Kevin Lewis’s research while affiliated with University of California, San Diego and other places

Forward flow is a new measure that quantifies free thought and predicts creativity (Gray et al., 2019). In his comment, Rossiter (2020) raises some conceptual and measurement concerns about this measure. We believe these concerns are specious, resting on fundamental misunderstandings about our aim and approach. This reply clarifies the nature of forward flow and dispels these concerns.

When the human mind is free to roam, its subjective experience is characterized by a continuously evolving stream of thought. Although there is a technique that captures people’s streams of free thought—free association—its utility for scientific research is undermined by two open questions: 1) How can streams of thought be quantified? and 2) Do such streams predict psychological phenomena? We resolve the first issue—quantification—by presenting a new metric, “forward flow,” that uses latent semantic analysis (LSA) to capture the semantic evolution of thoughts over time (i.e., how much present thoughts diverge from past thoughts). We resolve the second issue—prediction—by examining whether forward flow predicts creativity in the lab and the real-world. Our studies reveal that forward flow predicts creativity in college students (Study 1) and a representative sample of Americans (Study 2), even when controlling for intelligence. Studies also reveal that membership in real-world creative groups—performance majors (Study 3), professional actors (Study 4) and entrepreneurs (Study 5)—is predicted by forward flow, even when controlling for performance on divergent thinking tasks. Study 6 reveals that forward flow in celebrities’ social media posts (i.e., on Twitter) predicts their creative achievement. In addition to creativity, forward flow may also help predict mental illness, emotional experience, leadership ability, adaptability, neural dynamics, group productivity, and cultural success. We present open-access online tools at www.forwardflow.org for assessing and visualizing forward flow for both illustrative and large-scale data analytic purposes.

Agent-based modeling is a longstanding but under-used method that allows researchers to simulate artificial worlds for hypothesis testing and theory building. Agent-based models (ABMs) offer unprecedented control and statistical power by allowing researchers to precisely specify the behavior of any number of agents and observe their interactions over time. ABMs are especially useful when investigating group behavior or evolutionary processes, and can uniquely reveal non-linear dynamics and emergence—the process whereby local interactions aggregate into often-surprising collective phenomena, such as spatial segregation and relational homophily. We review several illustrative ABMs, describe the strengths and limitations of this method, and address two misconceptions about ABMs: reductionism and “you get out what you put in.” We also offer maxims for good and bad ABMs, give practical tips for beginner modelers, and include a list of resources and other models. We conclude with a 7-step guide to creating your own model.

Romantic partnership is often considered an optimal barometer of intergroup relations. To date, however, it has been challenging to distinguish the characteristics people prefer in a partner from the types of partners that are locally available. Online dating presents a new opportunity to address this puzzle. In this paper, I use behavioral data from a popular online dating site to answer three questions regarding preferences in the early stages of mate choice: First, to what extent do people prefer similarity versus status in a partner—and do these preferences vary by gender? Second, what is the relative importance of different types of preferences—and to what extent are apparent preferences for one characteristic merely a “by-product” of preferences for another characteristic with which the first is correlated? Third, do preferences vary at different moments of selection—and if so, how? These analyses not only provide a nuanced portrait of how interpersonal dynamics shape broader social structures—here, a network of romantic ties—but they recommend a future approach to mate choice that prioritizes processes over outcomes and more deeply engages the literature on gender, social networks, and symbolic boundaries.

“Digital footprints” is an attractive, useful, and increasingly popular metaphor for thinking about Big Data. In this essay, I elaborate on this metaphor to highlight three relatively basic fallacies in the way we tend to think about Big Data: first, that they contain information on complete populations, or “N = all”; second, that they contain recordings of naturalistic behavior; and third, that they can be understood devoid of context.

Psychological explanations of group genesis often require population heterogeneity in identity or other characteristics, whether deep (e.g., religion) or superficial (e.g., eye color). We used agent-based models to explore group genesis in homogeneous populations and found robust group formation with just two basic principles: reciprocity and transitivity. These emergent groups demonstrated in-group cooperation and out-group defection, even though agents lacked common identity. Group formation increased individual payoffs, and group number and size were robust to varying levels of reciprocity and transitivity. Increasing population size increased group size more than group number, and manipulating baseline trust in a population had predictable effects on group genesis. An interactive demonstration of the parameter space and source code for implementing the model are available online.

Despite the tremendous amount of attention that has been paid to the internet as a tool for civic engagement, we still have little idea how “active” is the average online activist or how social networks matter in facilitating electronic protest. In this paper, we use complete records on the donation and recruitment activity of 1.2 million members of the Save Darfur “Cause” on Facebook to provide a detailed first look at a massive online social movement. While both donation and recruitment behavior are socially patterned, the vast majority of Cause members recruited no one else into the Cause and contributed no money to it-suggesting that in the case of the Save Darfur campaign, Facebook conjured an illusion of activism rather than facilitating the real thing.

Significance Racial segregation in romantic networks is a robust and ubiquitous social phenomenon—but one we understand remarkably poorly. In this paper, I analyze a large network of interactions among users of a popular online dating site. First, I find that users from all racial backgrounds are equally likely or more likely to cross a racial boundary when reciprocating than when initiating romantic contact. Second, I find that certain subsets of users who receive—and reply to—a cross-race message initiate more new interracial exchanges in the short-term future than they would have otherwise. These findings illustrate an important mechanism whereby racial biases in assortative mating may be reduced temporarily by the actions of others.