Selecting a subset of stimulus-response pairs with maximal transmitted information.

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Author
Sheehan, Michael J.
Date
1992-03Advisor
Theise, Eric S.
Poock, G.K.
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Show full item recordAbstract
System designers are often faced with the task of
assigning symbolic representations to user actions, e.g.,
icons to choices in graphical interfaces. When a confusion
matrix—on discriminability of the symbols—is available, it
is used to guide the selection of the set of symbols to be
implemented. While trial and error methods or clustering
approaches have been used to analyze this problem, it was only
recently that a true optimization approach was offered.
Theise (1989) formulated the symbol selection problem as a
zero-one integer programming problem whose objective function
was linked to the minimization of within-subset confusion.
Confusion is not the traditional metric used by human factors engineers to analyze confusion matrices. Rather,
transmitted-information—a metric from information theory—has
long been used to evaluate system performance. The purpose of
this thesis is to formulate a model of subset selection in
which transmitted information will be maximized.
It is possible to specify a correct model, although
current algorithms are incapable of solving it. This thesis
reports on the performance of a GAMS-based approximation to
the original model, as well as an exhaustive enumeration
scheme. Solutions from both information-theoretic approaches
are compared to solutions from the confusion/recognition
model.
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