Publications

2023

1. 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 Feb 2023

   Abstract URL

   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.

2. 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 Feb 2023

   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

1. The representational structure of mental states generalizes across target people and stimulus modalities

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

   NeuroImage Feb 2021

   Abstract URL

   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.

2. 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 Feb 2021

   Abstract URL

   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.

2020

1. 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 Jun 2020

   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

1. The social brain automatically predicts others’ future mental states

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

   The Journal of Neuroscience Jun 2019

   Abstract URL

   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. SIGNIFICANCE STATEMENT When you see a ball in flight, your brain calculates not just its static visual features like size and shape, but also predicts its future trajectory. Here we examined whether the same might hold true in the social world: when we see someone flying into a rage, does our brain automatically predict their social trajectory? In this study, we scanned participants’ brain activity while they judged others’ mental states. We found that neural activity associated with a given state resembled activity associated with likely future states. Additionally, unpredictable sequences of states evoked more brain activity than predictable sequences — consistent with monitoring for, and updating from, prediction errors. These results suggest that the social brain automatically predicts others’ future mental states.

2. 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 Jun 2019

   Abstract URL

   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.

3. The brain represents people as the mental states they habitually experience

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

   Nature Communications Jun 2019

   Abstract URL

   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.

2018

1. The neural representation of social networks

   Miriam E. Weaverdyck, and Carolyn Parkinson

   Current Opinion in Psychology Dec 2018

   Abstract URL

   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.

2014

1. On God’s Number(s) for Rubik’s Slide

   Michael A Jones, Brittany C Shelton, and Miriam E. Weaverdyck

   The College Mathematics Journal Sep 2014

   URL