Hello, my name is Roderick Seow, and today
I will tell you about our efforts towards
establishing a paradigm to investigate strategy
use in complex skills.
This is a prerecorded presentation for the
2020 International Conference on Cognitive
Modelling.
Across various skills and tasks such as editing
a document, learning a language, or even driving
a racecar, how do people decide which strategy
to use, and what are the factors and processes
that underlie such decision making?
Prior research has identified a few key factors
such as history of success, but the relationship
between changes in skill level and strategy
selection remains relatively unstudied.
In the tasks used in prior research, the strategies
typically have stable payoffs or ordering
of payoffs, meaning that one strategy is often
always better than another.
However, to study how skill level relates
to strategy selection, we need a task where
the payoffs of strategies change with increasing
practice such that different strategies could
be optimal depending on a person’s skill
level, much like in many real-life tasks.
More concretely, we want a task paradigm with
two strategies, where strategy 1 is easy to
execute well, and thus optimal for novices,
but limits performance, and strategy 2 is
difficult to execute well, and only becomes
the optimal strategy after sufficient practice
on the task.
A candidate task is Space Track – a racing
video game originally developed by Anderson
and colleagues.
The goal is to earn points by flying the yellow
space ship through track segments in a frictionless
environment, while avoiding having the middle
of the ship crash into the green walls as
this will lose you points.
Each game is 3 minutes long.
To control the ship, a player would press
W to accelerate in the direction that the
ship is pointing and A and D to rotate the
ship.
Note that rotating alone does not change the
ship’s flight path, but still needs to be
followed up with a thrust.
This clip on the left shows a player navigating
around corners smoothly, but also experiencing
a crash at the end.
Because of the frictionless physics of the
task, one needs to account for leftover momentum
when aiming for a new flight path.
As the illustration on the right shows, naively
thrusting while pointing along the desired
trajectory will result instead in the red
trajectory.
What a player should do is to point the ship
somewhere between the current and desired
trajectories, as shown by the illustration
on the left.
Some players, like the one you just saw, are
able to account for this to fly smoothly around
the corners.
Others reduce this complexity by reducing
the current speed of the ship through a stopping
strategy consisting of two actions: A turn
to orient the ship 180 degrees with respect
to its current trajectory, and then a thrust
to reduce the ship’s speed, as you can see
in this clip on the right.
Stopping helps to compensate for a lack of
turning knowledge and gives the player more
time to react to the situation, which leads
to fewer crashes especially for less experienced
players.
However, stopping also limits the maximum
speed which in turn limits how far one can
travel.
Thus, greater use of stopping does not straightforwardly
predict either an improvement or decrease
in performance.
We can begin to observe this tradeoff in previous
experiments.
Each dot is a trial from human players, and
we can see that as the use of stopping increases,
the mean points scored per trial remains roughly
constant around 500 points.
However, what shrinks with increasing use
is the range between the floor and the ceiling
in points.
If you are performing below the blue line,
increased stopping will improve performance,
but if you are already performing above the
blue line, then decreasing stopping will be
more beneficial for performance.
To investigate potential interactions between
strategy use and skill, we created 4 ACT-R
models by crossing two levels of turning knowledge
with two levels of strategy use.
As shown by the leftmost graph, when the player
is a naïve turner and has little turning
knowledge, stopping is always better than
not stopping.
However, when the player knows how to turn
in this frictionless space, there is a crossover
point at a certain amount of practice such
that a non-stopping player overtakes the stopping
player in performance.
So in summary, stopping in Space Track appears
to be a suitable and interesting paradigm
for studying how changes in skill level affect
strategy selection.
We are currently augmenting this paradigm
by including independent measures of things
like turn knowledge and perceptual-motor calibration
to better understand how agent-internal variables
mediate the relationship between practice,
skill, and strategy selection.
Thank you for listening!
