Weber Romann M, Fajen Brett R
California Institute of Technology, MC 228-77, Pasadena, CA, 91125, USA,
Psychon Bull Rev. 2015 Jun;22(3):653-72. doi: 10.3758/s13423-014-0732-0.
A major focus of research on visually guided action is the identification of control strategies that map optical information to actions. The traditional approach has been to test the behavioral predictions of a few hypothesized strategies against subject behavior in environments in which various manipulations of available information have been made. While important and compelling results have been achieved with these methods, they are potentially limited by small sets of hypotheses and the methods used to test them. In this study, we introduce a novel application of data-mining techniques in an analysis of experimental data that is able to both describe and model human behavior. This method permits the rapid testing of a wide range of possible control strategies using arbitrarily complex combinations of optical variables. Through the use of decision-tree techniques, subject data can be transformed into an easily interpretable, algorithmic form. This output can then be immediately incorporated into a working model of subject behavior. We tested the effectiveness of this method in identifying the optical information used by human subjects in a collision-avoidance task. Our results comport with published research on collision-avoidance control strategies while also providing additional insight not possible with traditional methods. Further, the modeling component of our method produces behavior that closely resembles that of the subjects upon whose data the models were based. Taken together, the findings demonstrate that data-mining techniques provide powerful new tools for analyzing human data and building models that can be applied to a wide range of perception-action tasks, even outside the visual-control setting we describe.
视觉引导行为研究的一个主要重点是识别将光学信息映射到行为的控制策略。传统方法是在对可用信息进行各种操作的环境中，针对少数假设策略的行为预测对受试者行为进行测试。虽然通过这些方法取得了重要且令人信服的结果，但它们可能受到少量假设及其测试方法的限制。在本研究中，我们介绍了数据挖掘技术在实验数据分析中的一种新应用，该应用能够描述和模拟人类行为。这种方法允许使用光学变量的任意复杂组合快速测试各种可能的控制策略。通过使用决策树技术，受试者数据可以转换为易于解释的算法形式。然后，该输出可以立即纳入受试者行为的工作模型。我们测试了这种方法在识别人类受试者在避撞任务中使用的光学信息方面的有效性。我们的结果与已发表的关于避撞控制策略的研究一致，同时还提供了传统方法无法获得的额外见解。此外，我们方法的建模部分产生的行为与基于其数据构建模型的受试者的行为非常相似。综上所述，这些发现表明，数据挖掘技术为分析人类数据和构建可应用于广泛感知 - 行动任务的模型提供了强大的新工具，甚至超出了我们所描述的视觉控制设置范围。
