How do people hear laughter in expressive, unprompted speech? What is the range of expressivity and function of laughter in this speech, and how can laughter inform the recognition of higher-level expressive dimensions in a corpus? This paper presents a scalable method for collecting natural human description of laughter, transforming the description to a vector of quantifiable laughter dimensions, and deriving baseline classifiers for the different dimensions of expressive laughter. Then, it explores the impact of leveraging nuances of laughter in the recognition of higher-level, general expressive dimensions, discovered in the same way, such as genuine happiness, sarcasm, nervous reflection, and more. The performance of the low-level laughter classifiers is presented, along with the performance of the high-level laughter-aware and laughter-unaware classifiers.