Cognitive mechanisms underlying the spread of conventions in communities

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This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Thomas Griffiths and Adele Goldberg at Princeton University, this postdoctoral fellowship award supports an early career scientist investigating how linguistic conventions spread through communities. To successfully communicate using language, speakers must share consistent conventions about the meanings of words. As modern communication media has revealed, however, these linguistic conventions are often in flux: novel meanings rapidly spread across social networks and become widely adopted. At the same time, speakers across different sub-communities may vary substantially in which conventions they find meaningful (e.g. the acronyms filling scientific journals). This project investigates the adaptive cognitive mechanisms that allow speakers to navigate this complex landscape of meaning, and how these mechanisms may in turn explain how conventions spread within and across communities. The proposed model of how social expectations are represented and generalized lays the groundwork for applying formal models to calibrate interventions on social norms and conventions more broadly. These interventions hold promise as solutions to large-scale social problems ranging from public health concerns like smoking cigarettes to participation in collective actions like voting. Additionally, this work could contribute to understanding how linguistic conventions vary across ethnic or SES-based communities. Using a series of large-scale behavioral studies of referential communication on social networks and state-of-the-art computational cognitive models of language use, the proposed work tests a novel hypothesis about how speakers rationally update and deploy their mental representations of linguistic conventions in different contexts. This hypothesis is made formally precise in a hierarchical Bayesian model and rigorously compared against previous models using new behavioral data. First, the project will examine the mechanisms of generalization supporting the spread of new linguistic conventions within a community. Second, the project will investigate how speakers learn and flexibly switch between different sets of conventions in different communities. Third, the hierarchical model will be extended using a deep neural network architecture to produce finer-grained predictions about natural language descriptions, thus enabling further theory development. This project uses ideas from computational cognitive science to make new connections between two interdisciplinary traditions (psycholinguistics and cultural evolution) that deepen our understanding of how large groups of people coordinate their beliefs to better understand one another.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.