An Automated Technology-Based Personality Classifier

个体以稳定的方式彼此不同，这对他们的繁荣，健康和福利有重要影响。人格特质可以预测经济，社会和健康领域的许多结果，例如工作表现，关系质量和生病的可能性。尽管人格的重要性已经得到证实，但评估人格的主要技术--“自我报告问卷”--几乎没有随着时间的推移而改变。这些自我报告受到一系列限制，例如破坏性，耗时，易受记忆偏差的影响;这些限制可能会破坏人格问卷的有效性。人格评估领域的进展一直受到这样一个事实的限制，即通过日常行为和语言表达人格一直具有挑战性，难以在日常生活的自然流中直接测量。少数收集了客观行为测量的研究通常是在非常有限的行为范围内或在实验室的人工范围内进行的。然而，智能手机的出现及其在现代生活中的普遍存在为人格评估领域的革命提供了希望。目前的研究将使用智能手机，它将自动和不引人注目地测量个性，因为它是在日常生活中表达。该应用程序将使用嵌入式传感器（例如，加速度计、光传感器、麦克风、GPS）来收集行为（例如，活动类型、睡眠模式、社交性、位置）数据。该研究将从多达2000名参与者中收集数据，这些数据是广泛的（多种行为），细粒度的（每小时许多评估），纵向的（许多周的连续行为数据）和上下文标记。这些数据将有两个主要用途。首先，它们将提供关于行为如何在日常生活中展开的大规模客观记录，使研究人员能够了解什么样的行为倾向于在每个生命中共同发生，以及它们遵循什么样的时间模式。其次，这些数据将用于生成一种不引人注目的自动化方法，用于在日常生活中通过智能手机测量个性。使用这些数据，主要研究者将确定标准的五因素人格模型是否足以捕捉现实世界行为的结构，或者是否需要一个新的自下而上的，经验得出的人格结构修正。从方法论上讲，该项目将产生软件、分析工具和分类器，使研究人员能够摆脱对自我报告和人为限制的基于实验室的真实世界行为代理的依赖。 这项研究将推进和验证用于推断复杂行为的传感技术（例如，研究人员将能够从传感器数据的连续流中提取信息（例如，情境、对话贡献），并将由此产生的软件和分析工具提供给研究人员使用。

Individuals differ from each other in stable ways that have important implications for their prosperity, health, and welfare. Personality traits predict numerous consequential outcomes in the economic, social, and health domains, such as work performance, relationship quality, and the likelihood of getting sick. Despite personality's demonstrated importance, the predominant technology for assessing personality-- "self-report questionnaires"--has remained virtually unchanged over time. These self-reports are subject to an array of limitations, such as being disruptive, time consuming, and vulnerable to memory biases; these limitations potentially undermine the validity of personality questionnaires. Progress in the field of personality assessment has been constrained by the fact that the everyday behaviors and language through which personality is expressed have been challenging to measure directly in the natural stream of daily life. The few studies that have collected objective measures of behavior have typically done so on a very limited range of behaviors or within the artificial confines of a laboratory. However, the advent of smartphones and their ubiquity in modern life offer the promise of revolutionizing the field of personality assessment. The present research will use a smartphone that will automatically and unobtrusively measure personality as it is expressed in daily life. The app will use embedded sensors (e.g., accelerometer, light sensor, microphone, GPS) to gather behavioral (e.g., activity type, sleep patterns, sociability, location) data from participants. The research will collect data from up to 2000 participants that is broad-based (many kinds of behavior), fine-grained (many assessments per hour), longitudinal (many weeks of continuous behavioral data), and context tagged. These data will have two primary uses. First, they will provide large-scale objective records of how behavior unfolds in the context of everyday life, allowing researchers to learn what kinds of behavior tend to co-occur in every life and what kinds of temporal patterns they follow. Second, the data will be used to generate an unobtrusive automated method for measuring personality via smartphones in everyday lives. Using such data, the principal investigator will determine whether the standard five-factor personality model adequately captures the structure of real-world behaviors or whether a new bottom-up, empirically derived revision of personality structure is needed. Methodologically, the project will yield software, analysis tools, and classifiers that allow researchers to move beyond their reliance on self-reports and artificially constrained lab-based proxies of real-world behavior. The study will advance and validate sensing techniques used to infer complex behaviors (e.g., situations, conversation contribution) from continuous steams of sensor data, and the resulting software and analyses tools will be made available for researchers to use.