Sevan Harootonian, Psychology and Cognitive Science Graduate Fellow

"Modeling Cognitive Strategies in Teaching: From Theory of Mind to Heuristics"

Abstract: Teaching plays a crucial role in human learning, from formal educational environments to
mentorship scenarios, yet its cognitive underpinnings remain underexplored. We focus on the distinction between teaching by reasoning using Theory of Mind (i.e., explicitly inferring what a learner knows) and teaching using heuristics (i.e., relying on a simple rule). We use a graph-navigation task where a learner agent with limited knowledge attempts to navigate through the most rewarding trajectory, with guidance from a human teacher. Our findings reveal that teachers utilize a blend of learner-specific strategies and general heuristics. We model learner-specific strategies using Bayesian Theory of Mind (Baker, Saxe, & Tenenbaum, 2009) and demonstrate that the most effective teachers incorporate this strategy. Intriguingly, we show that teaching strategies can be altered without explicit feedback. This suggests that subtle changes in the environment may significantly alter teaching approaches, highlighting the importance of understanding the cognitive processes behind teaching.

Dan-Mircea Mirea, Psychology and Cognitive Science Graduate Fellow

"Reinforcement learning in the wild: how depression affects social media behavior"

Abstract: Social media platforms have become a ubiquitous part of our lives, and there are widespread concerns about how their features might interact with our mental health. One central feature of the social media experience is the constant receipt of social rewards (e.g. likes, reposts, views) when posting on social media platforms. Reinforcement learning theory predicts that these rewards will modulate users’ posting behavior, leading users to post more frequently in response to higher rates of reward, and to learn what type of content is most rewarding. This process might function differently in depression, which has been widely linked to atypical reinforcement learning in lab studies. Across two Twitter/X datasets, I will show how depression modulates the effect of two forms of social media reward (likes and retweets/reposts) on different aspects of posting behavior - post frequency, sentiment and semantic content. Studying these differences could help us better understand the complex interaction between social media and mental health.