Publications

Please refer to my Google Scholar and ORCID profiles for the most up-to-date information.

2025

Predicting that birds of a feather will flock together: Expectations of homophily for others but not the self

Miriam E. Schwyck, and Carolyn Parkinson

Journal of Experimental Social Psychology

Abstract URL OSF

Similarity among friends – or other socially connected individuals – is a ubiquitous characteristic of social networks. There are several, often simultaneous, mechanisms (e.g., social influence, shared environments) through which such links between social connection and similarity arise, including homophily, or the tendency for similar people to attract one another. While past research has found that people use similarity heuristics to structure their mental representations of social networks (predicting that friends are likely to be similar to each other), it is unknown if people assume such similarity arises through homophily, specifically. Here, we tested if people assume that homophily will shape their own and others’ future friendships. Participants (NTotal = 560) learned how (i) trustworthy and (ii) trusting various partners were through repeated trust games. Participants predicted which partners would become friends with one another and which partners they would become friends with themselves if they were to meet in person. Across two studies and both trait measures, we found that participants were significantly more likely to predict that partners who behaved similarly would later become friends compared to those who behaved dissimilarly. Interestingly, we found that participants were significantly more likely to predict that they would become friends with highly trustworthy and highly trusting partners compared to highly untrustworthy or highly untrusting partners, regardless of their own behavioral tendencies or even their own self-perceptions. These findings suggest that for trust-related traits, people assume homophily will govern others’ relationships but not necessarily their own. Such expectations likely shape how people approach or foster new friendships for themselves and between others.
The role of one's own social network position in learning new networks: Brokerage is associated with better network learning

Miriam E. Schwyck, Meng Du, and Carolyn Parkinson

Journal of Experimental Psychology: General

Abstract URL OSF

Navigating our complex social lives requires understanding where others sit in our social networks. Some individuals may be more attuned to the structure of their social world, and thus, better able to learn new social networks due to having accumulated accurate priors about social network structure in their own lives. Correspondingly, such individuals may acquire more advantageous positions in their own social networks. In four studies (Ntotal = 1,768), brokers (people who connect otherwise disparate people in their own networks) were especially good at learning and remembering new networks that were structured like typical real-world social networks, but not atypically structured ones, suggesting that brokers are attuned to the structure of real-world networks. Additionally, we found that brokers were able to learn networks better by focusing on ties that exist in those networks (as opposed to focusing on ties that were missing) and other brokers. We found no differences when the network was framed as a social network of friends or a nonsocial network of flights between airports. This work illuminates the mechanisms of network learning based on one’s own experiences and establishes links between one’s own social network position and one’s ability to learn new networks.
Loneliness is associated with unstable and distorted emotion transition predictions

Ava Ma de Sousa, Miriam E. Schwyck, Laura Furtado Fernandes, Ezra Ford, Begüm Babür, Chang Lu, Jacob Zimmerman, Honbo Yu, Shannon Burns*, and Elisa C. Baek*

Communications Psychology

Abstract URL OSF

Loneliness is associated with disruptions in socio-cognitive processes, including altered self-other representations and atypical processing of external stimuli. Here, we examine whether loneliness is characterized by altered expectations of emotion transitions for both oneself and others, which may contribute to the observed disruptions in socio-cognitive processes and pose challenges for social connection. Drawing on data from seven studies (total N = 1730; NStudy1 = 113; NStudy2 = 185; NStudy3 = 376; NStudy4 = 91; NStudy5 = 68; NStudy6 = 41; NStudy7 = 856) using a validated emotion transition task, we found that lonely individuals hold atypical expectations about both their own and others’ likelihoods to transition between emotions and are less accurate at predicting others’ emotion transitions. While lonely participants relied less on their own emotion transition patterns when predicting others’ emotions, they also showed a response pattern that may reflect reduced confidence, suggesting they use a less stable or altered strategy for predicting others. Furthermore, lonely individuals perceived others as more volatile, expecting them to switch emotion valence states more frequently and be less likely to maintain the same emotion state. At the same time, they viewed themselves as more likely to shift away from positive states. Altogether, these findings suggest that loneliness is associated with unstable, inaccurate expectations of emotion continuity in others and a bias against sustaining positive emotions in the self—patterns that may contribute to challenges in social interactions and reinforce feelings of isolation.

