Anneke Buffone

The Neurogenetics of Nice Receptor Genes for Oxytocin and Vasopressin Interact With Threat to Predict Prosocial Behavior

Oxytocin, vasopressin, and their receptor genes influence prosocial behavior in the laboratory and in the context of close relationships. These peptides may also promote social engagement following threat. However, the scope of their prosocial effects is unknown. We examined oxytocin receptor (OXTR) polymorphism rs53576, as well as vasopressin receptor 1a (AVPR1a) polymorphisms rs1 and rs3 in a national sample of U.S. residents (n = 348). These polymorphisms interacted with perceived threat to predict engagement in volunteer work or charitable activities and commitment to civic duty. Specifically, greater perceived threat predicted engagement in fewer charitable activities for individuals with A/A and A/G genotypes of OXTR rs53576, but not for G/G individuals. Similarly, greater perceived threat predicted lower commitment to civic duty for individuals with one or two short alleles for AVPR1a rs1, but not for individuals with only long alleles. Oxytocin, vasopressin, and their receptor genes may significantly influence prosocial behavior and may lie at the core of the caregiving behavioral system.

Empathy, Target Distress, and Neurohormone Genes Interact to Predict Aggression for Others–Even Without Provocation

Can empathy for others motivate aggression on their behalf? This research examined potential predictors of empathy-linked aggression including the emotional state of empathy, an empathy target’s distress state, and the function of the social anxiety-modulating neuropeptides oxytocin and vasopressin. In Study 1 (N = 69), self-reported empathy combined with threat to a close other and individual differences in genes for the vasopressin receptor (AVPR1a rs3) and oxytocin receptor (OXTR rs53576) to predict self-reported aggression against a person who threatened a close other. In Study 2 (N = 162), induced empathy for a person combined with OXTR variation or with that person’s distress and AVPR1a variation led to increased amount of hot sauce assigned to that person’s competitor. Empathy uniquely predicts aggression and may do so by way of aspects of the human caregiving system in the form of oxytocin and vasopressin.

Real Men Don’t Say “Cute”: Using Automatic Language Analysis to Isolate Inaccurate Aspects of Stereotypes

People associate certain behaviors with certain social groups. These stereotypical beliefs consist of both accurate and inaccurate associations. Using large-scale, data-driven methods with social media as a context, we isolate stereotypes by using verbal expression. Across four social categories—gender, age, education level, and political orientation—we identify words and phrases that lead people to incorrectly guess the social category of the writer. Although raters often correctly categorize authors, they overestimate the importance of some stereotype-congruent signal. Findings suggest that data-driven approaches might be a valuable and ecologically valid tool for identifying even subtle aspects of stereotypes and highlighting the facets that are exaggerated or misapplied.

The noetic quality: A multimethod exploratory study.

Religious, spiritual, and mystical experiences (RSMEs) are often described as having a noetic quality, or the compelling sense that the experience feels “real.” In this exploratory, multimethod study, 701 participants completed questions about the subjective qualities of their RMSEs, reported the impact of their RSMEs on various life domains, and provided written descriptions of their experiences for quantitative linguistic analysis. The majority of participants (69%) reported that their RSMEs felt “more real than their usual sense of reality.” This quality of realness was associated with positive self-reported impacts on family life (r = .16), health (r = .22), sense of purpose (r = .29), spirituality (r = .30), and reduced fear of death (r = .24). Participants who reported experiences as feeling more real used more language referring to connection, a greater whole, and certainty (“love,” “all,” “and,” “everything”) and fewer first-person pronouns, cognitive processes, and tentativeness (“I,” “me,” “think,” “probably”). These findings provide insight into the noetic quality, as well as the psychological characteristics that may underlie the noetic quality of RSMEs.

There But for the Grace of God Counterfactuals Influence Religious Belief and Images of the Divine

Past research suggests that thinking counterfactually leads to a perception that major life events are fated or predetermined. We hypothesized that a perception that events are fated will activate perceptions that God played a role in the event, which will increase feelings of religiousness. Because most people view God as a positive influence, we hypothesized that this would only happen when imagining how events might have turned out worse (i.e., downward counterfactuals). Across two experiments, we examined the hypothesis that thinking counterfactually increases religiosity. The studies supported our predictions. Generating counterfactuals increased belief in God and religiosity across several variables. Furthermore, evidence was found for the proposed pathway. In summary, the studies provided strong and consistent empirical support for counterfactual thinking’s role in religious faith and for this effect to be due to increased perceptions of God’ role in the event. Implications for religion, cognition, and counterfactual thinking are discussed.

The language of religious affiliation: social, emotional, and cognitive differences

Religious affiliation is an important identifying characteristic for many individuals and relates to numerous life outcomes including health, well-being, policy positions, and cognitive style. Using methods from computational linguistics, we examined language from 12,815 Facebook users in the United States and United Kingdom who indicated their religious affiliation. Religious individuals used more positive emotion words (β = .278, p < .0001) and social themes such as family (β = .242, p < .0001), while nonreligious people expressed more negative emotions like anger (β = −.427, p < .0001) and categories related to cognitive processes, like tentativeness (β = −.153, p < .0001). Nonreligious individuals also used more themes related to the body (β = −.265, p < .0001) and death (β = −.247, p < .0001). The findings offer directions for future research on religious affiliation, specifically in terms of social, emotional, and cognitive differences.

Recognizing Counterfactual Thinking in Social Media Texts.

Counterfactual statements, describing events that did not occur and their consequents, have been studied in areas including problem-solving, affect management, and behavior regulation. People with more counterfactual thinking tend to perceive life events as more personally meaningful. Nevertheless, counterfactuals have not been studied in computational linguistics. We create a counterfactual tweet dataset and explore approaches for detecting counterfactuals using rule-based and supervised statistical approaches. A combined rule-based and statistical approach yielded the best results (F1 = 0.77) outperforming either approach used alone.

Does ‘well-being’ translate on Twitter?

We investigate whether psychological wellbeing translates across English and Spanish Twitter, by building and comparing source language and automatically translated weighted lexica in English and Spanish. We find that the source language models perform substantially better than the machine translated versions. Moreover, manually correcting translation errors does not improve model performance, suggesting that meaningful cultural information is being lost in translation. Further work is needed to clarify when automatic translation of well-being lexica is effective and how it can be improved for crosscultural analysis.

Empathic Concern and the Desire to Help as Separable Components of Compassionate Responding

When do people experience versus regulate responses to compassion-evoking stimuli? We hypothesized that compassionate responding is composed of two factors—empathic concern and the desire to help—and that these would be differentially affected by perspective taking and self-affirmation. Exploratory (Study 1) and confirmatory (Study 2) factor analyses indicated that a compassion measure consisted of two factors corresponding to empathic concern and the desire to help. In Study 1 (N = 237), participants with high emotion regulation ability reported less empathic concern for multiple children than for one, but perspective taking prevented this effect. In Study 2 (N = 155), participants reported less desire to help multiple children, but only in the presence of self-affirmation. In both the studies, empathic concern predicted greater distress while the desire to help predicted greater chances of donating. Compassionate responding may consist of two separable facets that collapse under distinct conditions and that predict distinct outcomes.

Can Twitter be used to predict county excessive alcohol consumption rates

Objectives The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county. Methods Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis. Results Twitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. ‘ready gettin leave’) can explain much of the variance associated between socioeconomics and excessive alcohol consumption. Conclusions Twitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained.