Brain Electrophysiology Attention Movement Laboratory, Institute of Psychology, University of Lausanne, Geopolis Quartier Mouline, CH-1015, Lausanne, Switzerland.
Behav Res Methods. 2024 Aug;56(5):4643-4660. doi: 10.3758/s13428-023-02208-z. Epub 2023 Aug 24.
Relying on existing literature to identify suitable techniques for characterizing individual differences presents practical and methodological challenges. These challenges include the frequent absence of detailed descriptions of raw data, which hinders the assessment of analysis appropriateness, as well as the exclusion of data points deemed outliers, or the reliance on comparing only extreme groups by categorizing continuous variables into upper and lower quartiles. Despite the availability of algorithmic modeling in standard statistical software, investigations into individual differences predominantly focus on factor analysis and parametric tests. To address these limitations, this application-oriented study proposes a comprehensive approach that leverages behavioral responses through the use of signal detection theory and clustering techniques. Unlike conventional methods, signal detection theory considers both sensitivity and bias, offering insights into the intricate interplay between perceptual ability and decision-making processes. On the other hand, clustering techniques enable the identification and classification of distinct patterns within the dataset, allowing for the detection of singular behaviors that form the foundation of individual differences. In a broader framework, these combined approaches prove particularly advantageous when analyzing large and heterogeneous datasets provided by data archive platforms. By applying these techniques more widely, our understanding of the cognitive and behavioral processes underlying learning can be expedited and enhanced.
依靠现有文献来确定适合的技术来描述个体差异存在实际和方法学上的挑战。这些挑战包括频繁缺乏原始数据的详细描述，这阻碍了对分析适当性的评估，以及排除被认为是异常值的数据点，或者通过将连续变量划分为四分位数来仅比较极端组。尽管标准统计软件中提供了算法建模，但个体差异的研究主要集中在因子分析和参数检验上。为了解决这些限制，本面向应用的研究提出了一种综合方法，通过使用信号检测理论和聚类技术来利用行为反应。与传统方法不同，信号检测理论同时考虑了敏感性和偏差，深入了解感知能力和决策过程之间的复杂相互作用。另一方面，聚类技术能够识别和分类数据集中的不同模式，从而检测形成个体差异基础的单一行为。在更广泛的框架中，当分析数据档案平台提供的大型和异构数据集时，这些组合方法被证明特别有利。通过更广泛地应用这些技术，可以加速和增强我们对学习背后的认知和行为过程的理解。
