Main Research Interests
When explaining events, individuals tend to prefer a single cause to multiple causes.
For example, when 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. Our research investigates discounting in several domains.
Fluency is the ease with which information can be processed. For instance, the top font on the right is fluent
because it is easy to read, but the bottom font is disfluent because it is difficult to read.
The literature has shown that fluency influences a wide variety of judgments and cognitive operations--including judgments of
truth, intelligence, familiarity, attractiveness, and more. Our research investigates how and when fluency will affect our judgments.
We often consult different pieces of information when we make judgments.
But how do we decide which pieces of information should weigh most heavily in our judgment?
While much research has focused on the kinds of information people use,
less research has examined the ways people determine how to weight the information.
It turns out that deciding how to use this information can be a difficult task.
We are currently studying the ways that people simplify this process and save effort when deciding how to weight information.
Generating Causal Explanations
We are often confronted with an event and must determine why it occurred.
We must come up with a set of possible explanations and evaluate these possibilities to figure out which is most likely.
While most models currently take a set of possible causes as given,
we are exploring how people generate these explanations in the first place.
In particular, how do people come up with explanations for events they have never seen before?
Furthermore, once someone is choosing among a set of possible causes, how do they determine which one is most likely?
Latent Scope Explanations
Explanations that account for a wider range of phenomena are said to have broader explanatory scope
and are therefore considered stronger explanations of the data than alternative accounts.
Explanatory scope assumes that individuals are certain about the data under investigation,
i.e., that they compare explanations against a set of observed phenomena.
Yet in daily life, reasoners are often forced to evaluate explanations based on uncertain, unobserved phenomena.
Our research has examined the role of latent scope in explanatory reasoning,
i.e, the number of distinct effects for which an explanation could account.
We have also explored whether latent scope biases are evident in other cognitive processes,
e.g., categorization, classification, property induction, learning, and memory.
Making Numerical Estimations
We constantly make numerical estimates: How much is a tall coffee?
How long does it take to drive to the airport? What's the chance of raining tomorrow?
This ability to estimate quantitative values is crucial to our functioning and to our understanding of the world.
We are investigating how people come to these estimations and whether they possess distributional knowledge of the domain.
Information Structure in Group Decision Making
How does the distribution of information within a group affect that group's behavior?
How does varying the proportion of members in a group possessing special information affect achievement for groups of varying size?
What role does conflicting information play in decision-making for different sized groups?
This has applications in any scenario with informed, uninformed, and misinformed individuals where group decisions must be made,
for example in evacuations.
Other Research Interests
Perceptions of Randomness
One area of interest is how people understand random events, and use the concept of randomness in their daily lives.
To this end, there are several programs of study we have been working on.
The unfortunate truth is that disasters are a frequent occurrance in the world. Fortunately, so is charitable giving.
Since these contributions form such an important factor in the eventual outcome of disaster-stricken areas, we have begun a research
project looking at people's giving. Most previous research in this area has focused on which people donate - a person-level question.
Instead, we're looking at which disasters get the most charitable giving - a disaster-level question.
Our goal is to eventually create interventions that would lead to more donations.