At the Princeton Social Neuroscience Lab, we study the thoughts, cognitive processes, and behaviors that occur at the place where our internal world meets the external social world. We are interested in the dynamics of the mind – both on its own and in interaction with other minds - during naturalistic experiences. We employ methods like functional neuroimaging, experience sampling, machine learning, and behavioral experiments to gain empirical insights into questions about the self and the social world.
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What makes a good conversation?
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Conversation is a powerful tool for transforming individual minds into a whole greater than the sum of its parts. It allows people to access information and emotions unavailable through direct experience alone. It allows people to view their own experiences from an outsider’s eyes, affording a new perspective that might otherwise be out of reach. What differentiates a great conversation from a terrible one?
Our team is pioneering fMRI hyperscanning methods to interrogate the interdependent dynamics of two brains as they engage in naturalistic conversation (Tsoi, Burns, Falk, Tamir, 2022). Using this method, we aim to uncover how a mere exchange of words weaves into a rich fabric of an interconnected, meaningful life. We see that conversations align minds. That said, friends use different conversation strategies than strangers: while strangers focus on finding common ground, friends focus on exploring new ground and have better conversations as a result (Speer et al., 2024). This exploration strategy also helps strangers find agreement in decision-making conversations.
Current lab projects:How does the flow of a conversation predict successful social connection? Interpersonal emotion regulation? Mutual compromise? Effective persuasion?
What is the role of finding common ground vs. exploring new ground in affiliative conversation? in decision-making conversations?
What makes an individual a great (vs. a boring) conversationalist?
How does self-disclosure impact social connection during naturalistic conversation?
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Why do we think?
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The mind can access a vast landscape of thought. One can think about the local sights and sounds, or time travel into the past or far into the future. One can think about worms and wormholes, order and chaos, friends and fairies, and everything in between. With this vast landscape of thought at our minds’ doorstep, what determines the content of one’s thoughts at any given time?
We endeavor to understand why people think the things they think, and are doing so by studying the content and dynamics of thought. We have pioneered methods to automate analyses of think-aloud data. By mapping the dynamic trajectories of thoughts through semantic space, we can test hypotheses about what thought trajectories are optimized for. So far, we have found evidence that spontaneous thought helps people to process and remember incoming stimuli from the world around them (Mildner & Tamir, 2021; Mildner & Tamir, 2022), pursue goals (Mildner & Tamir, 2024), make decisions (Enz & Tamir, 2023), and regulate emotions.
Our ongoing work builds upon a framework of spontaneous thought that operationalizes the dynamics of spontaneous thought as an unconstrained memory process (Mildner & Tamir, 2019).
Current lab projects:
How does spontaneous thought help people regulate emotions?
Can we formally model people’s spontaneous thought trajectories? Which content and dynamics features does an optimal model include?
How are the content and dynamics of spontaneous thought distorted in clinical populations or enhanced in creative populations?
How do psychedelics change the dynamics of spontaneous thoughts?
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How do we understand others' minds?
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One person can never truly know the contents of another person’s mind. Despite the inherent opacity of others’ thoughts, feelings, and intentions, humans are actually quite proficient at inferring others’ invisible, internal mental states—a capacity known as mentalizing. The lab uses behavioral and neuroimaging methods to explore the processes that allow us to accomplish these mind reading feats.
One way to understand others’ minds is through the process of simulation, where people use their own self-knowledge to infer other people’s mental states and traits. Our research has explored the cognitive processes underlying such social inferences, such as egocentric anchoring and adjustment (Tamir & Mitchell, 2010; Tamir & Mitchel, 2013), the flexible self-application hypothesis (Todd, Simpson, & Tamir, 2016), and the factors that amplify and attenuate the use of self during mentalizing (Sened et al., in press; Todd & Tamir, 2024).
A second route to understanding others’ minds is through model-based social cognition. Our lab has developed the 3D Mind Model, a framework for explaining how people model the social world around them (Tamir & Thornton, 2018; Thornton & Tamir, 2021; Tamir & Thornton, 2024). Our multi-layered framework distills the complexity of the social world into low-dimensional maps. For example, we have shown that people represent mental states using three dimensions: rationality, social impact, and valence (Tamir, Thornton, Contreras, & Mitchell, 2016), and that they do so consistently across cultures and history (Thornton et al., 2022). This model of the social mind is tailored to the problem of predicting other people. We have used behavioral, linguistic, and neuroimaging analyses to show that people use these dimensions to accurately predict how a person will transition from one state to the next, or from one action to the next (Thornton & Tamir, 2017; Thornton, Weaverdyck, & Tamir, 2019; Thornton, Rmus, Vyas, Tamir, 2023; Thornton & Tamir, 2021a; Thornton & Tamir, 2021b). This model helps us to understand how people incorporate states, actions, and situations in social representations (Thornton & Tamir, 2024). This model thus offers a computationally tractable method for modeling how people represent and predict their social world.
Current lab projects:
Who is good at representing and predicting others? What are the social benefits of doing so?
How is social prediction impacted in clinical populations?
How do people learn how to accurately predict others over development?
How does simulating others change the self?