Current research in ecological and social psychology最新文献

Sometimes people help one another expecting to be repaid, while at other times people help without an expectation of repayment. What might underlie this difference in expectations of repayment? We investigate this question in a nationally representative sample of US adults (N = 915), and find that people are more likely to expect repayment when needs are perceived to be more predictable. We then replicate these findings in a new sample of US adults (N = 417), and show that people have higher expectations of repayment when needs are perceived to be more predictable because people assign greater responsibility to others for experiencing such predictable needs (e.g., needing money for utilities). This is consistent with previous work based on smaller-scale societies, which shows that the predictability of needs influences expectations of repayment. Our results also add to this previous work by (1) showing that the positive relationship between predictability of needs and expectations of repayment previously found in smaller-scale communities is generalizable to the US population, and (2) showing that attributions of responsibility partially mediate this relationship. This work shows that the predictability of needs and attributions of responsibility for that need are important factors underlying the psychology of helping in times of need.

Prior research on eco-anxiety, or anxiety and worry about mounting environmental issues, has almost exclusively relied on cross-sectional trait reports. Consequently, little is known about how it is related to focal outcomes, such as well-being (e.g., happiness, meaning in life) and pro-environmental behavior, over time in daily life. To help address this issue, we conducted a preregistered daily diary study, wherein Canadian undergraduates (N = 132) provided trait reports and two weeks of daily reports (n = 1439) on eco-anxiety, positive and negative affect, meaning in life, and pro-environmental behavior. At the trait level, average scores on eco-anxiety were fairly low; yet, higher scores were associated with less positive affect and more negative affect and pro-environmental behavior. Average scores on eco-anxiety were even lower at the state level but nonetheless exhibited notable within-person variability. On days that people felt greater eco-anxiety, they also reported greater negative affect and pro-environmental behavior. Lagged analyses from one day to the next provided some evidence that eco-anxiety increases future negative affect. No significant relationships between eco-anxiety and meaning in life emerged at both levels of analysis. Together, these findings demonstrate that eco-anxiety can be productively conceived of—and studied as—both a trait and a state.

Single-use plastic has devastating impacts on the natural environment and scalable theory-based interventions are urgently needed to curb plastic consumption. The purpose of this study is to test the impact of two brief plastic reduction interventions on consumption on college campuses and whether these effects will be mediated by changes in the extended Theory of Planned Behavior (TPB) model consisting of attitudes, subjective norms, perceived behavioral control, moral norms, descriptive norms, and self-identity. 375 undergraduate students (77% female) from two colleges in the southeastern US completed baseline measures of plastic consumption beliefs and behavior in line with the extended TPB model. Participants were then randomized into one of three groups - control group (n = 152), app intervention group (who tracked plastic behavior on a mobile phone app for a week and received TPB-based daily messages via push notifications; n = 89), or pledge intervention group (who made a pledge to reduce plastic for a week and received TPB-based daily messages via email; n = 134). All participants completed the survey again after the intervention week. Results showed that the extended TPB model along with the intervention condition significantly predicted changes in plastic behavior over the week, (R² = 0.24, p < .001). Additionally, mediation analysis revealed that the pledge group (compared to the control group) reported a significant decrease in plastic consumption over the week-long intervention, with indirect effects via changes in attitudes, perceived behavioral control, and descriptive norms. The app group (compared to the control group) decreased plastic consumption less and showed no change to the extended TPB constructs. Results suggest that plastic reduction interventions that influence the extended TPB constructs can be expected to have corresponding changes in plastic consumption behavior.

Full national coverage below the state level is difficult to attain through survey-based data collection. Even the largest survey-based data collections, such as the CDC's Behavioral Risk Factor Surveillance System or the Gallup-Healthways Well-being Index (both with more than 300,000 responses p.a.) only allow for the estimation of annual averages for about 260 out of roughly U.S. 3,000 counties when a threshold of 300 responses per county is used. Using a relatively high threshold of 300 responses gives substantially higher convergent validity–higher correlations with health variables–than lower thresholds but covers a reduced and biased sample of the population. We present principled methods to interpolate spatial estimates and show that including large-scale geotagged social media data can increase interpolation accuracy. In this work, we focus on Gallup-reported life satisfaction, a widely-used measure of subjective well-being. We use Gaussian Processes (GP), a formal Bayesian model, to interpolate life satisfaction, which we optimally combine with estimates from low-count data. We interpolate over several spaces (geographic and socioeconomic) and extend these evaluations to the space created by variables encoding language frequencies of approximately 6 million geotagged Twitter users. We find that Twitter language use can serve as a rough aggregate measure of socioeconomic and cultural similarity, and improves upon estimates derived from a wide variety of socioeconomic, demographic, and geographic similarity measures. We show that applying Gaussian Processes to the limited Gallup data allows us to generate estimates for a much larger number of counties while maintaining the same level of convergent validity with external criteria (i.e., N = 1,133 vs. 2,954 counties). This work suggests that spatial coverage of psychological variables can be reliably extended through Bayesian techniques while maintaining out-of-sample prediction accuracy and that Twitter language adds important information about cultural similarity over and above traditional socio-demographic and geographic similarity measures. Finally, to facilitate the adoption of these methods, we have also open-sourced an online tool that researchers can freely use to interpolate their data across geographies.

Data generated by social media platforms such as Twitter provide a unique opportunity to examine large-scale public communication produced spontaneously and in real-time, and consequently, contribute to comprehensively understanding the complex set of attitudes towards refugees. The present study utilises a qualitative approach to explore host members’ attitudes towards refugees on social media during the global crisis. In particular, we examine perceptions of a broader population, Twitter users, and analyse public reactions towards Afghan refugees expressed within tweets during the pandemic in Turkey as a response to the 2021 Afghan refugee influx. We analysed and coded 2,686 tweets using qualitative content analysis based on four main categories: 1) Representations of Afghan refugees and immigrants, 2) main causes for rejecting them, 3) taking action against them and their supporters, and 4) positive attitudes towards them. Results highlighted the prevalence of negative perceptions about, dehumanisation of, and racism towards Afghan refugees in Turkey mainly due to perceived realistic, symbolic, demographic, security, moral, and health threats from them. However, the findings also showed there were positive, albeit very few, attitudes towards Afghan refugees in the form of solidarity, help and empathy. We discuss these findings in relation to the existing literature on host society members’ attitudes towards refugees and immigrants and why we need to qualitatively examine these attitudes on social media.

Activities that require mentalizing, such as meditating or reading literary fiction, have been shown to enhance Theory of Mind (ToM) (e.g., Tan, Lo, and Macrae, 2014; Kidd & Castano, 2013). In this article, we conjecture that relatively greater mentalizing effort also occurs when individuals can only listen in on a conversation, compared to those who actively participate in it. Therefore, those who listen in should show better performance on subsequent ToM tasks. Participants (N = 77) were divided into triads and randomly assigned to be Interlocutors (a Director and a Matcher) or Listeners. In each triad, Interlocutors completed a collaborative figure matching task while talking to each other, while Listeners completed the same task while listening to live audio of the Interlocutors' conversation. All participants then completed two Theory of Mind measures. Multivariate analyses show that Listeners outperformed Interlocutors on both measures, but the pattern is significant only for one measure. These results complement existing theorizing and findings regarding the potential benefit of participating in activities that train ToM and may also help explain the often-observed stronger ToM performance of those (e.g., women) who, at least in certain contexts, have typically been discouraged from participating actively in conversations.

This paper addresses methodological challenges in the growing research field of geographical psychology such as omitted variables, spatial autocorrelation, and causality. I provide tips and practical guidance for the spatial analysis of psychological data.