2024

Similarity among friends serves as a social prior: The assumption that “Birds of a feather flock together” shapes social decisions and relationship beliefs

Miriam E. Schwyck*, Meng Du*, Yuchen Li, Luke J. Chang, and Carolyn Parkinson

Personality and Social Psychology Bulletin

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Social interactions unfold within networks of relationships. How do beliefs about others’ social ties shape—and how are they shaped by—expectations about how others will behave? Here, participants joined a fictive online game-playing community and interacted with its purported members, who varied in terms of their trustworthiness and apparent relationships with one another. Participants were less trusting of partners with untrustworthy friends, even after they consistently showed themselves to be trustworthy, and were less willing to engage with them in the future. To test whether people not only expect friends to behave similarly but also expect those who behave similarly to be friends, an incidental memory test was given. Participants were exceptionally likely to falsely remember similarly behaving partners as friends. Thus, people expect friendship to predict similar behavior and vice versa. These results suggest that knowledge of social networks and others’ behavioral tendencies reciprocally interact to shape social thought and behavior.

2023

Neural encoding of novel social networks: Evidence that perceivers prioritize others’ centrality

Miriam E. Schwyck, Meng Du, Pratishta Natarajan, John Andrew Chwe, and Carolyn Parkinson

Social Cognitive and Affective Neuroscience

Abstract URL

Knowledge of someone’s friendships can powerfully impact how one interacts with them. Previous research suggests that information about others’ real-world social network positions—e.g. how well-connected they are (centrality), ‘degrees of separation’ (relative social distance)—is spontaneously encoded when encountering familiar individuals. However, many types of information covary with where someone sits in a social network. For instance, strangers’ face-based trait impressions are associated with their social network centrality, and social distance and centrality are inherently intertwined with familiarity, interpersonal similarity and memories. To disentangle the encoding of the social network position from other social information, participants learned a novel social network in which the social network position was decoupled from other factors and then saw each person’s image during functional magnetic resonance imaging scanning. Using representational similarity analysis, we found that social network centrality was robustly encoded in regions associated with visual attention and mentalizing. Thus, even when considering a social network in which one is not included and where centrality is unlinked from perceptual and experience-based features to which it is inextricably tied in naturalistic contexts, the brain encodes information about others’ importance in that network, likely shaping future perceptions of and interactions with those individuals.

2021

Having more virtual interaction partners during COVID-19 physical distancing measures may benefit mental health

Razia S. Sahi*, Miriam E. Schwyck*, Carolyn Parkinson, and Naomi I. Eisenberger

Scientific Reports

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Social interactions play an extremely important role in maintaining health and well-being. The COVID-19 pandemic and associated physical distancing measures, however, restricted the number of people one could physically interact with on a regular basis. A large percentage of social interactions moved online, resulting in reports of “Zoom fatigue,” or exhaustion from virtual interactions. These reports focused on how online communication differs from in-person communication, but it is possible that when in-person interactions are restricted, virtual interactions may benefit mental health overall. In a survey conducted near the beginning of the COVID-19 pandemic (N2020 = 230), we found that having a greater number of virtual interaction partners was associated with better mental health. This relationship was statistically mediated by decreased loneliness and increased perceptions of social support. We replicated these findings during the pandemic 1 year later (N2021 = 256) and found that these effects held even after controlling for the amount of time people spent interacting online. Convergent with previous literature on social interactions, these findings suggest that virtual interactions may benefit overall mental health, particularly during physical distancing and other circumstances where opportunities to interact in-person with different people are limited.
The representational structure of mental states generalizes across target people and stimulus modalities

Miriam E. Weaverdyck, Mark A. Thornton, and Diana I. Tamir

NeuroImage

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Each individual experiences mental states in their own idiosyncratic way, yet perceivers can accurately under- stand a huge variety of states across unique individuals. How do they accomplish this feat? Do people think about their own anger in the same ways as another person’s anger? Is reading about someone’s anxiety the same as seeing it? Here, we test the hypothesis that a common conceptual core unites mental state representations across contexts. Across three studies, participants judged the mental states of multiple targets, including a generic other, the self, a socially close other, and a socially distant other. Participants viewed mental state stimuli in multiple modalities, including written scenarios and images. Using representational similarity analysis, we found that brain regions associated with social cognition expressed stable neural representations of mental states across both targets and modalities. Together, these results suggest that people use stable models of mental states across different people and contexts.

