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1. Social-Structure Learning

   https://doi.org/10.1177/0963721420924481  

   Gershman, Samuel J.; Cikara, Mina (October 2020, Current Directions in Psychological Science)

   null (Ed.)

   Social-structure learning is the process by which social groups are identified on the basis of experience. Building on models of structure learning in other domains, we formalize this problem within a Bayesian framework. According to this framework, the probabilistic assignment of individuals to groups is computed by combining information about individuals with prior beliefs about group structure. Experiments with adults and children provide support for this framework, ruling out alternative accounts based on dyadic similarity. More broadly, we highlight the implications of social-structure learning for intergroup cognition, stereotype updating, and coalition formation. 

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2. Synchronized affect in shared experiences strengthens social connection

   https://doi.org/10.1038/s42003-023-05461-2  

   Cheong, Jin Hyun; Molani, Zainab; Sadhukha, Sushmita; Chang, Luke J. (October 2023, Communications Biology)

   Abstract People structure their days to experience events with others. We gather to eat meals, watch TV, and attend concerts together. What constitutes a shared experience and how does it manifest in dyadic behavior? The present study investigates how shared experiences—measured through emotional, motoric, physiological, and cognitive alignment—promote social bonding. We recorded the facial expressions and electrodermal activity (EDA) of participants as they watched four episodes of a TV show for a total of 4 h with another participant. Participants displayed temporally synchronized and spatially aligned emotional facial expressions and the degree of synchronization predicted the self-reported social connection ratings between viewing partners. We observed a similar pattern of results for dyadic physiological synchrony measured via EDA and their cognitive impressions of the characters. All four of these factors, temporal synchrony of positive facial expressions, spatial alignment of expressions, EDA synchrony, and character impression similarity, contributed to a latent factor of a shared experience that predicted social connection. Our findings suggest that the development of interpersonal affiliations in shared experiences emerges from shared affective experiences comprising synchronous processes and demonstrate that these complex interpersonal processes can be studied in a holistic and multi-modal framework leveraging naturalistic experimental designs. 

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3. Respect the code: Speakers expect novel conventions to generalize within but not across social group boundaries

   Hawkins, Robert D.; Liu, I; Goldberg, Adele E.; Griffiths, Thomas L. (January 2021, Proceedings of the Annual Conference of the Cognitive Science Society)

   null (Ed.)

   Speakers use different language to communicate with partners in different communities. But how do we learn and represent which conventions to use with which partners? In this paper, we argue that solving this challenging computational problem requires speakers to supplement their lexical representations with knowledge of social group structure. We formalize this idea by extending a recent hierarchical Bayesian model of convention formation with an intermediate layer explicitly representing the latent communities each partner belongs to, and derive predictions about how conventions formed within a group ought to extend to new in-group and out-group members. We then present evidence from two behavioral experiments testing these predictions using a minimal group paradigm. Taken together, our findings provide a first step toward a formal framework for understanding the interplay between language use and social group knowledge. 

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4. A self-supervised domain-general learning framework for human ventral stream representation

   https://doi.org/10.1038/s41467-022-28091-4  

   Konkle, Talia; Alvarez, George A. (January 2022, Nature Communications)

   Abstract Anterior regions of the ventral visual stream encode substantial information about object categories. Are top-down category-level forces critical for arriving at this representation, or can this representation be formed purely through domain-general learning of natural image structure? Here we present a fully self-supervised model which learns to represent individual images, rather than categories, such that views of the same image are embedded nearby in a low-dimensional feature space, distinctly from other recently encountered views. We find that category information implicitly emerges in the local similarity structure of this feature space. Further, these models learn hierarchical features which capture the structure of brain responses across the human ventral visual stream, on par with category-supervised models. These results provide computational support for a domain-general framework guiding the formation of visual representation, where the proximate goal is not explicitly about category information, but is instead to learn unique, compressed descriptions of the visual world. 

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5. Faculty allyship: Differences by gender, race, and rank at a single U.S. University

   https://doi.org/10.1111/gwao.12988  

   Ro, Hyun Kyoung; Campbell‐Jacobs, Blaze; Broido, Ellen M.; Hanasono, Lisa K.; O’Neil, Deborah A.; Yacobucci, Margaret M.; Root, Karen V. (March 2023, Gender, Work & Organization)

   Abstract Within the growing literature about allyship in the workplace, few studies have specifically addressed faculty allyship for faculty colleagues. Previous studies on faculty allyship for inclusive academic environments address only men's contributions as allies. Using an expansive definition of faculty allyship and including any faculty members with membership in at least one dominant social group, we sought to better understand how faculty members perceive allyship, their concerns about allyship, and how those perceptions vary by gender, race, and rank. We examined the responses of faculty who participated in an allyship training program that was offered at a university in Ohio, USA as part of a National Science Foundation ADVANCE grant intended to reduce gender inequity among science, technology, engineering, and mathematics faculty. We framed this study by employing Hardiman et al.'s (2007) three‐dimensional matrix of oppression and used a mixed‐method research design. Participants' primary concerns about engaging in allyship related to their academic rank. We offer several implications for policies, practices, and future research on faculty allyship for faculty colleagues by considering positional power and rank as well as race and gender. 

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