ACM
Transactions on
Reconfigurable
Technology and
Systems
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This quarterly publication is a peer-reviewed and archival journal that
covers reconfigurable technology,
systems, and applications on reconfigurable computers. Topics include
all levels of reconfigurable system
abstractions and all aspects of reconfigurable technology including platforms, programming environments
and application successes.
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March + April 2009
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at the University of Southern
California and the California
Institute of Technology [ 9].
The model applies a series of
feature-specific filters (color,
intensity, and orientation) that
emulate the processing that
occurs in the retina and brain
as a user views an image. For
a given input image, the model
produces a corresponding
salience map (Figure 1) that
quantitatively describes which
regions of the image are most
likely to draw the user’s gaze—
in other words, which regions
“pop out” the most.
Our goal is to apply this
model to investigate the relationship between the visual
properties of an interface
and an operator’s “situational
awareness.” Situational awareness is the ability to perceive
and understand a changing
environment and predict probable future events. Memory
facilitates situational awareness
by enabling a user to maintain
a continuously updated picture
of his or her environment (
playing back past events). A military
commander, for example, needs
situational awareness to keep
track of assets and adversaries within the battle space
over time. In an ongoing set
of experiments, we are studying how visual salience affects
memory. In particular, we are
testing participants’ ability to
remember the location of icons
on a 2-D map and examining
whether greater icon salience
correlates with lower spatial
recall error. The broader scientific aim of our research is
to examine how attention and
memory subsystems interact
within the brain. However,
studies such as these, as well as
research by other groups [ 10],
are laying the groundwork for
a new class of smart interfaces
that will be able to improve
operator performance by
monitoring—and by adaptively
modifying—the contents and
configuration of the current
display.
Our own experiments are
examples of research where
neuroscience and HCI intersect.
For instance, other research
efforts for the defense and
automotive industries seek to
correlate neurophysiological
measures of cognitive workload
with the properties of a user
interface, thereby providing a
direct link between interface
properties and brain activity.
In fact, many of the questions
that drive contemporary cognitive neuroscience research also
speak to issues in interface
design. Opportunities abound
for HCI researchers to collaborate with neuroscientists
to address these topics of common interest. In addition to the
earlier examples, neuroscientists are also studying how the
brain:
• manages attentional resources across multiple sensory
channels,
• navigates through virtual as
well as real environments,
• learns the most efficient
procedures for performing a
task,
• allocates trust in competitive
and/or cooperative situations
involving multiple agents or
other users.
Brain-Machine Interface
The most extreme example of
how neuroscience might change
the trajectory of HCI comes
from the nascent field of brain-
