Inflammation and Intergroup Bias
Creators: name: Madison, Annelise
name: Kiecolt-Glaser, Janice
name: Way, Baldwin
name: Ratner, Kyle
name: Andridge, Rebecca
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: Activation of the biological immune system requires a steep metabolic cost. Additionally, if inflammation does not adequately resolve after the infection, it can hasten the cellular aging process (i.e., "inflamm-aging") and help to onset chronic disease (Franceschi et al., 2000). To circumvent the need for its activation, humans have a set of sensory, affective, cognitive, and behavioral mechanisms, dubbed the behavioral immune system, that together can mitigate pathogen exposure and ultimately infection (Schaller and Park, 2011). Behaviors can be proactive, such as mask wearing or frequent handwashing to generally minimize the risk of infection, or they can be reactive to a detected pathogenic threat, such as avoiding someone who appears to be ill. Importantly, there are costs and benefits associated with both proactive and reactive measures, and one major cost is lost social connection. Thus, the behavioral immune system is functionally flexible, in that an individual weighs out the likelihood of exposure as well as their own vulnerability to infection (Schaller and Park, 2011). In the absence of chronic immunosuppression in which the individual always is highly vulnerable to infection, vulnerability waxes and wanes. The behavioral immune system is more active during times of high vulnerability. For example, one cross-sectional study showed that mothers in the first trimester of pregnancy, when the immune system is downregulated to avoid harming the fetus, expressed in-group favoritism more so than mothers in their second and third trimesters when immune function is more robust (Navarrete et al., 2007). Indeed, the behavioral immune system can transiently boost prejudice against perceived out-group members, as they may carry novel pathogens (Faulkner et al., 2004). Therefore, one cost of behavioral immune system activation is xenophobia and out-group avoidance. In the current study, we will use a novel behavioral paradigm to examine whether exposure to a mild inflammatory stimulus – a typhoid vaccine – transiently increases implicit or explicit out-group avoidance, compared to a placebo saline injection. Given that the typhoid vaccine provokes a metabolically-expensive acute inflammatory response, we hypothesize that participants will be more avoidant of out-group faces, particularly those that express negative emotion, following the typhoid vaccine, compared to the placebo injection. Moreover, we expect that those who have greater inflammatory rises throughout the study visit will display more avoidance of out-group members. The proposed secondary analyses will be conducted with data that has already been collected from a within-subjects randomized, crossover trial. In this trial, middle-aged women completed two visits, and received the placebo injection or typhoid vaccine, in a randomized sequential order, one at each visit. The within-subjects design is critical for the research question of interest because it inherently controls for inter-individual differences in prejudice by comparing each woman’s performance at the vaccine visit to her performance at the placebo visit. Seven hours after the injection, participants completed the 15-minute implicit and five-minute explicit social behavior tasks, respectively. During these tasks, participants were presented with several emotional facial stimuli, and their response times to approach or avoid the stimuli (implicit task) as well as their explicit ratings concerning whether they wanted to take steps toward or away from the stimuli (explicit task) were measured. Approximately one-third of the stimuli were Black, and the rest were White. Half of the stimuli were male faces. Overall, 172 women completed the study, including 159 White women, 10 Black women, one Asian woman, and two who identified as “Mixed.” One White woman identified as Latina. Due to the small group sizes for the ethnic and racial minority groups, only non-Hispanic White women will be included in the proposed analyses, yielding a total sample size of 158. Thus, in this context, there are two dimensions of out-group membership – race (Black faces) and gender (male faces). For the primary analyses, we are interested in the effects of (1) baseline inflammation, (2) the vaccine/placebo injection, (3) and change in inflammation throughout the day on (1) implicit task performance (response times), (2) explicit task performance (ratings) – and each of these predictor/outcome combinations will be examined in separate linear mixed effects models. All models will include data from the vaccine and placebo visits, except the models with baseline inflammation predicting implicit and explicit task performance, which will include only the placebo visit. For models with implicit task performance as the outcome, of primary relevance is the predictor of interest in interaction with task-related dimensions [stimulus gender, stimulus race, stimulus emotion, trial type (approach/avoid)]. For models with explicit ratings as the outcome, of primary relevance is the predictor of interest with the task-related factors of stimulus gender, stimulus race, and stimulus emotion. Lower-order interactions and main effects will be examined if the higher-order interaction is not significant. Significant interactions will be probed at each injection type, as well as each category of the task-relevant features, and interactions involving continuous inflammatory markers will be probed at ±1 SD. Due to the multiple visits per participant and repeated measurements within a visit, all analyses will use linear mixed effects models. For models that use only data from the placebo visit, we will use linear mixed effects models with random subject-specific intercepts. For all other models (involving repeated measurements per visit and multiple visits per participant), we will use linear mixed effects models with random subject-specific visit effects that are allowed to be correlated, thus modeling the different magnitude of within-subject correlations within and across visits. All models will control for age and education.