Models of sexuality have evolved substantially in the past several decades through the inclusion of new aspects which were previously overlooked. Components such as romantic attraction and behavior have also traditionally been included in models of sexuality. However, romantic and sexual orientations do not coincide for all individuals. A population for which this is true and one that has developed a robust language for discussing romantic orientation is the asexual community. The current study aimed to explore romantic and sexual orientation through patterns found within the factors of attraction, behavior, and identity in the asexual community. The current sample composed of individuals who identified as asexual (N = 306, M_age = 27.1) was 61% female, 13% non-binary, and 10% self-described or used multiple labels. Within this sample, aspects of sexual and romantic orientations and experiences were measured, including fluidity, the quantity and type of self-identified labels, desire for romance or sex, and the role of contextual influences on these experiences. These aspects were used as the primary characteristics to construct participant profiles, both complete profiles and factor specific (attraction, behavior, identity). t-distributed stochastic neighbor embedding (tSNE) was used to find patterns of similarity between individual participant profiles. Overall, it appeared that attraction was the factor most closely associated with overall experiences; however, substantial variability existed between participants. These findings provide a mechanism for better understanding of some nuances of romantic and sexual orientation and may be a useful first step toward future inquiry and hypothesis generation.

在过去的几十年里，通过纳入以前被忽视的新方面，性行为模式发生了重大变化。浪漫的吸引力和行为等组成部分传统上也包含在性模型中。然而，并非所有人的浪漫取向和性取向都一致。无性恋社区是这样一种人群，并且已经发展出一种强有力的语言来讨论浪漫取向。目前的研究旨在通过在无性恋社区的吸引力、行为和身份因素中发现的模式来探索浪漫和性取向。当前样本由确定为无性恋的个体组成（N  = 306，M_年龄 = 27.1) 是 61% 的女性，13% 是非二元性别，10% 自我描述或使用多个标签。在这个样本中，我们测量了性和浪漫取向和经历的各个方面，包括流动性、自我认同标签的数量和类型、对浪漫或性的渴望，以及情境对这些经历的影响。这些方面被用作构建参与者档案的主要特征，包括完整档案和特定因素（吸引力、行为、身份）。t 分布随机邻域嵌入 (tSNE) 用于查找各个参与者配置文件之间的相似性模式。总的来说，吸引力似乎是与整体体验最密切相关的因素；然而，参与者之间存在很大差异。