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

这一行动为NSF 2022财年生物学博士后研究奖学金提供了资金，综合研究调查了基因组、环境和表型之间相互作用的生命规则。该奖学金支持研究员的研究和培训，这些研究员将以创新的方式为生活规则领域做出贡献。通过将视频跟踪与分子方法相结合，这位研究员将评估社交优势的行为和遗传驱动因素。具体地说，这位研究员将测试过去的社会互动对个人在未来的统治竞争中决策的影响，以及这些个人行为如何推动群体等级的形成。这些实验将为分析和预测社会行为建立一个新的框架，增加我们对是什么决定动物物种间社会网络结构的理解。该奖学金还将支持对高中生和本科生的辅导，帮助他们建立参与科学研究和计算分析的机会。在社交比赛中，先前的胜利往往通过赢家效应增加未来获胜的可能性，而赢家效应是通过在随后的比赛中个人攻击性的转变来调节的。贝叶斯更新提供了一个很好的框架来对驱动这些行为变化的内部过程进行建模。在获胜后观察到的攻击性增加表明，动物们正在更新自己对未来比赛获胜概率的预期。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人的短期影响，到较大群体之间长期重复互动的结果。这位研究员将通过两个测试模型预测的实验来测试赢家效应是否与贝叶斯更新一致：首先，通过在受控结果下重复举行比赛，研究员将测试意外事件(例如战胜较大的鱼)是否会导致基因表达和行为的对比比预期结果之后的变化更大；其次，通过在受控社会环境中执行长期行为跟踪，研究员将测试赢家效应的稳定性是否依赖于获得新信息。这项工作将利用自然克隆物种Amazon Molly(Poecilia Formosa)进行，限制生物复制中的个体差异，并为测试社会支配机制提供更大的统计能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.