NSF PRFB 2022: A Bayesian framework for hierarchy: Do past experiences shape self-assessment of fighting ability?

该行动资助了美国国家科学基金会2022财年生物学博士后研究奖学金，研究基因组，环境和表型之间相互作用的生命规则的综合研究。该奖学金支持将以创新方式对生活规则领域作出贡献的研究员的研究和培训。通过将视频跟踪与分子方法相结合，研究员将评估社会支配的行为和遗传驱动因素。具体来说，研究员将测试过去的社会互动对个体在未来优势竞争中的决策的影响，以及这些个体行为如何推动群体等级制度的形成。这些实验将为分析和预测社会行为建立一个新的框架，增加我们对决定动物物种社会网络结构的因素的理解。该研究金还将支持对高中和本科学生的指导，为参与科学研究和计算分析创造机会。在社会竞争中，先前的胜利往往通过赢家效应增加未来胜利的可能性，在随后的竞争中，个体攻击性的转变起到了中介作用。贝叶斯更新提供了一个很好的框架来模拟驱动这些行为变化的内部过程。观察到的获胜后攻击性的增加表明，动物正在更新自己对未来比赛中获胜概率的预期。贝叶斯模型允许我们在多个尺度上预测变化：从单个竞赛事件对个体的短期影响到更大群体之间长期、反复互动的结果。该研究员将通过执行两个实验来测试模型的预测，从而测试赢家效应是否与贝叶斯更新相一致：首先，通过重复进行控制结果的竞赛，该研究员将测试意外事件（例如，战胜一条大鱼）是否会比预期结果后的变化在基因表达和行为上产生更大的差异；其次，通过在受控的社会环境中进行长期的行为跟踪，研究人员将测试赢家效应的稳定性是否取决于获得新信息的途径。这项工作将使用亚马逊茉莉（Poecilia formosa）进行，这是一种自然克隆物种，限制了生物复制中的个体差异，并为测试社会优势机制提供了更多的统计能力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2022, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the fellow that will contribute to the area of Rules of Life in innovative ways. By combining video tracking with molecular approaches, the fellow will assess the behavioral and genetic drivers of social dominance. Specifically, the fellow will test the effect of past social interactions on an individual’s decisions in future dominance contests, and how these individual behaviors drive the formation of group hierarchy. These experiments will establish a new framework for analyzing and predicting social behavior, increasing our understanding of what determines the structure of social networks across animal species. The fellowship will also support the mentoring of students at both the high school and undergraduate level to establish opportunities for participation in science research and computational analysis.During social contests, prior winning often increases the likelihood of future victories via the winner-effect, mediated by a shift in individual aggression in subsequent contests. Bayesian updating provides an excellent framework to model the internal processes which drive these changes in behavior. The observed increase in aggression following a win suggests that animals are updating their own expectations of the probability of victory in future contests. Bayesian models allow us to predict changes at multiple scales: from the short-term effect of single contest events on an individual to the outcome of long-term, repeated interactions among larger groups. The fellow will test whether the winner-effect is consistent with Bayesian updating, by performing two experiments that test the model’s predictions: First, by staging repeated contests with controlled outcomes, the fellow will test whether unexpected events (e.g. a win against a larger fish) drive a greater contrast in gene expression and behavior than changes following expected outcomes; second, by performing long-term behavioral tracking in controlled social environments, the fellow will test whether the stability of the winner-effect is dependent on access to new information. This work will be conducted using the Amazon molly (Poecilia formosa), a naturally clonal species, limiting individual variation in biological replicates and providing increased statistical power for testing the mechanisms of social dominance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.