem of modeling the relationships between
objects within a person's mind. For example, there are many aspects to how people
think about different fruits, but one popular comic has suggested a 2-dimensional
model in which one axis represents the difficulty of preparation and the other
represents tastiness (where bananas might end up at the top right, being both very
easy and very tasty, while durian might be at the opposite corner). It is an
interesting problem to try to experimentally learn such a model (with or without
prescribed axes) for any given person or population. We present Durian,
an experimental framework to allow a scientist to directly query subjects' 2D mental
models of a collection of objects using a variety of algorithms to perform the
queries and to generalise the model to further data.
Durian began as a class project for my advanced Machine Learning class
at the University Of Wisconsin. I plan on extending this work in various applications
of computational learning and psychology.