YSM 85-3

You’re out of breath. Your heart is
pounding as you make your way
through the crowd. You’re being
chased, and you know it. But you’re surrounded
by hundreds of bystanders, and not
sure where your pursuer is. Then suddenly,
you notice someone, and know he is the one
chasing you without a second thought. How
did your brain determine that?
Brian Scholl, Professor of Psychology at
Yale University, is attempting to answer that
question by studying the cognitive mechanisms
responsible for the detection of chasing.
This research is part of the Yale Perception
and Cognition Lab’s broader investigation
of how humans perceive animacy — the
ability of objects to have motivations or goals
and to act accordingly.
Psychologists first recognized animacy
as a distinct property of visual experience
during the early 20th century. An important
early development in the field came in 1944,
when Fritz Heider and Marianne Simmel
showed that observers attributed animacy, to
simple geometric figures. Since then, multiple
research groups have found that animacy perception
persists when observers have explicit
knowledge that objects are not animate, and
that animacy perception occurs across cultures
and even in infants.
Scholl became interested in animacy
research when he asked himself the simple
question, “What is it that I see, and what is
it that I’m thinking about?” He realized that
objects’ animacy stood out with as much
immediacy as their color or shape, which led
him to wonder whether animacy might be
processed at a fundamental level in the brain,
instead of at the higher levels on which most
previous research had been focused.
Quantifying Animacy
When Scholl and graduate student Tao Gao
began to study the perception of animacy several
years ago, they faced a lack of quantitative
methods to measure animacy perception. As
Scholl puts it, animacy perception has been
“fascinating psychologists … for decades as
demonstration, and we’ve been in search of a
way to turn it into rigorous science.”
The lack of quantitative methods resulted
from two main methodological issues. First,
most of the animations used in animacy
studies were scripted manually and included
multiple types of implied behavior, making
the influence of any single feature difficult to
isolate. Second, the most common measurement
of animacy perception was a subjective
questionnaire. The combination of these
two issues made it difficult for researchers to
distinguish animacy perception in the visual
system from higher-level inferences.
To overcome these challenges, Scholl and
Gao developed two models to measure one
kind of animacy perception, chasing detection.
Both involve three types of simple
shapes moving on a two-dimensional screen:
one “sheep,” one “wolf,” and multiple “distractors”
identical in appearance, but not
behavior, to the wolf. The behaviors of both
the distractors and the wolf are generated by
mathematical algorithms, allowing systematic
control of the differences between them.
The first experiment (“Find the Chase”)
generates the sheep’s movements algorithmically
and asks observers to identify whether
any chasing behavior is present, and if so, to
identify the sheep and the wolf. The second
(“Don’t Get Caught”) requires the observer
to control the sheep and attempt to avoid
the wolf for a fixed duration. In both experiments,
observer performance can be objectively
quantified by the number of correct
detections and the number of escapes in the
second, respectively.
Cues for Chasing
Using these new methods, Scholl and Gao
examined different features of wolf motion,
attempting to determine which were important
for chase detection. One important cue
that they identified was the maximum deviation
of the wolf from the line between it and
the sheep, which they called “chasing subtlety.”
At a chasing subtlety of zero degrees, detecting
a chase initially seemed very difficult, but
the wolf and sheep quickly became obvious,
allowing observers to detect chases in nearly
90 percent of “Find the Chase” trials and to
escape 60 percent of “Don’t Get Caught”
trials. In constrast, at a chasing subtlety of
60 degrees, the wolf and sheep failed to stand
out and performance decreased drastically,
with chase detection falling to 60 percent and
escape rate to 25 percent.
Scholl and Gao then decided to study
the impact of object orientation on chasing
detection. By switching the shapes used to
represent wolves and distractors from circles
18 Yale Scientific Magazine | April 2012 www.yalescientific.org