Neil Hester, Ph.D. is a graduate of our Social Psychology program with an additional concentration in Quantitative Psychology. He is now an Assistant Professor at University of Waterloo (which, coincidentally, is the undergraduate alma mater of his advisor at UNC, Kurt Gray).
Neil’s research investigates the myriad factors that explain how perceivers form impressions of targets—in other words, why people are perceived as more or less attractive, trustworthy, competent, threatening, and so on. This work encompasses topic such as stereotyping and discrimination, face perception, and perceptions of bodies and clothing. Most recently, Neil has launched a new line of inquiry specifically about dress and first impressions, which he says is “undeniably important in shaping first impressions, but is notably absent from decades of theorizing, which instead emphasize the roles of faces and bodies.” When asked about the motivation for this new area of work, Neil cited his experiences at Carolina as an essential part of learning how to think in new ways about old ideas: “I had the opportunity to learn from folks like Kurt Gray, Keith Payne, and Kristen Lindquist, who really teach you how to critically examine current or dominant modes of thought and ask questions about what’s wrong or what’s missing.”
Neil also teaches Introduction to Social Psychology for undergraduate students as well as Data Visualization, Analysis, and Communication for graduate students, which is a coding course in the statistical language R. He is especially fond of the coding course, which is broadly beneficial to students regardless of whether they pursue careers in academia, industry, or otherwise. This aspect of Neil’s training also has roots at UNC, which “provided really amazing quantitative training. There aren’t many places where you can take so many unique and useful grad-level stats courses, and it’s nice to be on the other side of things at Waterloo.”
For prospective and current graduate students, Neil thinks that it’s important to accept rejection as part of the process, not just a negative outcome. “Do not view any one failure as a reflection of your own abilities. Remember that short-term results can be pretty random, like patterns in small data sets. If you experience some early failures, this is not evidence that you are ‘not smart enough’ for research—believe in yourself, take care of yourself, and keep at it.”