insufficient. For this reason, menu-based
interaction has attracted a lot of research
attention and resulted in a number of
interaction techniques, user studies,
and models. To expand the scope of
optimizable menu designs, we need to
start to address them. We identified five
challenges, which we discuss here.
Challenge 1: Design space. As
mentioned earlier, the search space
of menus is immense because it
comprises numerous dimensions, such
as the item dimensions, visual cues,
and menu layout, but also hotkeys,
gestures, temporal considerations,
and so on. Unfortunately, not all main
dimensions of this design space have
been characterized. Besides hierarchical
command assignment, the design space
of existing major menu techniques has
not been defined.
The lack of a problem definition is
of course natural for a new research
topic. Although research in menu
design began several decades ago, we
think that it is still in its early stages
due to its inherent complexity and the
constant evolution of techniques. The
possibilities and decision variables are
just starting to be explored.
Thus, the first major challenge
we see is to analyze the features of
existing menu systems to identify the
most important design variables (e.g.,
geometric, visual cues, semantic, etc.)
beyond the ordering of items. This will
help target work to the most pertinent
menu types and will guide work on
developing predictive models.
A related question is to which novel
techniques to first expand. Almost 100
novel visual menus have been proposed
the past few decades ( www.gillesbailly.
fr/menua/). While for some tasks they
can be more efficient, they add new
trade-offs. For instance, Figure 3 shows
examples of two designs with different
appearance and interaction properties.
They are based on circular layout
and gestural interaction, reminding
us that menu organization need not
be systematically linear but can also
be circular. Such menus have several
interesting properties for selection time
and spatial memory.
To prioritize the design space, we
need to better understand existing
menu techniques, in particular which
features offer the largest positive effects
to users. To do that, it is necessary to
conduct empirical studies of menu
behavior. However, the difficulty is
that the experimental designs tend to
quickly get complex because there are
subtle interactions among properties of
even very simple menus [ 2].
Challenge 2: Multiplicity of
objectives, users, and constraints.
Menu designers typically have multiple
objectives. They want not only to
improve speed, but also to consider
errors, discoverability, aesthetics,
learnability, comfort, and so on. These
latter objectives are less studied.
Moreover, in a real design case the
objectives can be contradictory. For
example, fast, precise menus may be
difficult to learn.
It is thus necessary to explicitly
deal with complex trade-offs during
optimization. This is technically
challenging because the search
process becomes more complicated.
It also requires designers to be able to
define their objectives more clearly.
It may be that it is easier to simply
try to design the menu than specify
all the constraints, objectives, and
assumptions to a computer.
This also calls for new ideas in how
to represent users in code. Previous
work in menu optimization has
assumed a “generic user” represented
as a statistical distribution. It has
disregarded constraints and nuances
arising in different contexts and user
However, there is also an unavoidable
chicken-and-egg problem in task
definition: How can we predict how
a menu will be used and by whom? If
successful, a design will attract new
users, change behaviors, and extend
to contexts that were not known at
design time. A challenge is to find ways
to optimize for “robust” designs that
are not as dependent on particular use
Challenge 3: Predictive models
of user performance. Optimization
methods evaluate the quality of a
menu based on a predictive model.
A predictive model is the engine of
the approach: It allows menus to be
evaluated without empirical studies.
However, the scope of these evaluations
depends on the available predictive
We recently proposed a novel
analytic model of menu performance
at CHI’ 14 [ 2]. It assumes two visual
search strategies, serial and directed,
and a pointing subtask. It captures the
change of performance with five factors:
menu length, menu organization,
target position, absence/presence of
target, and practice. One novel aspect
is the model is expressed as probability
density distribution of gaze (when and
what items users are looking at), which
allows for deriving total selection time.
While it is one of the more
advanced predictive models of menu
performance, we are still far from
proposing a model mature enough
to avoid empirical studies. Indeed,
existing predictive models are limited
to a small subset of possible menu
designs and menu properties. For
instance, they do not cover hotkeys,
gestures, or semantics, major factors of
A main challenge will be to develop
predictive models that better cover
existing features and then expand to
advanced menu techniques such as
those in Figure 3.
Unfortunately, in contrast with the
first challenge, there is significantly
less work on predictive models of
Save Page As
Show All History
Subscribe to This Page