Tessa Charlesworth: 2022 Doctoral Research Excellence Award Winner
May 26, 2022
FABBS is delighted to highlight the exceptional work of young scholars. The FABBS Doctoral Dissertation Research Excellence Awards honor graduate student scientists who have conducted research of superior quality and with broader societal impact.
The opportunity to nominate students for these awards is offered to our Departmental and Division Affiliates. This year, nominations were reviewed by a committee of current and former FABBS Board members, including Jeffrey Schall, Frances Gabbay, and Adriana Galván. The committee was impressed with the strength of the applicants, and felt privileged to be able to see a preview of the future of our sciences.
We are excited to introduce you to 2022 award winner Tessa Charlesworth, from Harvard University, whose outstanding work focused on Patterns of Long-Term Change in Implicit Social Cognition.
Can you introduce yourself and tell us a little bit about your area of research?
I was born and raised in Victoria, BC, Canada amongst the beautiful trees and ocean. For college, I moved to the “concrete jungle” of New York City, where I attended Columbia University. At Columbia I fell in love with Psychology and especially the research from Mahzarin Banaji and her colleagues on the topic of implicit bias. It was a therefore a dream come true to be able to study with Professor Banaji at Harvard University, where I pursued my PhD in Psychology until graduation in May 2021.
At the broadest level, my research is concerned with the question of social change, especially change in our more subtle, automatic, or implicit beliefs and attitudes. I am driven by questions such as: How does a culture transform across time in what it thinks and feels about social identities like race, gender, class, age? Which attitudes and beliefs show the greatest change, and what lessons can we learn from those changing topics?
For a long time, social scientists have studied questions of social change by measuring explicit, self-reported attitudes and beliefs. But, over the past two decades, it has become clear that explicit measures can’t tell us the whole story: measures that are more indirect and implicit can reveal complementary portraits of what is (and, equally important, is not) changing over time. Now that we are in an era of “big data,” with massive amounts of archived data and the novel techniques to analyze it, we can begin to describe, and even theorize, on the patterns of long-term social change in implicit attitudes and beliefs.
What inspired your interest in this topic?
As often happens, the inspiration for my research program came from an accumulation of sources. First, I came into Mahzarin Banaji’s lab at a time when many of the other students and post-docs were tackling questions of individual-level change (that is, changing a single person’s mind): Calvin Lai, Benedek Kurdi, and Tom Mann were all inspirational researchers who were trying to understand when and how we could best intervene in an individual’s daily life to alter their implicit bias. While the results were promising, with some later work showing that change could persist over the span of a few weeks, it was also clear that we needed to expand to look at much longer timespans and much bigger samples of our culture.
Second, at the same time that my colleagues and I were having these conversations, I just so happened to be in our first-year statistics class, taught by Patrick Mair. I was required to do some sort of data analysis final project. I knew I wanted to teach myself big data wrangling, so I figured I should look at the massive data archive from Project Implicit – an online demonstration website. As I started poking deeper into the data, I was shocked to find long-term change even in implicit measures of attitudes. This was largely unexpected given the pervasive idea that implicit bias was generally very hard to change over the really long-term. After another year and a half of re-analyzing in many different ways to ensure the robustness of those trends, Mahzarin and I had the first paper in a series that would become my dissertation.
Any exciting or surprising findings in your research?
Research always brings up many surprising findings, so I have many, many answers to this question! But to keep it short and digestible: to me, the most surprising finding from my dissertation work is that these long-term patterns of change in implicit bias are happening across nearly every demographic group in US society. That is, for the attitudes that we find are changing (i.e., race, sexuality), we find that both men and women, religious and non-religious, college-educated and non-college educated, White, Asian, and Black Americans, young and old, even liberal and conservative, have all been moving in the same direction over time. While there were some exceptions in the rate of change (e.g., younger have moved faster than older, liberal faster than conversative), the overwhelming consistency in the direction of change is surprising. It shows us that change in implicit bias is truly a macro-level, societal phenomenon that reflects a cross-cutting transformation in what we associate with these groups.
This award recognizes the broader impact of your research. What are the societal implications from your work?
The first implication of the work is the conclusion that “implicit bias can change over the long-term,” particularly for race, skin-tone and sexuality biases. This is important in showing us that the efforts being made to change the culture and conversations around race and sexuality – interventions such as same-gender marriage legislation or Black Lives Matter protests and better representation in media, sports, and politics – are working to change the automatic associations that come to mind in US society. Although it is crucial to emphasize how much further we have to go, I think the fact that change is possiblecan be motivating to continue as well as expand the efforts being made in society.
The second implication, however, is that implicit bias doesn’t have to change. This is in contrast to what we found for explicit biases: self-reported biases about every social group we’ve studied (age, disability, body weight, race, skin-tone, sexuality, gender) have decreased in bias over the past decade and a half. And yet, at the same time, we find that some implicit biases – about age and disability in particular – have hardly budged in recent years. This, to me, shows the true dissociation between explicit and implicit measures of attitudes, and the clear importance of studying both measures to understand societal change. While our explicit anti-elderly or anti-disabled biases could be decreasing because of norms against expressing prejudice, the automatic associations our culture still makes about those groups have nevertheless remained stubbornly intransigent. It is a result that I hope motivates us to focus even more on tackling those persistent implicit biases that we may not often think about.
What ‘s next? Do you plan to continue your research on this subject?
Yes, absolutely! I will be applying for faculty jobs this coming year so that I can continue pursuing these questions of societal change for the rest of my life. Who knows, perhaps I’ll even study it long enough that we’ll begin to see those particularly stubborn biases begin to change too!