Marcos Gallo


No strong evidence that social network index is associated with gray matter volume from a data-driven investigation.

With Chujun Lin, Umit Keles U, Mike Tyszka, Lynn Paul, and Ralph Adolphs.

Recent studies in adult humans have reported correlations between individual differences in people's Social Network Index (SNI) and gray matter volume (GMV) across multiple regions of the brain. However, the cortical and subcortical loci identified are inconsistent across studies. These discrepancies might arise because different regions of interest were hypothesized and tested in different studies without controlling for multiple comparisons, and/or from insufficiently large sample sizes to fully protect against statistically unreliable findings. Here we took a data-driven approach in a pre-registered study to comprehensively investigate the relationship between SNI and GMV in every cortical and subcortical region, using three predictive modeling frameworks. We also included psychological predictors such as cognitive and emotional intelligence, personality, and mood. In a sample of healthy adults (n = 92), neither multivariate frameworks (e.g., ridge regression with cross-validation) nor univariate frameworks (e.g., univariate linear regression with cross-validation) showed a significant association between SNI and any GMV or psychological feature after multiple comparison corrections (all R-squared values ≤ .1). These results emphasize the importance of large sample sizes and hypothesis-driven studies to derive statistically reliable conclusions, and suggest that future meta-analyses will be needed to more accurately estimate the true effect sizes in this field.

The Golden Age of Social Science

With Anastasia Buyalskaya and Colin Camerer

In this short essay, we argue that social science is entering a golden age, marked by explosive growth in new data and analytic methods, interdisciplinarity, and a recognition that both of those ingredients are necessary to solve hard problems. Two examples are given to illustrate these themes, which are behavioral economics and social networks. Numerous other specific study examples are then given. We also address the challenges that accompany the three positive trends, which include informatics, career incentives, and the search for unifying frameworks.

A Meta-Analysis of Bribery Experiments

An Interdisciplinary Approach to Predicting Unequal Treatment

With Colin Camerer, Ming Hsu, and Adrianna Jenkins.

Disparities in outcomes across social groups are found in nearly every domain of modern human society, including education, the labor market, and healthcare. Whether on the basis of gender, ethnicity, age or other markers, group-based differences in how people treat others are known to arise even when social group information is irrelevant and even when people explicitly reject social stereotypes. Despite progress in documenting these disparities, much remains unknown about their origins. The current research focuses on the role of individual human decision-making in producing societal-level outcomes. Specifically, the investigators aim to leverage complementary strengths of behavioral economics, social psychology, and cognitive neuroscience to uncover systematic patterns of individual human decision-making that, in aggregate, contribute to societal treatment disparities. The primary goal is to characterize the origins of unequal treatment with sufficient precision to support accurate, context-specific predictions of how people will treat members of different social groups.

Contact Hypothesis: Effects of Full-time LDS Mission Service on Discriminatory Attitudes

With Talon Hicken, Brock Kirwan, and Colin Camerer.

The extent to which international experiences change attitudes and behavior is mostly unexplored. We propose to answer this question by analyzing the changes in discriminatory attitudes and prosocial behavior of individuals called as missionaries of the Church of Jesus Christ of Latter-day Saints, who are assigned to work for up to two years in varying regions of the world. Our design is equipped to find better causal identification with as-if random assignment and no self-selection. Unlike any previous study, our design will explore the effects of cross-border contact on altruistic behavior and attitudes towards an out-group. In order to address this question, subjects will participate in three tasks, a dictator game task to measure prosociality (Hutcherson, Bushong, & Rangel, 2015), a perceptual bias task (Duchaine & Nakayama, 2006), and an implicit association task (IAT) (Greenwald, Nosek, & Banaji, 2003; Nosek, Greenwald, & Banaji, 2007).