Projects

Below are summaries of some of our current and recent topics and projects.

Replication, Meta-Analysis, Open Science Practices

This is quickly becoming a major theme in the Lab. In addition to conducting new empirical studies, we are becoming more interested in looking closely and critically at existing research by conducting replication studies, doing small scale meta-analyses with a focus on detecting publication bias, and promoting open science practices.

• Power and Cognition - We recently tried unsuccessfully to replicate a highly cited result that feeling powerful affects basic cognitive processes (as part of a multi-lab effort). We are now going to do a more systematic review and meta-analysis of research on this topic.
• Anxiety and Risk Taking - We recently started a meta-analysis on the relationship between these two variables.
• p-Curve Analysis - This is a statistical approach to detecting publication bias in an area of research by considering only published results that are statistically significant.

Risk Perception and Risk Taking

• Risk Perception - What factors influence how people perceive and judge risks? This includes personal risks (e.g., one's own risk of having a heart attack) and societal risks (e.g., the risk posed by genetically modified food). Much of this work is based on self-report measures of risk perception.
  • Risk Perception and Social Class. Based on current theories about the psychology of social class, we have hypothesized that people who are lower in social class should perceive greater risk across a variety of situations than people who are higher in social class. We have "sort of" confirmed this hypothesis but need to do more to fully understand our results.
  • Risk Perception and Masculinity. We have found that people who score higher in "conformity to masculine norms"--regardless of whether they are male or female--tend to perceive less risk across a variety of situations. We continue to work on understanding what these results mean and how they can be extended.
• Risky Decision Making - What factors influence people's tendency to take risks? Here we focus on basic decision processes that can be studied using simple laboratory risk-taking tasks.
  • Carryover Effects on Risk Taking. One current project (with Dr. Martin Shapiro) focuses on the "reflection effect," which is people's tendency to take more risks when dealing with potential losses than potential gains. Specifically, we have found that making several decisions about losses can put people in a "risk taking mindset" that then carries over to decisions about gains. Likewise, we have found that making several decisions about gains can put people in a "risk avoiding mindset" that then carries over to decisions about losses.
  • Adolescent Risky Decision Making. Under what conditions do adolescents make riskier decisions than adults? We have evidence that adolescents are especially likely to make risky decisions when outcome probabilities are expressed in vague verbal terms (e.g., "good chance") as opposed to precise numerical terms (e.g., "60% chance). We now need to replicate, extend, and explain these results.
  • Icon Arrays for Risk Communication. Icon arrays are graphical representations of the probability that something bad will happen. A typical icon array might consist of 100 ovals, where the probability of the bad outcome represented by the percentage of red ovals. We have found that people less likely to take a risk when probabilities are communicated using icon arrays. But we still need to figure out why this is.

Basic Quantitative Thinking

• Sample Size Bias - Sample size bias is a phenomenon we discovered in our Lab, where judgments of averages increase as the sample size increases. We have shown that this happens for estimates of the means of sets of numbers (Smith & Price, 2010), estimates for the mean sizes of sets of objects (Price et al., 2014), estimates of the mean risk of a set of people (Price et al., 2006). We have some new studies showing that it also happens for judgments of the average attractiveness of groups of people. We are still working on understanding the precise conditions under which it happens (and does not happen) and why.
• Quantitative Estimation - How good (or bad) are people at estimating quantities, including numbers of things, weights, distances, and so on? Are there individual people who are "good estimators" across the board and, if so, why? To what extent do they just have a "good feel for numbers" as opposed to use more effective estimation strategies? For "bad estimators" (which might be most of us), are there simple ways to help them make better estimates?

Diversity Judgments

• There are many situations in which people judge how diverse a group is (e.g., employees at a workplace, students in a classroom). But how do people make these judgments and what factors (including the people's own identities) influence them?