Understanding stable patterns of interpersonal movement coordination is essential to understanding successful social interaction and activity (i.e., joint action). Previous research investigating such coordination has primarily focused on the synchronization of simple rhythmic movements (e.g., finger/forearm oscillations or pendulum swinging). Very few studies, however, have explored the stable patterns of coordination that emerge during task-directed complementary coordination tasks. Thus, the aim of the current study was to investigate and model the behavioral dynamics of a complementary collision-avoidance task. Participant pairs performed a repetitive targeting task in which they moved computer stimuli back and forth between sets of target locations without colliding into each other. The results revealed that pairs quickly converged onto a stable, asymmetric pattern of movement coordination that reflected differential control across participants, with 1 participant adopting a more straight-line movement trajectory between targets, and the other participant adopting a more elliptical trajectory between targets. This asymmetric movement pattern was also characterized by a phase lag between participants and was essential to task success. Coupling directionality analysis and dynamical modeling revealed that this dynamic regime was due to participant-specific differences in the coupling functions that defined the task-dynamics of participant pairs. Collectively, the current findings provide evidence that the dynamical coordination processes previously identified to underlie simple motor synchronization can also support more complex, goal-directed, joint action behavior, and can participate the spontaneous emergence of complementary joint action roles.
We examined patterns of self-evaluative information use in a sample of college women who were trying to lose weight (N = 306). Participants described their weight loss experiences and answered questions about their self-evaluative activity via an online survey. The analysis strategy examined the relative use of four types of selfevaluative information (objective, upward social comparison, lateral social comparison, and downward social comparison) to meet three basic self-evaluative motives (accurate self-assessment, self-enhancement, and self-improvement). We also examined the role that dissatisfaction, uncertainty, importance, and self-esteem played in the relative use of information and the relationship of these factors on weight loss success. Our findings support previous research showing the primacy of accurate and self-improvement motives in the domain of weight loss and the usefulness of lateral social comparison information for meeting all three motives. Women evaluating their weight reported using upward social comparison information most often, followed by objective information. Lateral and upward social comparison information were rated as more useful than downward social comparison information for meeting accuracy and self-improvement motives. Both lateral and downward social comparison information were reported as especially useful for self-enhancement, with upward social comparison information rated as least useful. Our study utilized an integrative approach for understanding selfevaluative processes in the area of college women's weight loss. We found general support for our hypotheses regarding well-documented patterns of social comparison information usefulness for meeting three self-evaluative motives. Our data also support earlier research arguing that it is important to view information use in the context of multiple self-evaluative motives.
Cincinnati, like other new migration areas, has recently experienced tremendous growth in the Latino immigrant population. Because greater health disparities exist for Latinos compared to both majority and other minority groups, it is essential to understand how migratory patterns and healthcare infrastructure are related. In this study, social network analysis (SNA), which quantitatively assesses and evaluates network formation and network relationships, was used to investigate the structure of the Greater Cincinnati Latino health network. Referral and collaboration networks were assessed for 29 individuals serving the Latino community. Results indicated the desired collaboration network was nearly twice as dense as either the physical or the mental health referral networks. The physical network was also denser than the mental health network. Similar results were found when analyzing network centralization. Taken together, results indicate a need for additional strategic partnerships between Latino‐serving providers and the Latino‐serving community. Specific recommendations are briefly discussed.
Using focus groups, we examined support and opposition for Donald Trump prior to the 2016 presidential election. When ingroup members participate in discussion, this conversation alone typically strengthens and intensifies members’ initial attitudes. We used a pre‐ to post‐focus‐group questionnaire to assess attitudes toward Trump, his campaign, and policies. We argue that group polarization influenced people’s opinions about Trump such that attitudes became more extreme after discussion with like‐minded individuals. We report changes for Trump nonsupporters for which group polarization occurred on attitudes toward illegal immigration, political correctness, the military, women, and veterans after the group discussion. For each, level of support for Trump’s views decreased. To further explore potential psychological mechanisms associated with group polarization, we employed network science methods to examine the structure of the language associated with these issues and identify potential drivers of attitude change. Results provide some support for a common mechanism for group polarization, which may be driven by language dynamics specific to individual attitudes.
The current article investigated and described differences in online, pro-recovery communities' linguistic themes extracted from online messaging. More specifically, the authors examined language alignment between academic journal abstracts (n = 9,744), Twitter influencers (n = 43,384), Twitter organizations (n = 52,748), and Reddit posts (n = 73,628) related to aspects of eating disorders (EDs). Natural language processing techniques (i.e., the meaning extraction method) along with a principal component analysis (PCA) were used to define themes and create novel Linguistic Inquiry and Word Count (LIWC) dictionaries specific to each platform and mental health more broadly. Using these dictionaries, along with others in LIWC previously associated with aspects of EDs, we evaluated the degree to which each platform's language reflected each dictionary. A multitude of findings arose using both common and novel LIWC dictionaries between scientific abstracts, Twitter influencers' tweets, Twitter organizations' tweets, and Reddit posts. These differences spanned language constructs, such as body, health, positive emotion, negative emotion, mental health, Twitter language, and Reddit language. These linguistic differences suggest that each platform has its own purpose and function, such that they may be considered as smaller, specialized communities within the larger pro-recovery context. Although each platform functions under different constraints, leaders and community members should project messaging that promotes recovery at the broadest level. Community leaders should take advantage of preexisting frameworks to connect mental health professionals and individuals in the recovery process. Public Policy Relevance StatementThis article reports analyses of the language used across the online pro-recovery eating disorder communities. Language from several different sources (academic journal abstracts, Twitter influencers, Twitter organizations, Reddit) differed on themes, language indicators relevant to ED recovery (i.e., body, positive emotion, negative emotion), and words related to mental health more broadly. These findings support the notion that each source serves its own function in a smaller specialized community within the larger pro-recovery community.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.