Collaborative Research: Perception, Behavior and Learning in the Museum

合作研究：博物馆中的感知、行为和学习

• 批准号: 2218024
• 负责人: Victoria Behner
• 金额: $ 36.92万
• 依托单位: MUSEUM ASSOCIATES
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218024&HistoricalAwards=false
• 关键词: Collaborative; Research; Perception; Behavior; Learning

In addition to their traditional role as a source of emotional rewards and inspiration, the modern mission of art museums is education. Objects of interest are presented in the context of a narrative that the museum visitor follows to learn history, materials, technique, and function, as well as relationships to the natural world and human civilization. Narrative principles for exhibition design have for decades emerged from small scale observational studies of museum visitor behaviors, such as expressions of engagement and choice of path. Building on recent advances in scientific understanding of sensory processing and behavioral choice, in combination with sophisticated computational tools for characterization of fine details of behavior, a team of scientists and museum professionals will turn a designated gallery at the Los Angeles County Museum of Art into a laboratory for investigation of human perception, action, choice, and learning. The broad applied goal of this project is to obtain scientific knowledge that will further enhance the educational mission of museums. The intellectual impact of the project will be an improved understanding of environmental and social factors that guide human behavior under naturalistic conditions. More generally, the project will benefit the larger communities of architecture and design professionals, as a model of experimental methodology and by offering a unique multifaceted dataset for analysis and evaluation of the influence of the built environment.The project builds upon several technological and computational innovations. One is the methods of computational ethology, which is a new approach to quantitative behavioral analysis that employs high-resolution 3D motion capture together with machine learning methods for behavioral classification. This approach will yield efficient non-invasive measurements of visitor locations, rates of movement, poses, social interactions, gestures and expressions that reflect transitory cognitive states, such as visual attention and engagement with works of art. Observations from tens of thousands of anonymous museum visitors will be subjected to descriptive statistical analyses, to gain insights into the relationship between the structure and content of exhibition design and the behavior of individuals and social groups, and to discover spatial and temporal contingencies between visitor behaviors at different locations in the gallery. Results of these descriptive analyses will be used to develop predictive models of visitor behavior, capturing the full gamut of individual styles of visitor interaction with works of art and other visitors, informed by visitors’ sensory operating characteristics as well as sensory and motoric affordances of the gallery space. In the final stage of the project, strategic modifications to gallery design will be used to test and further develop predictive models in forecasting visitor behaviors. Results will constitute a new empirical framework for exhibition design and its impact on visitor experience.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.

除了作为情感奖励和灵感来源的传统角色外，艺术博物馆的现代使命是教育。感兴趣的对象是在博物馆参观者遵循的叙事背景下呈现的，以了解历史，材料，技术和功能，以及与自然世界和人类文明的关系。几十年来，展览设计的叙事原则一直来自于对博物馆参观者行为的小规模观察研究，例如参与和路径选择的表达。基于对感官处理和行为选择的科学理解的最新进展，结合用于表征行为细节的复杂计算工具，一个由科学家和博物馆专业人员组成的团队将把洛杉矶县艺术博物馆的一个指定画廊变成一个实验室，用于研究人类的感知、行动、选择和学习。该项目的广泛应用目标是获得科学知识，进一步加强博物馆的教育使命。该项目的智力影响将是对自然条件下指导人类行为的环境和社会因素的更好理解。更广泛地说，该项目将使更大的建筑和设计专业人士社区受益，作为实验方法的典范，并通过提供一个独特的多方面数据集来分析和评估建筑环境的影响。该项目建立在几项技术和计算创新的基础上。一种是计算行为学方法，这是一种定量行为分析的新方法，它采用高分辨率3D运动捕捉和机器学习方法进行行为分类。这种方法将产生有效的非侵入性测量游客的位置，运动速度，姿势，社会互动，手势和表达，反映短暂的认知状态，如视觉注意力和参与艺术作品。从成千上万的匿名博物馆游客的观察将受到描述性统计分析，深入了解展览设计的结构和内容与个人和社会群体行为之间的关系，并发现画廊中不同位置的游客行为之间的空间和时间偶然性。这些描述性分析的结果将用于开发游客行为的预测模型，捕捉游客与艺术品和其他游客互动的个人风格的全部范围，由游客的感官操作特征以及画廊空间的感官和运动功能提供信息。在项目的最后阶段，对画廊设计的战略性修改将用于测试和进一步开发预测游客行为的预测模型。该奖项反映了NSF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。