Causal Discounting
The first and foremost of my research foci is on causal discounting.
Researchers have long known that
when explaining events, individuals tend to prefer a single cause to multiple
causes. For example, upon noticing
that the lawn is wet, an individual is faced with several possible causes; it
could have rained, or the sprinklers could have turned on. However, once it
is clear that it did rain, the individual might consider it less likely that
the sprinklers turned on, even though these causes are not mutually
exclusive. The individual is discounting the possibility that the sprinklers
caused the moisture. I investigate discounting in several domains.
Discounting of Fluency in Judgments
Perceptual fluency -- the ease with which an individual can process a piece
of information -- influences a variety of judgments. (For information on my
work on fluency, click here ). For example,
items that are easily retrieved from memory are often judged to be occur more
frequently in the real world. This makes sense, as items that are
frequent are likely to be seen more often, and thus more easily
remembered. However, there are many possible reasons that a piece of
information could be processed fluently. If there is a salient cause for
fluency aside from frequency (such as fame) will
people engage in causal discounting and lower their estimation that the item
is frequent?
I have done a great deal of research on this topic, and found many examples
of causal discounting. People discount for fame, proximity, personal
relevance, and a variety of other reasons -- in each case they attribute
their mental state of easy process to something other than frequency, and
thus discount the possibility that the item could actually be frequent.
I am currently investigating possible cognitive mechanisms that might be used
in discounting of mental states. Further, I'm looking into what processes
people might use in retrieving possible causes for fluency. This is an
extremely rich area of study, and will be the topic of my
dissertation.
Discounting of Fluency Outside of Judgment
Demonstrations of discounting of fluency have, until now, been limited to the
domain of judgment. However, I believe that since fluency plays a large roll
in a variety of cognitive processes, discounting of fluency must do so as
well. Therefore, I am investigating whether discounting of fluency occurs in
other cognitive domains.
For example, studies by other researchers have demonstrated that fluency can
play a role in categorical induction. That is, if I tell you a robin has
sesimoid bones, you will be more likely to think that a penguin has sesimoid
bones as well if you are easily able to process the concept of penguin.
Since fluency plays a role in such inductions, the discounting of fluency
should as well. A set of studies I'm running involves asking students to
make inductions about their school mascot. The mascot should be available to
all students which should make them more likely to make inductions to that
animal. However students for whom the mascot has just been made
salient should discount that fluency, and thus be less likely to make an
induction.
Causal Discounting in Categorization
Categorization researchers are starting to realize that causal knowledge
plays a large role in categorization. Thus, many researchers are moving away
from more feature based theories, and towards theory based theories. If
causal knowledge plays a role in categorization, then causal discounting
should be observable given the correct situation. For example, if you see an
animal that has four legs, a tail, and smells bad, you might be tempted to
think it is a skunk. But if you learn that the animal has been wading in the
sewers, you might attribute the unfortunate odor to the sewage, and discount the
possibility that the smell is caused by "skunkness".
In fact, in a series of studies I have found just that. Additionally, I have
found augmenting -- if the skunk has been wading in a flower bed, the
influence of the stink is even greater. These findings support the notion
that categorization is at least partially causal in nature, and poses a major
challenge to many theories of categorization, which have trouble explaining
the data.
My collaborator Josh
Tenenbaum and I are in the process of examining models of categorization
to see which of them are capable of explaining these results.
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