Spring 2021 Workshops
When: Mondays 10:00–11:30 a.m. (unless otherwise noted)
Where: Workshops were held via Zoom due to COVID-19
Monday, April 12, 2021
Hyo Gweon
Stanford University
"Curious, cooperative, and communicative: How we learn from others and help others learn"
Humans are not the only species that learns from others, but only humans learn and communicate in rich, diverse social contexts, build repertoires of abstract, structured knowledge. What makes human social learning so distinctive, powerful, and smart? In this talk, I will present a series of studies that reveal the remarkably curious minds of young children, not only about the physical world but also about others and themselves; children are curious about what others do & what their actions mean, what others know & what they ought to know, and even what others think of them and how to change their beliefs. The results collectively paint a picture of young children as active social learners who voraciously yet intelligently gather useful information from others to learn about the world, and generously share what they know with those around them.
Pre-reading: "Inferential Social Learning: How humans learn from others and help others learn"
Wednesday, April 28, 2021
Nickola Overall
University of Auckland
"Behavioral Variability and the Social Benefits vs. Costs of Anger and Hostility"
Determining how social behavior affects social partners, and identifying how contextual factors shape these interpersonal dynamics, are cornerstones of social psychology. Relationship science has offered important advances in the study of social behavior because the dyadic and ongoing nature of most intimate relationships offers a unique social milieu to assess how one person’s behavior impacts another person’s outcomes across time. Yet, the comprehensive methods used to assess behavioral dynamics within relationships overlook what I propose to be an important determinant of the longitudinal impact of social behavior—behavioral variability. In this workshop, I will invite you to discuss the meaning and value of assessing variability in social behavior. To illustrate, I will provide a review of our research showing that (a) anger and hostility within close relationships can sometime have social benefits by helping to resolve relationship problems, (b) these potential benefits only occur when the expression of anger and hostility is diagnostic of problems that need to be addressed, and (c) behavioral variability may be an important global ingredient that indexes whether anger and hostility is more versus less sensitive to situational demands.
Pre-read 1 | Pre-read 2
Monday, May 10, 2021, *9:40-11 a.m.* Rescheduled to Fall Quarter
Michele Gelfand
University of Maryland
Monday, May 17, 2021
Steven Roberts
Stanford University
"Racism: A Developmental Story"
Racism – often conceptualized as disliking or mistreating others because of their race – is a system of advantage based on race. In this talk, I will share my personal and professional experiences within this system, and highlight how the two have developed hand in hand. Specifically, I will address racism in our categories, churches, relationships, and science. In doing so, I will aim to make three broader points. First, racism shapes our lives in ways that are often unappreciated and unrecognized. Second, racism shapes our lives from childhood well into adulthood and beyond. Third, our own experiences with racism (and race) inform who and what we study. I will conclude, as a human and as a psychologist, with recommendations for an anti-racist future.
Pre-reading: "The Psychology of American Racism"
Monday, May 24, 2021
Uri Simonsohn
ESADE
"Rethinking Interactions: How to Interpret Interactions from Studies Run in the Real (Non-Linear) World"
Hypotheses involving interactions, where one variable modifies the association between another two, are common in both experimental and observational social science. Interactions are typically tested relying on models that assume effects are linear, e.g., estimating a model like y = ax + bz + cxz. Linearity assumptions are common, but they are particularly problematic for estimating interaction effects. They may increase the false-positive rate for an interaction to over 50%, lead point estimates to always have the wrong sign, and makes follow-up analyses known a “simple-slopes”, “Johnson-Neyman procedure”, or “spotlight/floodlight” nearly hopeless. In this article I propose a revised toolbox for studying interaction effects that, depending on the data and research question at hand, ranges from simply comparing means across data subsets, to estimating flexible non-linear models (e.g., GAM) instead of linear ones. The goal is curvilinear robust estimation and testing (CREST) of interactions, for most study designs, with the smallest modification of current practice possible.