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associated with the conceptually simple “selected”
guidance modes. It is widely believed in the industry
that pilots use these modes most often early in their
line flying. In fact, many airlines, including the one
under study here, provide simulator training on the
simple modes only. It is assumed that the more
complex managed modes will be learned in line
operations. It is known that pilots make some use of
managed modes early in their line flying, but they
might not discuss them at length in the interviews
because they are not well understood. Like the term
speed, descent is rarely mentioned in the initial
interview, but peaks in the second line interview at a
relative frequency of 17% (second only to speed).
Speed and vertical are special terms because together
they compose the name of one of the simple vertical
guidance modes, vertical speed. These terms also
appear in the early line interviews with high relative
frequency.
The following terms have higher relative frequency
in the last line interview than they have in the first
two line interviews: managed, mode, thrust, idle,
autothrust, FMA, path, constraint, and target. The
increasing salience of these terms in the last line
interview indicates that between twelve and 18
months on the line, the managed modes, especially,
the idle thrust descent on a path defined by
constraints and speed targets, become more salient
concepts for the pilots. The relative frequency of the
term managed increases with each successive phase
of experience, reaching a maximum in 3L. Mode and
thrust have similar profiles, but with less pronounced
growth. It is probable that the third line interview has
captured this learning process in progress. The fact
that many of these terms, while increasing in relative
frequency, still have low relative frequencies
suggests that these concepts are still growing in
importance.
The frequency data indicates that when pilots have a
year of experience in the airplane, they talk more
about the simple “selected” modes than about the
more complex “managed” modes. At eighteen
months, talk about the selected modes still dominates,
but words that are associated with the managed
modes increase in frequency.
Term co-occurrence analysis
The relative frequencies of terms gives us an
indication of how the importance of various
autoflight concepts changes with experience on the
airplane, but it says nothing at all about the
organization of the concepts. Co-occurrence of terms
provides a simplified representation of conceptual
structure. Change in conceptual organization can be
tracked by representing the changing relations among
terms. Two analyses of the co-occurrence of terms
were performed. First, we examined each automation
related term and looked at the other terms (whether
related to automation or not) that tended to co-occur
most frequently with that term. Second, we
computed term/term similarity metrics.
To build the word-word co-occurrence matrix, a
window 21 words wide is passed over the pilot
conversational turns in the interview transcripts. The
‘target’ word is in the center of the window; the
‘context’ of the target word extends ten words to the
right and left. The window is weighted linearly,
meaning that words adjacent to the target word
receive the highest co-occurrence score (in our case,
10), and those at the ends of the window receive the
lowest co-occurrence score (1), with a linear
progression between these two. The result is a
symmetric matrix. The rows and columns are
represented by all of the words used in the interviews
at a particular stage of training. That is, four separate
matrices are constructed: one for each of the four sets
of interviews. Each cell in a matrix contains the cooccurrence
value for two words. Initially, the matrix
is filled with zeros. Each time two words co-occur in
the same context window, their co-occurrence score
is increased.
Each row (or column) in the matrix represents the cooccurrence
scores of a particular word, and can be
thought of as a vector in a high-dimensional space.
Now consider the automation terms. We use the
cosine metric to measure the angle between every
pair of automation term vectors. By this measure a
word will be judged semantically similar to another
word not only if the two have repeatedly occurred in
close proximity to one another, but also if they
appeared in similar contexts: that is, if the two words
occurred in close proximity to a similar set of words.
　
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