A prevailing assumption in many behavioral studies is the underlying normal distribution of the data under investigation. In this regard, although it appears plausible to presume a certain degree of similarity among individuals, this presumption does not necessarily warrant such simplifying assumptions as average or normally distributed human behavioral responses. In the present study, we examine the extent of such assumptions by considering the case of human–human touch interaction in which individuals signal their face area pre-touch distance boundaries. We then use these pre-touch distances along with their respective azimuth and elevation angles around the face area and perform three types of regression-based analyses to estimate a generalized facial pre-touch distance boundary. First, we use a Gaussian processes regression to evaluate whether assumption of normal distribution in participants’ reactions warrants a reliable estimate of this boundary. Second, we apply a support vector regression (SVR) to determine whether estimating this space by minimizing the orthogonal distance between participants’ pre-touch data and its corresponding pre-touch boundary can yield a better result. Third, we use ordinary regression to validate the utility of a non-parametric regressor with a simple regularization criterion in estimating such a pre-touch space. In addition, we compare these models with the scenarios in which a fixed boundary distance (i.e., a spherical boundary) is adopted. We show that within the context of facial pre-touch interaction, normal distribution does not capture the variability that is exhibited by human subjects during such non-verbal interaction. We also provide evidence that such interactions can be more adequately estimated by considering the individuals’ variable behavior and preferences through such estimation strategies as ordinary regression that solely relies on the distribution of their observed behavior which may not necessarily follow a parametric distribution.

中文翻译：

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对非语言人类行为分析的参数方法的批判性检查：面部触摸前交互的案例研究

许多行为研究中的普遍假设是所调查数据的基本正态分布。在这方面，虽然假设个体之间存在一定程度的相似性似乎是合理的，但这种假设并不一定保证诸如平均或正态分布的人类行为反应这样的简化假设。在本研究中，我们通过考虑人与人触摸交互的情况来检查这些假设的程度，在这种情况下，个人会在触摸前发出他们的面部区域距离边界信号。然后，我们使用这些触摸前距离以及它们各自在面部周围的方位角和仰角，并执行三种基于回归的分析来估计广义的面部触摸前距离边界。第一的，我们使用高斯过程回归来评估参与者反应中的正态分布假设是否保证了对该边界的可靠估计。其次，我们应用支持向量回归 (SVR) 来确定通过最小化参与者的触摸前数据与其相应的触摸前边界之间的正交距离来估计此空间是否可以产生更好的结果。第三，我们使用普通回归来验证具有简单正则化标准的非参数回归器在估计这种触摸前空间时的效用。此外，我们将这些模型与采用固定边界距离（即球面边界）的场景进行了比较。我们表明，在面部触摸前交互的背景下，正态分布没有捕捉到人类受试者在这种非语言互动中表现出的可变性。我们还提供证据表明，通过考虑个人的可变行为和偏好，通过诸如普通回归之类的估计策略，可以更充分地估计此类交互，该策略仅依赖于他们观察到的行为的分布，而这些行为不一定遵循参数分布。