Welcome! Research in the Markant Lab examines interactions between learning, memory, and decision making.
How do people take action to navigate uncertain or changing environments?
How do learners monitor and control their own learning experience?
How do learning and memory influence the tendency to take risks or explore new behaviors?
We use behavioral experiments and computational modeling to investigate cognitive mechanisms involved in effective learning and decision making.
In addition to basic research in cognitive science, the lab explores implications of these processes for learning and decision making in other contexts, including real-world instructional environments and among populations of learners with diverse cognitive abilities.
Learn more about our research by reading the posts below or by browsing our list of publications.
Why would people with a negative self-image prefer to hear unfavorable feedback that reinforces those self-views? A new paper (with Health Psychology Ph.D. students Shaina Glass and Ryan Hall) builds on our work to understand this phenomenon of self-verifying feedback seeking. We gave participants a task in which they chose to receive evaluative feedback from one of two “AI systems” that rated their abilities relative to their peers. These choices were set up so that one AI system gave feedback that aligned with participants’ self-ratings, while the other gave feedback that appeared to contradict their self-views.
There were two key findings from the paper. First, we replicated earlier work showing that people who self-verify are largely motivated by the desire for accurate feedback–in other words, their self-beliefs serve as a framework for judging whether they can trust information from a new feedback source. Second, we show that among people with negative self-views, there were different patterns of associations between emotion regulation habits and their feedback choice. These participants were more likely to seek out favorable feedback—despite the fact that it appeared to conflict with their self-ratings—when they also reported engaging in more proactive forms of emotion regulation (e.g., reappraisal, perspective-taking) in their daily life. This opens the door to future work examining how certain types of emotion regulation might make people more receptive to feedback that challenges existing self-views.
Link to the paper: Glass, S., Hall, R., & Markant, D. B. (2026). Breaking Away From Destructive Feedback: The Role of Emotion Regulation in Negative Self-Verification. Psychological Reports, 00332941261497277. https://doi.org/10.1177/00332941261497277
Congratulations to Shaina Glass, a Health Psychology Ph.D. student in the lab, for winning the Best Poster Prize at this year’s UNC Charlotte Graduate Research Symposium! Shaina presented work from her M.A. thesis examining connections between emotion regulation habits and the preference for self-verifying feedback (see below). Congrats Shaina!
Imagine that you’ve started a new job and you’ve noticed some perplexing patterns in how your co-workers interact. For example, you observe Bill being rude to Anna, but then shortly after he is perfectly nice when interacting with Carol.
What might explain why Bill acts differently in these situations?
One explanation is that Bill’s behavior reveals an underlying hierarchy (or ranking) of the kind commonly seen in social environments, whether they are formal (e.g., based on a management structure) or informal (e.g., based on popularity).
Bill might be rude to Anna because he’s ranked higher in the social hierarchy, but deferential to Carol because he’s ranked lower than her.
By observing differences in how people interact across different contexts, you can discover the underlying hierarchy and make sense of seemingly inconsistent behaviors.
In a new paper now out in Memory & Cognition, I examine how people acquire this kind of relational knowledge through trial and error learning. This work focuses on a more difficult version of this problem where people are unaware that an underlying hierarchy exists (compared to social situations, in which people can rely on prior knowledge about how social groups are commonly organized and interact). Past research has shown that some people nevertheless are able to discover that underlying hierarchy, and in this paper I examine the kinds of learning experiences that are most likely to facilitate that relational discovery.
Do people change their beliefs when they see a data visualization like a scatterplot? Communicating science and making evidence-based arguments often involve data visualizations like the one below. It can be tempting to think that “the data speaks for itself”—that it paints a clear, relatively unambiguous picture about the relationship between a set of variables. But the mere presentation of statistical evidence, no matter how strong, does not guarantee that people will change their minds, particularly when they have strong preexisting beliefs that run counter to the data.1
A recent figure showing a strong negative correlation between mask-wearing and COVID19 case counts. Would this change someone's mind if they believe these variables are unrelated?
In a new VIS 2020 paper, my colleagues and I examined how people update their beliefs about statistical relationships when viewing scatterplots. We developed some new methods for eliciting beliefs about these relationships and used computational modeling to evaluate the impact of different types of scatterplot visualizations on belief updating. This project adds to a growing number of studies that aim to better understand (and model) how people learn through interactive data visualizations, including cases where such visualizations fail to persuade.
A favorite paper of mine which reviews the many ways people avoid updating their beliefs in the face of contradictory evidence is Chinn and Brewer (1993). ↩
How do people discover relational knowledge through experience? I recently presented a talk on this topic at the all-virtual 2020 Meeting of the Cognitive Science Society. See the embedded video below for an overview of the project, or the proceedings paper for more detail.