2020

Tools of the Trade Multivoxel pattern analysis in fMRI: A practical introduction for social and affective neuroscientists

Miriam E. Weaverdyck, Matthew D. Lieberman, and Carolyn Parkinson

Social Cognitive and Affective Neuroscience

Abstract URL

The family of neuroimaging analytical techniques known as multivoxel pattern analysis (MVPA) has dramatically increased in popularity over the past decade, particularly in social and affective neuroscience research using functional magnetic resonance imaging (fMRI). MVPA examines patterns of neural responses, rather than analyzing single voxel- or region-based values, as is customary in conventional univariate analyses. Here, we provide a practical introduction to MVPA and its most popular variants (namely, representational similarity analysis (RSA) and decoding analyses, such as classification using machine learning) for social and affective neuroscientists of all levels, particularly those new to such methods. We discuss how MVPA differs from traditional mass-univariate analyses, the benefits MVPA offers to social neuroscientists, experimental design and analysis considerations, step-by-step instructions for how to implement specific analyses in one’s own dataset and issues that are currently facing research using MVPA methods.

2019

The social brain automatically predicts others’ future mental states

Mark A. Thornton, Miriam E. Weaverdyck, and Diana I. Tamir

The Journal of Neuroscience

Abstract URL OSF

Social life requires people to predict the future: people must anticipate others’ thoughts, feelings, and actions in order to interact with them successfully. The theory of predictive coding suggests that the social brain may meet this need by automatically predicting others’ social futures. If so, when representing others’ current mental state, the brain should already start representing their future states. To test this hypothesis, we used functional neuroimaging to measure female and male human participants’ neural representations of mental states. Representational similarity analysis revealed that neural patterns associated with mental states currently under consideration resembled patterns of likely future states, more so than patterns of unlikely future states. This effect manifested in activity across the social brain network, and in medial prefrontal cortex in particular. Repetition suppression analysis also supported the social predictive coding hypothesis: considering mental states presented in predictable sequences reduced activity in the precuneus, relative to unpredictable sequences. In addition to demonstrating that the brain makes automatic predictions of others’ social futures, the results also demonstrate that the brain leverages a three-dimensional representational space to make these predictions. Proximity between mental states on the psychological dimensions of rationality, social impact, and valence explained much of the association between state-specific neural pattern similarity and state transition likelihood. Together, these findings suggest that the way the brain represents the social present gives people an automatic glimpse of the social future.

People represent their own mental states more distinctly than others’

Mark A. Thornton, Miriam E. Weaverdyck, Judith N. Mildner, and Diana I. Tamir

Nature Communications

Abstract URL OSF

One can never know the internal workings of another person—one can only infer others’ mental states based on external cues. In contrast, each person has direct access to the contents of their own mind. Here, we test the hypothesis that this privileged access shapes the way people represent internal mental experiences, such that they represent their own mental states more distinctly than the states of others. Across four studies, participants considered their own and others’ mental states; analyses measured the distinctiveness of mental state representations. Two fMRI studies used representational similarity analyses to demonstrate that the social brain manifests more distinct activity patterns when thinking about one’s own states vs. others’. Two behavioral studies complement these findings, and demonstrate that people differentiate between states less as social distance increases. Together, these results suggest that we represent our own mind with greater granularity than the minds of others.

Social life requires us to treat each person according to their unique disposition. To tailor our behavior to specific individuals, we must represent their idiosyncrasies. Here, we advance the hypothesis that our representations of other people reflect the mental states we perceive those people to habitually experience. We tested this hypothesis by measuring whether neural representations of people could be accurately reconstructed by summing state representations. Separate participants underwent functional MRI while considering famous individuals and individual mental states. Online participants rated how often each famous person experiences each state. Results supported the summed state hypothesis: frequency-weighted sums of state-specific brain activity patterns accurately reconstructed person-specific patterns. Moreover, the summed state account outperformed the established alternative—that people represent others using trait dimensions—in explaining interpersonal similarity. These findings demonstrate that the brain represents people as the sums of the mental states they experience.

The computational demands associated with navigating large, complexly bonded social groups are thought to have significantly shaped human brain evolution. Yet, research on social network representation and cognitive neuroscience have progressed largely independently. Thus, little is known about how the human brain encodes the structure of the social networks in which it is embedded. This review highlights recent work seeking to bridge this gap in understanding. While the majority of research linking social network analysis and neuroimaging has focused on relating neuroanatomy to social network size, researchers have begun to define the neural architecture that encodes social network structure, cognitive and behavioral consequences of encoding this information, and individual differences in how people represent the structure of their social world.

Rubik's Slide consists of a 3 × 3 grid of squares reminiscent of a face of Rubik's Cube. Squares may light up in one of two colors or remain unlit, and the goal is to use a series of moves to change a given initial pattern to a given end pattern. Viewing these moves as permutations, we use algebraic and graph-theoretic tools to analyze a simpler version of the puzzle and the three difficulty levels of Rubik's Slide. We determine the maximum number of moves an omniscient player would need to solve each variation, known as God's number.