Summary: Putting It All Together - Foundations for Designing User-Centered Systems - page 386

Table 14.1 Example MHP analysis
MHP: example 1: motor skills, typing behavior
A manufacturer is considering whether to use an alphabetic keyboard on his handheld point of
sale (PoS) system. Among several factors influencing his decision is the question of whether
experienced users will find the keyboard slower for touch typing than the standard Sholes
(QWERTY) keyboard arrangement. What is the relative typing speed for expert users on the
two keyboards?
Typing rate = 152/ks (72 words/min)
Typing rate (alphabetic) = 164 ms/key (66.5 words/min)
MHP: example 2: cognitive, working memory
A programmer is told verbally the one-syllable file names of a dozen files to load into his
programming system. Assuming all the names are arbitrary, in which order should the
programmer write down the names so that he remembers the greatest number of them (has to
ask for the fewest number to be repeated)?
The fact that there are 12 arbitrary file names means the programmer has to remember 12 chunks
(assuming one chunk/name), which is larger than the storage capacity of working memory,
so some of the file names will be forgotten. The act of trying to recall the file names will
add new items to working memory, interfering with the previous names. The items likely
to be in working memory but not yet in long-term memory are those from the end of the
list. If the task is to recall the names from the end of the list first, he can snatch some of
these from working memory before they are displaced. The probability of recalling the
first names will not be affected because if they are available, they are in long-term
memory. Thus the programmer should recall the last names first and then the others
but will forget some
ACT-R has been continually evolved and updated—the latest version is
available at act.psy.cmu.edu. The structure of the latest version of ACT-R (see
Fig. 14.3 ) is somewhat similar to that of the MHP. The figure shows the modules
and mechanisms of cognition. It also attempts to show a mapping between the
mechanisms and the areas of the brain responsible for creating the behavior. This
correspondence is not perfect yet (Anderson 2007 ), but as technology advances
(brain scanning in particular) and becomes more sophisticated, it is becoming
more and more feasible to do this mapping.
ACT-R has been fairly extensively tested against psychology data to validate its
predictions. Like all information processing models, it has mostly been used in
thought experiments and as a research tool. ACT-R in particular, though, has been
used to create a large number of user models for several different domains
including driving (Salvucci 2006 ), human-robot interaction (Ritter et al. 2006 ,
2007 ), aviation (Byrne and Kirlik 2005 ; Gluck et al. 2007 ), air traffic control and
dual tasking (Schoelles and Gray 2001 ), and menu use (Byrne 2001 ). These
models are harder to create than GOMS or KLM models, but they make more
detailed predictions, including what information is required to perform the task,
and the results of the information processing. If an addition is performed by users,
for example, ACT-R can be used to predict their answers and the distribution of
errors in their answers (Lebiere and Anderson 1998 ).